AWS Partner Network (APN) Blog

Say Hello to 158 New AWS Competency Partners Added in April

Nick Paris — Wed, 06 May 2026 17:38:26 +0000

By Nick Paris, APN Marketing Manager – AWS Partner Network

The AWS Partner Network (APN) is a global community that leverages Amazon Web Services (AWS) technologies, programs, expertise, and tools to build solutions and provide services for customers.

The APN has more than 130,000 partners from over 200 countries, with 70% headquartered outside of the United States. Together, partners and AWS provide innovative solutions, solve technical challenges, win deals, and deliver greater customer value.

To achieve APN specializations such as AWS Competency, AWS Service Delivery, AWS Service Ready, and AWS Managed Service Provider (MSP), organizations must undergo rigorous technical validation and assessment of their AWS solutions and practices.

NEW! AWS Competency Partners

To succeed with cloud adoption in today’s complex IT environment and continue to advance in the future, customers can team up with an AWS Competency Partner.

The AWS Competency Program validates and promotes AWS Partners with demonstrated technical expertise and proven customer success in specialized areas across industries, use cases, and workloads. Guidance from these skilled professionals can lead to better business and bigger results.

Team up with our newest AWS Competency Partners:

AWS AI Competency

a2c Cloud | NAMER | Agentic AI Consulting Services
A3Data | LATAM | Agentic AI Consulting Services
Actwill | China | Generative AI Consulting Services
Apex Lab | EMEA | Agentic AI Consulting Services; Generative AI Consulting Services
Applogika | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
Awiant AB | EMEA | Agentic AI Consulting Services
axcess.io | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
Axiom Digital | EMEA | Agentic AI Consulting Services; Generative AI Consulting Services
binbash | NAMER | Generative AI Consulting Services
Cirrusgo | EMEA | Agentic AI Consulting Services
CloudifyOps | APAC | Agentic AI Consulting Services; Generative AI Consulting Services
CloudNation | EMEA | Agentic AI Consulting Services
CloudPlexo | EMEA | Agentic AI Consulting Services
CloudThinker | APAC | Agentic AI Consulting Services
CLOUDWICK TECHNOLOGIES | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
CloudZenia | APAC | Generative AI Consulting Services
Codex | APAC | Agentic AI Consulting Services
Criptonube – Myappsoftware | NAMER | Generative AI Consulting Services
Cybage | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
develeap | EMEA | Generative AI Consulting Services
DNX Solutions | APAC | Generative AI Consulting Services
DreamSquad Tecnologia | LATAM | Generative AI Consulting Services
E&M Computing | EMEA | Generative AI Consulting Services
Elad Software Systems | EMEA | Agentic AI Consulting Services
ETECH SYSTEM | APAC | Generative AI Consulting Services
gdna | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
Globant | LATAM | Generative AI Consulting Services
HiveTek Corporation | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
Ibexlabs / Labra | NAMER | Generative AI Consulting Services
INADEV Corporation | NAMER | Generative AI Consulting Services
Insbuilt | LATAM | Agentic AI Consulting Services
IT Visionary | EMEA | Generative AI Consulting Services
Marlocks Technologies | EMEA | Agentic AI Consulting Services; Generative AI Consulting Services
Metal Toad | NAMER | Agentic AI Consulting Services
MethodData | NAMER | Agentic AI Consulting Services
Nerdshub | EMEA | Agentic AI Consulting Services; Generative AI Consulting Services
Nubiral | LATAM | Agentic AI Consulting Services
Opsfleet | EMEA | Generative AI Consulting Services
Opsfleet | EMEA | Agentic AI Consulting Services
Pentagon System and Services | APAC | Agentic AI Consulting Services; Generative AI Consulting Services
ProfiSea | EMEA | Agentic AI Consulting Services; Generative AI Consulting Services
Qucoon | EMEA | Agentic AI Consulting Services
revolgy | EMEA | Agentic AI Consulting Services
Structurit Consulting | EMEA | Agentic AI Consulting Services
superluminar | EMEA | Generative AI Consulting Services
Switch Software | NAMER | Generative AI Consulting Services
Teleglobal International | APAC | Agentic AI Consulting Services; Generative AI Consulting Services
Thaumaturgix, Inc. (Tgix) | NAMER | Generative AI Consulting Services
YASH Technologies | NAMER | Agentic AI Consulting Services
上海邑炎信息科技有限公司 | China | Generative AI Applications
北京尽微致广信息技术有限公司 | China | Generative AI Applications
Agora | | Generative AI Applications
Backlight | NAMER | Generative AI Applications
biia Group | LATAM | Agentic AI Applications; Generative AI Applications
Dovetail | APAC | Generative AI Applications
Exaforce | NAMER | Agentic AI Applications; Generative AI Applications
Grafana Labs | NAMER | Foundation Models and App Development
Kong | NAMER | Infrastructure and Data
monday.com | NAMER | Generative AI Applications
Ona | EMEA | Generative AI Applications
Pixel ML | NAMER | Agentic AI Applications
Plerion | APAC | Agentic AI Tools; Generative AI Applications
Proximie | EMEA | Foundation Models and App Development; Generative AI Applications
Surecomp Development | EMEA | Generative AI Applications
Temporal Technologies | NAMER | Agentic AI Tools
ThinkAnalytics | EMEA | Agentic AI Applications; Generative AI Applications
vFunction | NAMER | Agentic AI Applications

AWS Cloud Operations Competency

DBiz Solutions | APAC | Cloud Governance
EETECH | EMEA | Cloud Governance
Engineering Ingegneria Informatica | EMEA | Operations Management
Epitechnic | EMEA | Cloud Governance
Epsilon | EMEA | Operations Management
Marlocks Technologies | EMEA | Cloud Governance
Mist Avinya Technologies | APAC | Cloud Financial Management
Morris & Opazo | LATAM | Cloud Governance
Several Clouds | EMEA | Cloud Financial Management
UsefulBI Corporation | NAMER | Cloud Financial Management
ZainTech Solution | EMEA | Cloud Governance
Cloudgov | NAMER | Cloud Financial Management; Cloud Governance; Monitoring & Observability; Operations Management

Consumer Goods Competency

Bynder | NAMER | Marketing
Claroty | NAMER | IT & Digital Transformation

AWS Data & Analytics Competency

DataArt | NAMER | Consulting Services
DBiz Solutions | APAC | Consulting Services
Intuitive.Ai | NAMER | Consulting Services
TrackIt | NAMER | Consulting Services

AWS DevOps Competency

10Pearls | NAMER | Consulting Services
Applogika | NAMER | Consulting Services
Appvia | EMEA | Consulting Services
Compass UOL | NAMER | Consulting Services
GoCloud | EMEA | Consulting Services

AWS DevOps Competency

OpsTeam | LATAM | Consulting Services

AWS Digital Sovereignty Competency

Cirrusgo | EMEA | Consulting Services
CloudSoftway | EMEA | Consulting Services
tecRacer | EMEA | Consulting Services
Woodmark Consulting | EMEA | Consulting Services

AWS Education Competency

i2k2 Networks | APAC | Consulting Services

Energy & Utilities Competency

Hypercube Consulting | EMEA | Consulting Services
ZetaMinusOne | LATAM | Consulting Services

AWS Financial Competency

TTEC Digital | NAMER | Banking; General Financial Services; Insurance
Constantinople Operating Company | APAC | Banking
Group-IB Global | EMEA | Banking
Honeycomb | NAMER | Payments
MUREX | EMEA | Capital Markets
Prove | NAMER | Banking; Capital Markets; General Financial Services; Insurance; Payments
Guidehouse | NAMER | Citizen Services
Coder Technologies | NAMER | Defense & National Security

AWS Healthcare Competency

ZiMetrics | APAC | Consulting Services
Avaamo | NAMER | Health Administration

AWS Life Sciences Competency

NDS Corporation | APAC | Consulting Services

AWS Mainframe Modernization Competency

GFT Technologies SE | NAMER | Mainframe Workload

AWS Manufacturing and Industrial Competency

FPT Software | APAC | Enterprise Solutions; Operational Technology; Smart Manufacturing; Smart Products and Services; Supply Chain Management
Mactores Cognition | NAMER | Engineering and Design; Enterprise Solutions; Operational Technology; Operational Technology Security; Smart Manufacturing; Smart Products and Services; Supply Chain Management; Sustainability

AWS Media & Entertainment Competency

EngineLab | EMEA | Content Production
FINCONS GROUP AG | EMEA | Direct to Consumer; Media Supply Chain and Archive
WORKMATES CORE2CLOUD SOLUTION | APAC | Direct to Consumer

AWS Migration and Modernization Competency

Coder Technologies | NAMER | Infrastructure Automation
Alithya | NAMER | Migration Services
CBTS Technology Solutions | NAMER | Migration Services
fourTheorem | EMEA | Migration Services

AWS Migration and Modernization Competency

Sublimation Health | NAMER | Migration Services
Synechron | NAMER | Migration Services
Xpertech Solutions | NAMER | Migration Services

AWS Networking Competency

Advanced Wireless Network (AWN) | APAC | Consulting Services

AWS Nonprofit Competency

BeCloud | NAMER | Consulting Services

AWS Resilience Competency

Welastic | EMEA | Resilience Design

AWS Security Competency

Bytes | EMEA | Identity and Access Management; Infrastructure Protection; Threat Detection and Response
CloudSoftway | EMEA | Infrastructure Protection
Rapyder Cloud Solution | APAC | AI Security
autobotAI | NAMER | Threat Detection and Response
Cato Networks | EMEA | Infrastructure Protection; Threat Detection and Response
Exaforce | NAMER | Identity and Access Management; Threat Detection and Response
Wallarm | NAMER | Application Security

AWS Small and Medium Business (SMB) Competency

Arthurite Integrated Solutions | EMEA | Small and Medium Business
Chengdu Yiding Cloud Technology Co. | China | Small and Medium Business
Devops TechLab | APAC | Small and Medium Business
Elevata | NAMER | Small and Medium Business
Jump Cube | NAMER | Small and Medium Business
SkyGRID Global Technologies | EMEA | Small and Medium Business
Valere | NAMER | Small and Medium Business
Vcloudmaster | EMEA | Small and Medium Business
Cognetiks Consulting | EMEA | Small and Medium Business
Krikey AI | NAMER | Small and Medium Business
Zendesk | NAMER | Small and Medium Business

AWS Migration and Modernization Competency

Xpertech Solutions | NAMER | Migration Services

AWS Networking Competency

Advanced Wireless Network (AWN) | APAC | Consulting Services

AWS Nonprofit Competency

BeCloud | NAMER | Consulting Services

AWS Resilience Competency

Welastic | EMEA | Resilience Design

AWS Security Competency

Bytes | EMEA | Identity and Access Management; Infrastructure Protection; Threat Detection and Response
CloudSoftway | EMEA | Infrastructure Protection
Rapyder Cloud Solution | APAC | AI Security
autobotAI | NAMER | Threat Detection and Response
Cato Networks | EMEA | Infrastructure Protection; Threat Detection and Response
Exaforce | NAMER | Identity and Access Management; Threat Detection and Response
Wallarm | NAMER | Application Security

AWS Small and Medium Business (SMB) Competency

Arthurite Integrated Solutions | EMEA | Small and Medium Business
Chengdu Yiding Cloud Technology Co. | China | Small and Medium Business
Devops TechLab | APAC | Small and Medium Business
Elevata | NAMER | Small and Medium Business

More Value, Greater Profitability for AWS Partners

Our mission is to make the AWS Partner Network (APN) with AWS Marketplace your preferred route to market and empower partners to deliver differentiated value to millions of AWS customers, helping you to win more, win bigger, and win faster.

We’re aligning the AWS Partner experience—including programs, incentives, and enablement—around AWS services and the partner business models: business outcome solutions, technology solutions, managed services, services, and resell.

These enhancements provide you with more relevant and prescriptive guidance, where you need it, and in a consistent and predictable manner. Your profitability, along with helping you drive new business, is our number one goal.

In 2026, we’ll continue to ensure that AWS Marketplace and our AWS Partner programs provide demonstrated pathways to success—helping you drive greater customer value and profitability. There’s an exciting journey ahead, and we’re thrilled to be on it together!

Learn how AWS is transforming the partner experience >>

Simplify multi-account log ingestion to Splunk

Jan Klotter — Mon, 04 May 2026 15:11:48 +0000

By: Jan Klotter, Solutions Architect – AWS
By: Ranjit Kalidasan, Principal Solutions Architect – AWS
By: Paul Frederiksen, Global AWS Partner Technical Leader – Splunk

Splunk

Managing logs across multiple AWS accounts and forwarding them to Splunk has traditionally required high-maintenance, custom-built aggregation pipelines. With Amazon CloudWatch Logs centralization, you can consolidate log data from across your organization into a single account and then forward it to Splunk through one integration point.

This post describes how CloudWatch Logs centralization works, how to architect your Splunk integration around it, and what cost and operational benefits you can expect.

How CloudWatch Logs centralization works

CloudWatch Logs centralization replicates log data from multiple accounts and AWS Regions into a central account. It integrates with AWS Organizations, so you can define rules that automatically scope centralization to your entire organization, specific organizational units, or individual accounts.

Key capabilities include:

Selective log group copying based on your requirements
Automatic merging of same-named log groups in the destination account
Source context enrichment through @aws.account and @aws.region system fields
Optional backup Region setup for resilience
Encryption control for log groups copied from source accounts, including data originally encrypted with customer-managed AWS Key Management Server (AWS KMS) keys

The CloudWatch Log centralization feature is available in 17 AWS Regions at the time this post was published.

What this means for your Splunk integration

The following section describes the architectural design pattern, which will benefit from the architecture pattern with CloudWatch Logs centralization.

Simplify custom solutions

Previously, organizations had to build complex custom-built aggregation solutions to consolidate logs from multiple accounts before forwarding them to Splunk. Previous built solutions can be found here. For example, the traditional approach required creating CloudWatch Logs destinations and AWS Identity and Access Management (IAM) roles in the central account, configuring access policies to allow sender accounts to write log data, and setting up separate subscription filters in each source account—all forwarding through multiple Amazon Data Firehose streams or Amazon Kinesis Data Streams. CloudWatch Logs centralization helps by removing the need for per-account pipeline setup by using centralization rules that automatically replicate log data from source accounts into the destination account, including automatic onboarding of new accounts and log groups based on your organizational scope.

CloudWatch Logs can be forwarded to other destinations using CloudWatch subscription filters, enabling integration with third-party tools like Splunk. The centralization feature works in conjunction with subscription filters to provide a near real-time feed of log events that can be delivered to services such as Kinesis Data Streams, Data Firehose streams, or AWS Lambda for custom processing and forwarding to Splunk.

Recommended architecture for Splunk

The recommended architecture for Splunk includes consolidating the CloudWatch logs by Splunk sources. For example, if you plan to consolidate Amazon Virtual Private Cloud (Amazon VPC) Flow Logs and AWS WAF logs across multiple accounts, consolidate VPC Flow Logs into one log group and AWS WAF logs into another log group in the destination account. This grouping of logs of same data types helps to stream the logs with a Data Firehose subscription filter with the CloudWatch Logs decompression feature along with message extraction, without requiring any additional data transformation using Lambda.

Figure 1: Example architecture for Splunk ingestion

TCO considerations

CloudWatch Logs centralization offers a straightforward pricing model:

Primary destination Region: No additional charge for centralizing logs from source accounts. Standard CloudWatch Logs ingestion and storage pricing applies in the destination account.
Optional backup Region: $0.05 per GB for replicating centralized logs to a second Region for resilience.

The AWS costs of the centralized approach are comparable to a per-account forwarding pipeline. In both cases, you pay for CloudWatch Logs ingestion in the source accounts and Data Firehose delivery to Splunk. Each subscription filter and IAM role requires configuration, monitoring, and error handling. At scale, the engineering time spent maintaining per-account pipelines, troubleshooting failed deliveries, and onboarding new accounts can result in a higher TCO. Centralization helps reduce this to a single pipeline to manage.

Implementation considerations

While the described architecture reduces the operational heavy lifting, the following guidelines should be still followed in a multi-account architecture.

Security and compliance

Centralized logging makes log data readily accessible to investigation teams by aggregating and standardizing logs in one location. You maintain complete control over encryption behavior for copied log groups, and you should verify that centralized data is accessible only to authorized personnel with appropriate access controls in place. When configuring IAM roles and policies for the centralization pipeline and Splunk integration, apply the principle of least privilege by granting only the minimum permissions required for each component to function. For example, the IAM role used by Data Firehose should have permissions limited to reading from specific CloudWatch Logs groups and writing to the designated Splunk endpoint, without broader access to other AWS resources. To detect unauthorized changes to IAM policies or roles used in your centralization pipeline (this solution is described here), you can configure Amazon EventBridge rules that match IAM API calls recorded by AWS CloudTrail and send notifications through Amazon Simple Notification Service (Amazon SNS).

Hybrid approach considerations

Consider whether a purely centralized approach or a hybrid model fits your organization. Application logs and metrics are often best kept in the workload account for day-to-day troubleshooting, while a separate logging account aggregates all workload logs for centralized analysis and operations.

Scaling with your organization

The centralization feature is designed to automatically handle new accounts and log groups without manual configuration updates. If you use AWS Control Tower or other multi-account management solutions, new accounts are automatically included in centralization rules based on your organizational scope. Enable CloudWatch Logs deletion protection on centralized log groups to prevent accidental deletion, and consider using AWS Organizations service control policies (SCPs) to enforce broader logging requirements across member accounts. Define log retention policies in the destination account based on your compliance requirements, and use CloudWatch Logs centralization rule health metrics to monitor replication status across accounts. For organizations with multiple business units, you can create separate centralization rules with different scopes and destination log group naming patterns to maintain logical separation of log data.

Conclusion

You can use Amazon CloudWatch Logs centralization to consolidate logs from across your organization in AWS into a single ingestion point for Splunk. It reduces the operational overhead of custom aggregation architectures while maintaining security boundaries and data lineage.

Splunk – AWS Partner Spotlight

Splunk is an AWS Specialization Partner with Competencies in Cloud Operations, Data and Analytics, DevOps, and more. Leading organizations use Splunk’s unified security and observability platform to keep their digital systems secure and reliable.

Contact Splunk | Partner Overview | AWS Marketplace

Architecting agentic AI for scale and trust from the start

Jerry Chen — Mon, 04 May 2026 15:09:34 +0000

By: Noel Williams, Banking and Capital Markets Leader — PwC Australia
By: Sanya Mediratta, Senior Manager, Digital Trust – PwC Australia
By: Jerry Chen, Senior Solutions Architect – AWS

The race to deploy AI agents is accelerating—and so is the cost of getting it wrong.

The IBM Cost of a Data Breach 2025 report found that 13% of organizations using AI experienced a breach. Of those, 97% identified weaknesses in AI access controls. Recent research from PwC shows companies with a mature responsible AI program recover faster from incidents. Those without one take more than three times as long—and might never fully recover value.

AI introduces new risks. But many existing controls, such as data governance, access controls, and operational processes, remain essential. In fact, AI can amplify any weaknesses in those controls at speed and scale.

Responsible AI works when organizations integrate AI-specific safeguards into existing risk frameworks and controls are engineered into the agentic platform infrastructure. Controls must be designed upfront that continue to run as core functionality of the agent, throughout the agent’s lifecycle. This is what moves organizations from pilot to scaled value.

You don’t need to start from scratch. But you do need to design trust from day one. This post brings together PwC’s risk and governance experience with Amazon Web Services (AWS) technical architecture and tooling. It sets out a practical blueprint for deploying AI agents safely—and at scale.

Three questions your board will ask

Let’s examine a common use case where your organization deploys an AI agent to handle customer support for triaging inquiries, resolving issues, and escalating complex cases. Within months, it’s successfully handling thousands of interactions daily at impressive speed, cost, and scale.

Before that happens, three questions will determine whether the solution scales and whether it remains trusted:

Can we explain how this solution makes decisions before we deploy it?
Who owns, monitors, and evaluates the overall solution performance, and when should they intervene?
Can we demonstrate sustained and trusted performance, and do we have evidence to prove it?

The following graphic illustrates these questions and how one naturally leads to the next.

Figure 1 – Illustration of the question sequence

The following sections go into each of these questions in detail.

Can we explain how this solution makes decisions before we deploy it?

Your customer support agent will triage thousands of inquiries daily. Before it touches a single customer interaction, your board needs confidence that they understand why it makes those decisions. Explainability isn’t an afterthought—it needs to be architected in from day one.

Operationalizing agentic AI on AWS in the AWS Prescriptive Guidance documentation provides practical frameworks across three dimensions:

Strategy — AI roadmap development with clear linkage to business outcomes, establishing governance frameworks that prioritize explainability from the outset
People — Structured skills development so relevant teams can interpret and communicate AI decisions
Technology — Agentic workflow design and system integration patterns that build observability and explainability into the architecture

AWS also has services and features that are particularly useful for you in this context:

Amazon Bedrock Guardrails Automated Reasoning checks can detect hallucinations, suggest corrections, and highlight unstated assumptions in the response of your generative AI application. More importantly, Automated Reasoning checks can explain why a statement is accurate using mathematically verifiable, deterministic formal logic.
Amazon SageMaker Clarify helps organizations detect bias in their AI models and explain why a model made a specific decision in plain terms that regulators, auditors, and boards can understand.
Amazon Bedrock AgentCore Observability helps you trace, debug, and monitor agent performance in production environments. It offers detailed visualizations of each step in the agent workflow, so you can inspect an agent’s execution path, audit intermediate outputs, and debug performance bottlenecks and failures.

Illustrative PwC governance checkpoints

Ask the following questions before deployment:

Has the board aligned on commercial rationale, values, and risk appetite?
Does this AI use case comply with applicable legal and regulatory requirements?
How does this agent use personal, third-party, and confidential information—in both training data and at inference?
Does the training data reflect all customer segments? Have potential blind spots been identified?
Can individual decisions be explained—not only model performance?
Do outputs make intuitive sense to domain experts?

Who owns, monitors, and evaluates this solution’s performance, and when should they intervene?

Your agent is live. It’s handling 5,000 customer inquiries daily. Performance looks strong initially, but in week three, escalation volumes rise and resolution rates drop.

Without end-to-end observability across the agent’s reasoning chain, your operations team sees only the surface symptoms and can’t isolate the root cause.

Without clear technical thresholds and defined governance ownership, teams start pointing fingers. Nobody knows whether to pause the agent, restrict it to lower-risk interactions, or keep watching.

The AWS Well-Architected Generative AI Lens outlines design principles that help organizations track, control, and intervene in AI agent performance:

Observability and reporting — Comprehensive monitoring of performance, cost, and security across foundation models (FMs)—giving teams a single view of system health
Overload mitigation — Rate limits, workflow tracing, and circuit-breaker patterns to understand how the system behaves under stress and prevent cascading failures
Operational controls — Timeout mechanisms on agentic workflows and automated conditions to halt long-running or anomalous processes before they cause harm

PwC teams have helped customers adopt AWS generative AI solutions to optimize application performance. For example, they used the Well-Architected IaC Analyzer to provide prescriptive recommendations by automatically analyzing infrastructure as code (IaC) artifacts, shortening performance evaluation time from hours to minutes.

Illustrative PwC governance checkpoints

Operational accountability requires clear answers to the following questions:

Is there a designated product owner with well-defined roles and responsibilities across enabling teams?
Have evaluation metrics been defined before deployment—not after the first incident?
Are change and release management controls applied consistently to all AI models?
Does the organization have observability across the agent’s full reasoning chain—not only surface-level performance metrics?
Is there a documented change management process capturing what changed, why, and what was retested?

Can we demonstrate sustained and trusted performance, and do we have evidence to prove it?

The agent has been live for 12 months. A customer disputes a refund denial and lodges a complaint with a financial conduct regulator. The regulator asks, “Show us how you handled this customer’s inquiry. Was the decision fair? How do you know?”

You have 48 hours to respond.

Observability and auditability need to be built into the architecture from the start—not retrofitted under pressure. Three AWS capabilities are foundational here:

Amazon Bedrock AgentCore Observability — AgentCore Observability enables real-time monitoring of agentic systems through Amazon CloudWatch powered dashboards tracking latency, token usage, and error rates, with OpenTelemetry-compatible trace visualizations for debugging performance bottlenecks.
AWS Audit Manager AI best practice framework v2 — AWS Audit Manager provides a prebuilt standard framework to help you gain visibility into how your generative AI implementation on Amazon Bedrock and Amazon SageMaker AI is working compared to AWS recommended best practices.
AWS CloudTrail — Captures API calls for generative AI workloads, including Amazon Bedrock Agents and Amazon Bedrock Knowledge Bases. AWS CloudTrail Lake enables SQL-based querying to reconstruct decision chains.

Illustrative PwC governance checkpoints

Auditability and fairness require ongoing discipline:

Does the organization maintain a complete log of all agent decisions, including the rationale for each?
Are quarterly fairness audits conducted comparing agent performance across customer segments?
How does the organization use continuous monitoring and automated testing to detect model drift?
Is there a documented response protocol for disputes and can the agent’s compliance with that protocol be measured?
Can the organization demonstrate that corrective action has been taken to prevent recurrence?

Building trust at scale

Here’s the competitive reality: organizations that move fastest aren’t the ones that skip governance—they’re the ones that embed it from the start. Transparent decision-making, clear observability and audit trails, and rigorous review processes aren’t constraints on speed. Done right, they’re a competitive advantage, demonstrated to customers through fair outcomes, to boards through measurable controls, and to regulators through auditable evidence.

Australia’s Voluntary AI Safety Standards, published in October 2025, reinforce this approach. The ten guardrails—spanning transparency, human oversight, contestability, and accountability—are principle-based by design, giving organizations latitude to tailor implementation to their specific risk profile. But that flexibility comes with responsibility. In the absence of prescriptive rules, the onus falls on organizations to demonstrate that their governance is fit for purpose.

PwC and AWS bring complementary strengths to this challenge. PwC’s deep experience in governance, risk, and responsible AI frameworks, paired with the technical architecture, tooling, and operational guidance of AWS provides not merely a strategy but a deployable path to AI agents that are trusted and trustworthy.

Ready to get started? Explore the AWS Well-Architected Generative AI Lens and reach out to your AWS and PwC teams to discuss how to safely implement a deployable path to AI agents within your organization.

PwC – AWS Partner Spotlight

PwC is an AWS Premier Tier Services Partner that helps you drive innovation throughout IT and the business to compete in today’s service economy.

Contact PwC | Partner Overview | AWS Marketplace

Orchestrating Intelligent Agents at Scale: How AgentCore and Temporal Create Robust AI Systems

Ruskin Dantra — Mon, 27 Apr 2026 20:42:58 +0000

By: Ruskin Dantra, Sr. Solutions Architect – AWS
By: Shun Mao, Sr. Solutions Architect – AWS
By: Justin Lim, Sr. Solutions Architect – AWS
By: Cornelia Davis, Sr. Staff Developer Advocate – Temporal

Temporal Technologies

Building durable, resilient, distributed systems has always been a challenge. When building traditional distributed systems, you must implement retry logic, circuit breakers, timeout handlers, and complex state machines to handle network failures and service outages gracefully. You often need to write more lines of code to handle these error conditions than for business logic.Agentic AI amplifies these challenges dramatically. Modern AI applications are increasingly running for extended periods of time, coordinating multiple specialized agents, each calling external systems and producing non-deterministic outputs. Where internet-based applications might have historically depended on dozens or hundreds of downstream service invocations, agents serving a user need today regularly involve an order of magnitude more calls over the network. Add human in the loop (HITL) approvals, extended reasoning chains spanning hours or days, and the need to preserve conversation history across failures, and traditional error handling approaches become unmaintainable.

In a previous blog post we covered how Temporal offers durable execution for distributed architectures. In this post, we demonstrate how Temporal integrates with Amazon Bedrock AgentCore to make your agentic workflows durable, resilient, and production ready.

Agentic AI and the durability challenge

Agentic workflows follow a common architecture practice centered around an agentic loop. Each of these agents requires many external calls to different services and systems, and like any distributed system, you need to implement robust error handling and retry mechanisms for these interactions. Your developers must manage dozens of potential failure points when implementing an agent. Moreover, modern AI applications rarely rely on a single agent for a complex business problem. For instance, a financial advisory system might orchestrate a budget analyzer, investment strategist, tax optimizer, and risk assessor. A supply chain application could coordinate demand forecasters, inventory managers, supplier negotiators, and logistics planners. This further increases the likelihood that there will be some failure that needs to be handled during a single customer interaction.

Traditional solutions require developers to write thousands of lines of defensive code: retry logic with exponential backoff, state management to track progress, timeout handlers, circuit breakers, and recovery mechanisms. In this post, we look at how Temporal helps by improving developer velocity and providing production-grade reliability to complex agentic workflow development.

Durable agentic workflows with Temporal

AgentCore is an agentic platform where developers can build, scale and operate AI agents without the need for infrastructure management. Let’s consider the following agentic use case without Temporal where you have created a multi-agent financial advisory system as shown in the following figure:

Figure 1: Multi-agent financial advisory system architecture

In the preceding diagram, each of the agents – the budget, financial analysis and the orchestrator agents – operate in a continuous cycle in which they reason, select tools, execute actions, and respond. Each consists of three core components: the model (reasoning engine), the tools (external capabilities) and the agent (agentic loop). AgentCore provides built-in resilience for agent execution. For complex multi-agent workflows involving multiple coordination points, HITL, approvals, and long-running processes, you can use workflow orchestration tools like Temporal to simplify state management and coordination. Let’s consider the same workload, now with Temporal. Your agentic orchestration logic can be managed by a Temporal workflow. In a multi-agent financial advisory system, the orchestrator agent’s workflow might look like this:

Receive user request: “Help me plan for retirement”
Invoke the model to reason about the request
Delegate to the budget agent (activity: analyze current finances)
Interact with the user to take in additional input
Delegate to the investment agent (activity: evaluate portfolio options)
Delegate to the tax optimizer agent (activity: identify tax-advantaged strategies)
Synthesize the results and return a comprehensive recommendation

Figure 2: Multi-agent financial advisory system architecture with Temporal

The workflow coordinates the agent’s reasoning cycle: invoke the model to analyze the user’s request, determine which tools (or agents) to call, execute those tools (or agents), feed results back to the model for continued reasoning, and ultimately return a response. Temporal orchestrates the workflow—managing retries, pauses, human interactions, and state persistence—while AgentCore makes sure the agents themselves are trustworthy, compliance-aligned, and consistently available. Temporal activities facilitate durability through event sourcing, persisting each workflow step as an immutable event so that completed actions aren’t re-executed. Large language model (LLM) invocations, in addition to the budget and financial analysis agents, are wrapped in Temporal activities. If the LLM is being rate limited, for example, Temporal will manage retries, even taking hints from the model API response to control the retry cycle. As the workflow executes, Temporal tracks progress and automatically preserves results from the LLM and the agent invocations, allowing the workflow to pick up where it left off in the event the process is halted. Resulting in no reissuing of tokens or unexpectedly different outcomes from the LLM in these recovery scenarios. When HITL approval is needed, the combination of Temporal and AgentCore will optimize resource utilization (including freeing compute resources entirely) while waiting as long as necessary for a response from the human. When approval arrives, the workflow state is restored, and the process resumes exactly where it paused. The combination of AI optimized infrastructure management from AgentCore and application management from Temporal means companies can handle millions of complex, high-stakes, long-running workflows with confidence, knowing that failures won’t compromise security, compliance, or user experience.

Customer story

A leading global consumer packaged goods company adopted AgentCore and Temporal to modernize its AI-powered marketing and supply chain workflows. Previously, the company faced challenges with unreliable custom-built state machines and queue-based architectures that struggled to handle millions of transactions and asynchronous interactions. By integrating AgentCore secure runtime with Temporal’s workflow orchestration, the company standardized processes like real-time promotions, loyalty program management, and supply chain automation. This resulted in a 66% reduction in backend costs compared to third-party solutions, improved operational resilience, and the ability to process millions of events monthly with full auditability and observability. The architecture now supports rapid onboarding of new use cases and global scalability while also supporting compliance and governance.

Conclusion

Amazon Bedrock AgentCore powers intelligent agents at scale. Temporal makes it more straightforward to make sure those agents deliver results with enterprise-grade durability. Together, they bridge the gap between AI reasoning and reliable production systems—transforming experimental agents into mission-critical infrastructure. Get started by exploring AgentCore plus Temporal samples. You can also check out Temporal on AWS Marketplace and deepen your understanding through other tutorials and demos.

Temporal – AWS Partner Spotlight

Temporal is an AWS Advanced Technology Partner that offers a robust workflow orchestration solution. The platform delivers increased value to customers through its durable execution framework, which ensures consistency and automatically recovers from system failures.

Contact Temporal | Partner Overview | AWS Marketplace

Announcing the Regional 2026 AWS Partners of the Year for Asia Pacific and Japan

AWS Editorial Team — Fri, 24 Apr 2026 14:58:01 +0000

We are excited to announce the regional 2026 AWS Partners of the Year for Asia Pacific and Japan (APJ). These annual awards recognize AWS Partner Network (APN) members who consistently deliver exceptional results and drive innovation for our customers.

Throughout 2026, our winning partners have demonstrated their expertise by delivering transformative cloud solutions and creating measurable business value through AWS technologies. They’ve successfully guided customers through complex migrations and helped organizations optimize their AWS environments for maximum impact.

The collaboration between AWS and our partners continues to be fundamental to customer success. These partnerships drive innovation and help customers take full advantage of AWS services to accelerate their growth.

Regional Awards are comprised of the following four awards:

Consulting Partner of the Year: Recognizes our top AWS Consulting Partners that have provided significant contributions related to revenue, launched opportunities, net new certified individuals, and AWS designations earned.
Rising Star Consulting Partner of the Year: Recognizes AWS Consulting Partners that have seen significant YoY growth in their business.
Technology Partner of the Year: Recognizes our top AWS Technology Partners that are using AWS to lower costs, increase agility, and innovate faster.
Rising Star Technology Partner of the Year: Recognizes AWS Technology Partners that have seen significant YoY growth in their business.

Please join us in congratulating our 2026 AWS Partner of the Year award winners!

Japan

Consulting Partner of the Year – Classmethod
Rising Star Consulting Partner of the Year – Benjamin Inc.
Technology Partner of the Year – Trend Micro Incorporated
Rising Star Technology Partner of the Year – KnowBe4, Inc.

Korea

Consulting Partner of the Year – MegazoneCloud
Rising Star Consulting Partner of the Year – Doosan Corporation Digital Innovation BU
Technology Partner of the Year – Datadog
Rising Star Technology Partner of the Year – Sendbird

Announcing the Regional 2026 AWS Partners of the Year for Europe, Middle East, and Africa

AWS Editorial Team — Fri, 24 Apr 2026 14:57:10 +0000

We are excited to announce the regional 2026 AWS Partners of the Year for Europe, Middle East, and Africa (EMEA). These annual awards recognize AWS Partner Network (APN) members who consistently deliver exceptional results and drive innovation for our customers.

Regional Awards are comprised of the following four awards:

Consulting Partner of the Year: Recognizes our top AWS Consulting Partners that have provided significant contributions related to revenue, launched opportunities, net new certified individuals, and AWS designations earned.
Rising Star Consulting Partner of the Year: Recognizes AWS Consulting Partners that have seen significant YoY growth in their business.
Technology Partner of the Year: Recognizes our top AWS Technology Partners that are using AWS to lower costs, increase agility, and innovate faster.
Rising Star Technology Partner of the Year: Recognizes AWS Technology Partners that have seen significant YoY growth in their business.

Please join us in congratulating our 2026 AWS Partner of the Year award winners!

United Kingdom and Ireland Winners

Consulting Partner of the Year – Devoteam
Rising Star Consulting Partner of the Year – PricewaterhouseCoopers (PwC)
Technology Partner of the Year – Snowflake
Rising Star Technology Partner of the Year – Redgate Software

France Winners

Consulting Partner of the Year – Capgemini
Rising Star Consulting Partner of the Year – KIIRO
Technology Partner of the Year – Salesforce
Rising Star Technology Partner of the Year – Mistral AI

How ClickHouse powers real-time AI Agents on Amazon Quick

Stanley Chukwuemeke — Thu, 23 Apr 2026 15:21:56 +0000

By: Stanley Chukwuemeke, Senior Partner Solutions Architect – AWS
By: Nakul Mishra, Senior Solutions Architect – AWS
By: Luke Gannon, Principal Product Manager – ClickHouse

ClickHouse

Building real-time AI agents requires instant access to analytical data at conversational speed, and integrating ClickHouse with Amazon Quick can deliver the sub-second query performance needed for conversational analytics to customers. When you build AI agents for customer behavior analysis, anomaly detection, or recommendation systems, database queries that take minutes instead of milliseconds degrade the user experience customers expect.

By integrating ClickHouse with Amazon Quick through Model Context Protocol (MCP), you can build AI agents that deliver sub-second query responses across billions of data points, maintaining natural dialogue flow while providing actionable insights instantly. ClickHouse’s millisecond query performance at scale, combined with Amazon Quick enterprise AI capabilities, gives you the tools to build conversational AI agents quickly and improve your interactive experience.

This post walks you through integrating ClickHouse Cloud with Amazon Quick for conversational analytics. We’ll cover the integration benefits, Amazon Quick MCP server architecture, setup steps, authentication configuration, and AI agent creation for real-time data queries.

Solution overview

Amazon Quick comes with a built-in MCP client that you activate through an integration. After it’s configured, it connects to a remote MCP server, automatically discovers the tools and data sources that server exposes, and makes them immediately available to your AI agents and automations with no custom middleware required. This means you can extend Amazon Quick with action execution, real-time data access, and knowledge base creation through a single integration point.

The following figure shows how customers use Amazon Quick to invoke application capabilities, exposed as MCP tools by independent software vendors (ISVs) such as ClickHouse, enterprise systems, or custom solutions through an MCP integration.

Figure 1: Amazon Quick MCP integration with an external MCP server that exposes application capabilities as MCP tools

Amazon Quick and ClickHouse integration architecture

This integration uses ClickHouse remote MCP server, which acts as the bridge between Amazon Quick AI agent and your ClickHouse Cloud service. Amazon Quick supports integrations with various third-party applications and services, with each integration supporting different combinations of actions and knowledge base creation capabilities. The following figure shows a reference architecture for integrating ClickHouse Cloud on AWS to Amazon Quick using ClickHouse remote MCP server. For more information, refer to Amazon Quick supported integrations.

Figure 2: Amazon Quick and ClickHouse integration through ClickHouse remote MCP server

Integration benefits

When AI agents need to make real-time decisions, waiting minutes for database queries fundamentally breaks the conversational experience and limits agent autonomy. ClickHouse columnar storage architecture delivers query results in milliseconds across billions of rows, ensuring AI agents rarely wait on data. With built-in support for streaming inserts and integrations with Amazon Managed Streaming for Apache Kafka (Amazon MSK), Amazon Kinesis, and other event pipelines, ClickHouse ensures that the data your agents reason over is always current. High-volume event and telemetry data that AI agents depend on can quickly become very expensive to store. ClickHouse’s ability to compress data on disk reduces the cost of storing information and the amount of resources needed to query, boosting query performance. ClickHouse speaks standard ANSI SQL with extensions to the language, making it straightforward to connect to existing business intelligence (BI) tools and orchestration layers without specialized adapters. ClickHouse Cloud separates compute from storage, helping organizations to scale query capacity on demand during peak AI workloads without over-provisioning and incurring idle infrastructure costs.

The combination of ClickHouse Cloud and Amazon Quick solves three fundamental business challenges: moving from insight to action in real time, giving nontechnical teams access to complex datasets, and the ability to scale volume without increasing cost.

Amazon Quick AI agents can now query live operational data and act on it. Agents can provide recommendations and trigger workflows or flag anomalies the moment they emerge, rather than after a reporting cycle has completed.

Using Amazon Quick, business users can query complex datasets without writing SQL. Paired with ClickHouse, those natural language queries now run against a database engineered for speed at scale, providing nontechnical teams with accurate, real-time answers without being blocked by data engineering backlogs.

As data volumes grow, most integrations degrade, producing slower queries, higher costs, and more infrastructure to manage. This integration scales with your business. ClickHouse Cloud handles large-scale data while Amazon Quick agents remain responsive, keeping the experience consistent for you regardless of data volume.

Integrating ClickHouse with Amazon Quick

Before you start this integration, check with your security and compliance team to understand your organization’s requirements when integrating tools.

To set up the integration, connect to your ClickHouse Cloud service to retrieve the ClickHouse remote MCP server endpoint.

Currently, the Amazon Quick client MCP can only connect to remote MCP servers that are accessible publicly. Refer to Security and MCP for ClickHouse guidance on exposing their MCP publicly.

To create an MCP integration in Amazon Quick:

Sign in to the Amazon Quick console with a user that has Author permissions or higher.
From the integration grid, choose Model Context Protocol (MCP).
Choose the plus sign ( + ).
On the Create integration page, enter a Name, Description, and your MCP server endpoint URL. Choose Next, as shown in the following screenshot.
Choose Create and then Continue. Review the discovered tools and data capabilities from your MCP server.
When you’re finished, choose Done.

Figure 3: Amazon Quick MCP Integration page

To authenticate to ClickHouse Cloud using OAuth:

After the integration becomes available, choose Sign in to complete authentication.
Authenticate using your ClickHouse Cloud credentials.
If you want other users to use the integration, share it.

The following screenshot shows the ClickHouse sign-in page.

Figure 4: ClickHouse sign-in

After the integration is complete, you create a custom chat agent and add the connector to create a conversational interface that answers specific business questions. Optionally, you can also add this connector to your Amazon Quick space and share it with your team or other parts of your organization either in isolation or across multiple agents. This way, you can scale the integration across multiple agents or combine it with other data sources in the space. Another option is to use this integration with Amazon Quick Flows to automate repetitive tasks and Amazon Quick Research to get a deep understanding of a specific topic by using data in ClickHouse in combination with other sources.

Powering real-time AI Agent with ClickHouse

Through the Amazon Quick AI agent chat interface, business users can query ClickHouse directly using natural language with no SQL knowledge required. Amazon Quick passes the conversation to the ClickHouse MCP server to translate those conversation inputs into SQL actions executed against the ClickHouse database, returning insights at the speed ClickHouse is known for, regardless of data volumes. The ClickHouse MCP server provides a tool called run_select_query that can be used to run a SQL SELECT statement against a ClickHouse database. The MCP server implements all the logic needed to perform that action when the tool is used, for example, for creating a connection or authentication.

For example, in the Amazon Quick AI chat agent, you can ask a question that requires real-time data, such as “What were the top 5 products by revenue yesterday?” The agent should invoke the ClickHouse MCP connector, discover the relevant schema, execute the SQL query, and return a synthesized natural language response with the results.

The following figures show a sample interaction using Amazon Quick AI chat agent.

Figure 5: Sample interaction using Amazon Quick AI chat agent

Conclusion

The integration of ClickHouse Cloud with Amazon Quick delivers sub-second query responses for AI-powered analytics. By combining ClickHouse analytical performance with Amazon Quick AI agents’ natural language capabilities, organizations can unlock real-time insights that transform how teams interact with their data. Using this integration, you can query complex datasets conversationally and make data-driven decisions faster. Start building your first real-time AI analytics agent today by exploring this integration setup for your own use cases and experience how the combination of ClickHouse speed and Amazon Quick intelligence can accelerate your analytics, workflow, and research journey.

For next steps, review Model Context Protocol (MCP) integration and Integrate external tools with Amazon Quick Agents using Model Context Protocol (MCP) for detailed implementation required by third-party partners to integrate with Amazon Quick using MCP.

To get started, sign up for ClickHouse Cloud and Amazon Quick to build your first real-time AI analytics agent.

ClickHouse – AWS Partner Spotlight

ClickHouse, Inc is an AWS Advanced Technology Partner and AWS Competency Partner that is behind ClickHouse, a fast, open source columnar database built for real-time analytics at scale. Engineered for high performance, ClickHouse Cloud delivers exceptional query speed and concurrency, and it’s purpose-built for AI agents generating frequent, complex queries. Trusted by leading companies like Sony, Tesla, Anthropic, and Lyft, ClickHouse delivers a high-throughput, low-latency platform for instant insights from massive data volumes.

Contact ClickHouse | Partner Overview | AWS Marketplace

How SAS Viya Workbench on AWS accelerated analytics innovation

Doug Mbaya — Wed, 22 Apr 2026 20:55:36 +0000

By: Doug Mbaya, Sr Partner Solutions Architect – AWS
By: Dmitry Frizner, Solutions Architect – AWS
By: Erin McCarthy, Senior Product Marketing Manager – SAS

SAS

Data scientists and developers today need to deliver insights faster while managing growing data volumes and meeting compliance requirements. SAS Viya Workbench on Amazon Web Services (AWS) helps accelerate analytics innovation by providing a cloud-centered environment where your team spends less time managing infrastructure and more time building models. If environment configuration, version conflicts, and security approvals are slowing your analytics projects, this solution offers a path forward.

In this post, we explore how SAS Viya Workbench on AWS addresses these challenges through its split-plane architecture, examine real-world use cases across industries, and show you how to get started.

Meeting developers where they are

Working across multiple languages and frameworks such as Python notebooks, R scripts, SAS procedures, and SQL-based workflows creates real obstacles when these tools run on disconnected local machines or legacy infrastructure.

Every environment needs its own infrastructure, security controls, software versions, and data access. Spending valuable time on managing these elements means neglecting more important work. Data scientists often spend hours installing software, resolving version conflicts, and configuring machines when they could be exploring data and generating insights.

Limited access to tools or environments, incompatible workflows, and slow approval processes create barriers to collaboration. Teams resort to emailing files or manually transferring datasets. The lack of a single, shared workspace leads to version mismatches and broken code.

Maintaining robust security and properly complying with regulations is essential, but the process of doing so can slow teams down. They might have to wait days or weeks for IT to approve new tools and infrastructure. But if they bypass controls, they put the organization at risk.

Many software professionals use standard laptops for work. But with fixed memory and processing power, such tools can cause crashes or force analysts to work with only a small sample of the data when demands spike.

Traditional approaches, including maintaining separate environments for each language, manually provisioning compute resources, or relying on desktop installations, struggle to address these challenges together. They typically solve one problem while creating others, forcing tradeoffs between flexibility, security, and scalability.

How SAS Viya Workbench on AWS addresses these challenges

AWS has partnered with SAS to bring SAS Viya Workbench to AWS Marketplace. This unified analytics environment reduces these tradeoffs by offering flexibility in programming languages, cloud scalability, and data custody.

SAS Viya Workbench offers a choice of languages. Your team can work in Python, R, and SAS within a single cloud-based environment, without having to switch between tools or manage separate installations.

By integrating with Amazon Simple Storage Service (Amazon S3), Amazon EMR, and Amazon Redshift, SAS Viya Workbench provides elastic compute that grows with your workload.

You maintain custody of your data within your AWS account. SAS manages only the control plane for orchestration.

With a few clicks, your team can provision fully managed development environments that are secure, collaborative, and optimized for modern analytics workloads.

Understanding the split-plane architecture

Traditional analytics solutions often force a choice between security and flexibility. SAS Viya Workbench addresses this tradeoff through a split-plane architecture.

Before diving into the benefits, review the functions of the control plane and data plane:

Control plane handles management functions such as user authentication, environment configuration, and orchestration.
Data plane processes your actual workloads, running queries, training models, and storing results.

The separation of the control plan and the data plane delivers specific advantages for your organization. Your AWS account contains all compute resources, data storage, and processing engines. Your data doesn’t leave your environment, so you maintain data custody. SAS handles environment orchestration, user management, and service coordination through the SAS-managed control plane, without accessing your data. Your stakeholders in your organization benefits from SAS analytics expertise, while your security team maintains full visibility and control. Connectivity between planes is encrypted with TLS for secure communication without sacrificing performance.

By using this design, your organization maintains control over data and compute resources while benefiting from SAS’s advanced analytics and AI capabilities.

The solution addresses enterprise security requirements through comprehensive protections. All customer data remains within your AWS account, maintaining data sovereignty. End-to-end encryption protects data in transit and at rest. Granular role-based access controls integrate with AWS Identity and Access Management (IAM) and third-party identity providers. Comprehensive logging and monitoring support compliance requirements, with adherence to industry standards including AWS System and Organization Controls (SOC) 2, General Data Protection Regulation (GDPR), and Health Insurance Portability and Accountability Act (HIPAA).

The following diagram shows the SAS Viya Workbench split-plane architecture.

Figure 1: SAS Viya Workbench architecture diagram

Real-world use cases

Organizations across industries are applying this approach to accelerate their analytics initiatives.

In life sciences, cross-functional research teams analyze genomic data at scale using R and SAS in the same environment. One research organization reduced environment configuration time significantly, which freed scientists to focus on analysis rather than infrastructure setup.

Financial services firms improve fraud detection pipelines by integrating Python-based machine learning (ML) with SAS regulatory models. Risk teams maintain compliance while accelerating model development cycles.

Manufacturing operations teams optimize supply chains through real-time analytics pipelines using Amazon S3 and Amazon EMR. When demand forecasting requires additional compute resources, the environment scales automatically rather than requiring manual intervention.

Marketing analytics enhance customer segmentation and campaign effectiveness through integrated multichannel data analysis and predictive modeling.

Retail analytics teams create personalized shopping experiences and optimize inventory through advanced demand forecasting and customer behavior analytics, combining multiple data sources in unified workflows.

Enhancing developer productivity with Workbench

Now that you understand the architecture, let’s examine how SAS Viya Workbench improves daily workflows.

Built-in Git support means that developers can collaborate with each other, track changes, and maintain quality code without leaving the environment. Prebuilt templates for Python, R, and SAS models and workflows reduce development time from weeks to days because teams start with proven patterns rather than blank screens. The browser-based integrated development environment (IDE) eliminates local setup and configuration, so developers can launch code, test models, and deploy from their preferred browser. Automatic self-termination shuts down idle sessions after a specified period, helping manage computing costs efficiently.

With infrastructure management handled by the environment, your team focuses on building, training, and deploying models, whether using R for statistical modeling, Python for ML, or SAS for regulatory reporting.

The following table summarizes the key benefits:

Benefit	Value delivered
Multi-language support	Flexibility to choose Python, R, or SAS without switching environments
Enterprise security	Data stays in your AWS account
Cloud-based scalability	Auto scaling with AWS services
Faster time to insight	Prebuilt environments and templates
Reduced overhead	No infrastructure to manage

Why this matters for your organization:

Democratized analytics access – SAS Viya Workbench provides an intuitive, web-based interface for technical users and data scientists who prefer a code-first approach. Teams work with preferred programming languages while maintaining consistency in governance and deployment.
Elastic scalability – The environment takes advantage of the elasticity of AWS and automatically scales compute resources based on workload demands, supporting optimal performance during peak usage and minimizing costs during idle periods.
Accelerated time-to-value – Teams can start analyzing data immediately because of the pre-configured environments and extensive example libraries. They can use existing Python, R, or SAS code with minimal modifications, start from prebuilt templates, or code from scratch.
Collaborative innovation – Built-in collaboration features support project teamwork, insight sharing, and model deployment. Version control, project templates, and shared computing resources foster innovation while maintaining reproducibility.

Conclusion

SAS Viya Workbench on AWS combines the analytical power of SAS with the scalability and security of AWS infrastructure. The split-plane architecture addresses data security and sovereignty concerns while providing collaboration and scalability benefits of cloud-based solutions.

For organizations that want to modernize analytics capabilities, this solution offers a compelling path forward that focuses on generating insights rather than managing infrastructure. At the same time, they maintain the necessary enterprise-grade security and compliance.

Get started today

Explore SAS Viya Workbench in AWS Marketplace to see how your team can:

Collaborate securely across Python, R, and SAS
Scale analytics workloads on demand
Speed up model development and deployment

Contact your AWS account team or visit the SAS Viya Workbench listing in AWS Marketplace to learn more.

We’d love to hear about your analytics modernization journey; share your experiences in the comments section.

SAS – AWS Partner Spotlight

SAS is an AWS Advanced Technology Partner and AWS Competency Partner and the founder and future of analytics, harnessing the power of data and AI to shorten the path from questions to answers. The result is solutions like SAS® Viya® Workbench and SAS Customer Intelligence 360 that deliver productivity, performance and trust to help our customers make better decisions, faster

Contact SAS | Partner Overview | AWS Marketplace

Build a secure, scalable deployment pipeline with CircleCI’s AWS Deployment Pipeline Reference Architecture implementation

Shalabh Nigam — Wed, 22 Apr 2026 20:43:04 +0000

By: Shalabh Nigam, DevSecOps Solutions Architect – AWS
By: Nitin Kuriakose, Senior Field Engineer – CircleCIer – CircleCI

CircleCI

Modern software teams face the critical challenge of needing to deploy applications faster while maintaining security and compliance. Organizations in regulated industries spend significant sprint capacity on manual compliance checks. Enterprises managing multi-Region deployments face extended deployment windows with elevated failure risks. Security scanning often becomes a bottleneck rather than an automated safeguard.

CircleCI addressed these challenges by building a reference implementation of the AWS Deployment Pipeline Reference Architecture (DPRA). This implementation demonstrates how to build enterprise-grade deployment pipelines that are secure by design, scalable across Regions, and integrated with best-in-class DevOps tools. This post explores how CircleCI created this implementation and how organizations can use it as part of CircleCI’s Integrated DevOps Toolchain (IDT).

Understanding the challenge

Organizations building deployment pipelines face challenges that standard continuous integration and continuous delivery (CI/CD) tools don’t adequately address. The compliance burden means organizations spend significant sprint capacity on manual security reviews and audit documentation. Multi-Region complexity creates deployment windows extending for hours, with each Region representing a potential failure point. The security scanning gap occurs when organizations struggle to implement comprehensive scanning without creating bottlenecks—requiring integration of multiple tools for container vulnerabilities, secrets detection, static analysis, and infrastructure compliance. The tool integration challenge emerges as enterprises manage 10–20 tools in their DevOps toolchain, with integration maintenance diverting engineering resources from feature development.

These challenges create a false choice between speed and safety. CircleCI’s DPRA implementation eliminates this trade-off.

What is the AWS Deployment Pipeline Reference Architecture?

The DPRA provides prescriptive guidance for building secure, automated deployment pipelines on AWS. It defines architectural patterns for progressive deployments, automated testing, security scanning, and multi-environment orchestration. Rather than prescribing specific tools, the DPRA establishes a framework that organizations can implement using their preferred CI/CD platform and DevOps tools. CircleCI’s reference implementation demonstrates how to realize the DPRA vision using CircleCI’s CI/CD platform integrated with AWS services and third-party security tools. This implementation serves as a reference blueprint that organizations can fork, customize, and deploy in their own environments.

Why CircleCI built a DPRA reference implementation

CircleCI built this reference implementation to address a fundamental question their customers and partners ask: “What does great look like in a DevOps pipeline?” Organizations know they need to modernize their deployment practices, but they lack concrete examples of reference architectures demonstrating best practices.

The reference implementation serves multiple strategic purposes: accelerate customer adoption by providing a working example that customers can fork and customize; demonstrate AWS integration depth through built-in integration with AWS services including AWS CodeDeploy, Amazon CloudWatch, Amazon Elastic Container Service (Amazon ECS), and Amazon Aurora; validate CircleCI’s IDT by proving the approach delivers measurable outcomes; and enable partner replication by creating a blueprint that AWS Partners can follow to build their own DPRA implementations.

About CircleCI

CircleCI is a standalone CI/CD platform with deep AWS integrations and more than 3,500 partner integrations across the DevOps toolchain. Teams standardize on CircleCI to accelerate delivery; adopt platform-agnostic infrastructure as code (IaC) such as AWS Cloud Development Kit (AWS CDK), Terraform, or Pulumi; and enforce security by design with OpenID Connect (OIDC), policy controls, and comprehensive testing insights. And they can achieve all this without being tied to a single cloud or version control system. CircleCI’s AI-powered capabilities include automated flaky test remediation that identifies and resolves intermittent test failures without manual intervention.

CircleCI’s platform provides enterprise-grade features including reusable configuration, CircleCI Contexts for secure credential management, Test Insights for tracking performance trends, Workspaces for artifact transfer, Matrix Jobs for multi-Region deployments, and the Orb ecosystem with pre-built, reusable configuration packages available at Orbs in the CircleCI Developer documentation.

CircleCI and AWS: A strategic partnership

The collaboration between AWS and CircleCI represents a proven approach to DevOps transformation validated across hundreds of customer implementations. CircleCI holds the AWS DevOps Competency, demonstrating deep expertise in AWS services and best practices. Unlike generic CI/CD tools that treat AWS as merely another deployment target, CircleCI’s AWS integration includes built-in OIDC authentication that eliminates long-lived credentials, optimized AWS service integration with CodeDeploy, CloudWatch, Amazon ECS, and Aurora, first-class AWS CDK support with flexibility for Terraform or Pulumi, and a platform-agnostic approach supporting multi-cloud and hybrid environments. This partnership enables the IDT, a comprehensive solution delivering turnkey excellence through pre-validated tool combinations, personalized solutions from more than 3,500 integrations, fast time to value with pre-built configurations, cost optimization with transparent pricing, and compliance-ready security controls.

Solution architecture

CircleCI’s DPRA reference implementation demonstrates enterprise-grade deployment patterns across three AWS environments: beta, gamma, and production. The implementation deploys a Java Spring Boot API running on Amazon ECS with Aurora database, showcasing real-world architectural patterns.

The architecture incorporates progressive deployment with AWS CodeDeploy for blue/green deployments with automated rollback based on CloudWatch alarms; multi-Region orchestration using CircleCI matrix jobs for parallel deployments; comprehensive security scanning with Trivy (container vulnerabilities), GitGuardian (secret detection), SpotBugs (static analysis), and cdk-nag (infrastructure compliance); automated testing with JUnit, SoapUI, and JMeter integrated with CircleCI Test Insights; and IaC using AWS CDK with reusable constructs.

The following diagram illustrates this architecture.

Figure 1: CircleCI DPRA reference implementation architecture

CircleCI made several critical architectural decisions. They chose to use OIDC over long-lived credentials for AWS authentication, eliminating security risks and simplifying credential rotation. They implemented security scanning at every stage rather than as an end-of-pipeline gate, catching issues earlier when they’re cheaper to fix. Platform-agnostic tooling supported AWS CDK, Terraform, and Pulumi to avoid vendor lock-in. Progressive deployment patterns using blue/green deployments with automated rollback minimized risk while maintaining rapid release velocity.

Business impact

Organizations implementing CircleCI’s DPRA reference implementation as part of the IDT typically achieve the following improvements:

50% reduction in deployment time through automated progressive deployments and parallel Region rollouts
85% decrease in security vulnerabilities using integrated scanning
3 times faster incident resolution with comprehensive monitoring using CloudWatch Synthetics and CircleCI Test Insights
40% reduction in configuration errors through reusable CircleCI configurations and pre-commit hooks.

Beyond these metrics, organizations report increased developer confidence in deployments, reduced on-call burden through automated rollbacks, and faster onboarding for new team members using documented reference patterns.

Getting started

Organizations can implement CircleCI’s DPRA reference implementation in an afternoon. Begin by forking the reference implementation from the GitHub repository, connect the repository to your CircleCI account, configure AWS credentials using OIDC authentication, customize the AWS CDK templates for your application and environments, and deploy the infrastructure to run your first pipeline.

The repository includes comprehensive documentation, preconfigured pipeline definitions, and step-by-step setup instructions. For organizations ready to go beyond the reference implementation, CircleCI and AWS offer IDT workshops where solution architects help design complete toolchain strategies. For partners interested in building their own DPRA implementations, the reference implementation serves as a blueprint. The AWS DPRA documentation provides additional guidance.

Building your own IDT and contributing to DPRA

AWS Partners can build their own IDT implementations to extend the DPRA ecosystem with validated, approved reference solutions. To do so, you need to select your core toolchain (CI/CD, security, monitoring, and IaC tools), build the reference implementation by forking DPRA architecture and integrating your tools, validate with pilot customers, and contribute back by publishing to GitHub with documentation.

For technical requirements, you need AWS service integration through OIDC, CodeDeploy, CloudWatch, and Amazon ECS or Amazon Elastic Kubernetes Service (Amazon EKS), multistage security scanning, progressive deployment patterns with automated rollback, and IaC with reusable modules.

To contribute to the DPRA ecosystem, submit implementations through the AWS Partner Network portal with architecture diagrams and guides. Participate in DPRA working groups and present at AWS events. Benefits include marketplace differentiation, accelerated customer adoption, deeper AWS partnership, and the potential to be featured in AWS case studies and documentation.

Conclusion

CircleCI’s implementation of the AWS DPRA demonstrates that organizations don’t have to choose between deployment speed and security. By combining the robust cloud infrastructure of AWS with CircleCI’s flexible CI/CD platform and an ecosystem of more than 3,500 integrations, organizations can build deployment pipelines that are secure by design, scalable across Regions, and tailored to their specific requirements. The reference implementation serves as more than merely a technical blueprint—it’s a gateway to comprehensive DevOps transformation through the IDT. Whether you’re starting with the reference implementation or ready to design your complete IDT, CircleCI and AWS provide the expertise, tools, and support to accelerate your DevOps journey.

To get started as an AWS Partner, review the DPRA documentation and CircleCI’s reference implementation, identify your value proposition, and engage your AWS Partner Development Manager. After accessing AWS Partner Central resources, join the DPRA partner community for support. To get started with CircleCI’s Deployment Pipeline Reference implementation, visit the aws-deployment-pipeline-reference-architecture source code on GitHub. Visit CircleCI in AWS Markteplace.

CircleCI – AWS Partner Spotlight

CircleCI is an AWS DevOps Competency Partner and a leading CI/CD platform with deep AWS integrations and more than 3,500 partner integrations across the DevOps toolchain. CircleCI helps organizations accelerate delivery, adopt platform-agnostic infrastructure as code, and enforce security by design—all without being tied to a single cloud or version control system.

Contact CircleCI | Partner Overview | AWS Marketplace

Impetus LeapLogic’s automated path to Amazon SageMaker

Sujit Singh — Tue, 21 Apr 2026 18:19:34 +0000

Impetus Technologies

By: Sujit Singh, Senior Partner Solutions Architect – AWS
By Dayananda Swamy VT, Partner Solutions Architect – AWS
By Ashish Dahiya, Principal Architect, Impetus Technologies
By Arpan Bhandari, Technical Architect, Impetus Technologies
By Gurvinder Arora, Product Marketing, Impetus Technologies

Legacy on premises machine-learning (ML) environments are often constrained by fixed infrastructure, fragmented tooling, and manual deployment processes. As data volumes grow and models become more complex, these limitations slow experimentation, delay production rollouts, and increase operational overhead.

By migrating these workloads to Amazon SageMaker, enterprises can standardize model development, automate training and deployment, and scale ML workloads on demand without managing underlying infrastructure. SageMaker provides a managed foundation for experimentation, versioning, monitoring, and production grade deployment across the ML lifecycle.

Traditional migration approaches often require months of manual refactoring, custom scripting, and repeated validation cycles, introducing delivery risk and slowing business outcomes. Impetus LeapLogic helps reduce this complexity by automating a significant portion of the migration to SageMaker, minimizing manual effort and accelerating time to production.

LeapLogic, Impetus’s automation led modernization system, helps enterprises transition legacy data and ML workloads into cloud-centric architectures on Amazon Web Services (AWS). Backed by deep experience in data engineering, analytics, and ML modernization, Impetus guides customers through migration from legacy environments to production-ready SageMaker deployments.

Using automated analysis, code and pipeline transformation, and structured validation, LeapLogic enables enterprises to modernize faster while maintaining accuracy, continuity, and operational stability.With Impetus LeapLogic, enterprises can:

Standardize ML development and deployment on SageMaker
Automate large portions of legacy pipeline and workload transformation
Reduce manual refactoring and migration risk
Coexist with existing AWS services while improving ROI on current investments
Move ML workloads to production with greater consistency and governance

This blog post describes how SageMaker and Impetus LeapLogic enable a structured, lower risk modernization of legacy workloads on AWS.

AI operationalization with SageMaker: Scaling ML from development to production

SageMaker streamlines the ML journey by combining automation, scalability, and governance into a single managed environment. From intelligent data prep and model selection to distributed training, automatic tuning, collaborative notebooks, and governed model cataloging, SageMaker helps trim the operational clutter and accelerate teams toward production with fast, monitored, versioned deployments and built-in model explainability.

An integrated experience with SageMaker Unified Studio

SageMaker supports the enterprise data management lifecycle, including data ingestion, scalable ML development, secure model deployment, and advanced analytics, enabling continuous improvement from a single, fully managed interface.Let’s see how these services operate cohesively and offer an integrated environment.

Figure 1: SageMaker Unified Studio with complete AWS stack

The architecture shown in the preceding diagram illustrates how enterprise data flows through a unified SageMaker environment, beginning with diverse real-time and batch sources entering AWS through services such as Amazon Kinesis, Amazon Managed Streaming for Apache Kafka (Amazon MSK), and AWS IoT Core. This raw stream is shaped and refined through AWS Glue, AWS Glue Studio, and AWS Glue DataBrew before landing in the curated zones that support analytics engines such as Amazon Redshift, Amazon EMR, Amazon OpenSearch Service, Amazon Quick Sight, and Amazon Athena. Amazon SageMaker Unified Studio, the integrated development environment within SageMaker, is used to orchestrate feature engineering, experimentation, training, and deployment within a single ML workbench, while also drawing on surrounding AI services for added intelligence. The result is a cohesive environment where data movement, transformation, modeling, and insight generation operate as an integrated workflow, giving teams a governed, scalable, and production ready path from ingestion to applied machine learning.

Real world enterprise use cases

SageMaker excels as a centralized interface and integrated environment for data warehousing, extract, transform, and load (ETL) processes, BI reporting, orchestration, advanced analytics, generative AI, automated machine learning (AutoML), explainability, and model monitoring.By seamlessly integrating with a variety of AWS services, SageMaker unlocks powerful use cases, such as the following, that help drive business value, accelerate AI adoption, and optimize operational efficiency.

Automated data preparation, feature engineering, and predictive modeling
Unified metadata management
Interactive ML-driven BI
Data access controls, usage, monitoring, anomaly detection, and compliance with regulations

Impetus LeapLogic – Elevate intelligence

LeapLogic is Impetus’s automation system designed to modernize complex legacy data, analytics, and ML workloads into cloud native AWS architectures. It supports modernization across data warehouses, ETL systems, orchestration tools, analytics layers, and ML pipelines, enabling enterprises to transition to SageMaker with reduced manual effort and predictable outcomes.Trusted by several Fortune 500 enterprises, LeapLogic simplifies data system modernization with a four-step paradigm:

Workload assessment offers automated workload analysis with actionable recommendations
Business logic transformation translates legacy logic into cloud native vernacular
Validation and reconciliation facilitates seamless business continuity
Operationalization launches your cloud native future.

Beyond LeapLogic, Impetus, as a premier data and AI system integrator, plays a vital role in rapidly developing enterprise class generative AI solutions. With their generative AI Innovation Labs—the industry’s first strategy, design, and build collaborative service offering—enterprises can create a production ready generative AI prototype in less than six weeks.

LeapLogic-powered fully automated stack mapping

Let’s see how LeapLogic with its new AI enabled conversion capabilities, along with Impetus’s transformative services, helps enable automated transformation of legacy tools and technologies to AI-ready SageMaker and its supporting stack or alternative options such as PySpark. The following shows the usage categories and the LeapLogic process to map to AWS services.

Data sources and storage

Automates schema, metadata, and data migration into AWS services such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon DynamoDB, and Amazon Relational Database Service (Amazon RDS) by parsing legacy structures and generating equivalent cloud native storage models
Creates AWS Lake Formation blueprints by converting legacy governance rules and access patterns into reusable templates
Generates validated, optimized AWS workloads by profiling legacy pipelines and producing performance tuned equivalents

Workload modernization

Translates ETL and extract, load, and transform (ELT) logic, jobs, and orchestration scripts into assets compatible with AWS Glue, Amazon EMR, or Amazon Redshift using automated semantic mapping of transformations
Reconstructs business logic by interpreting legacy operators, functions, and procedures and generating their AWS service counterparts
Performs end-to-end functional and row level validation using AWS Glue and Amazon EMR jobs to facilitate accuracy and readiness for production
Auto provisions the appropriate AWS compute and orchestration services needed to implement in production

Compute and infrastructure

Maps on-premises execution patterns to cloud compute constructs (such as Amazon Elastic Compute Cloud (Amazon EC2), AWS Lambda, AWS Fargate, or AWS Batch) by analyzing concurrency, scheduling, and resource needs
Generates infrastructure as code (IaC) deployment templates that automate provisioning, scaling, and operationalization of migrated workloads

Analytics and visualization

Converts Tableau, Cognos, OBIEE, SAS, and Alterx assets into analytical views backed by Amazon Quick Sight, Athena, and Lake Formation by mapping visual and semantic layers
Recreates dashboards and workflows using AWS datasets, facilitating functional parity and improved performance
Suggests conversational analytics enhancements using Amazon Q where applicable
Provisions Amazon CloudWatch dashboards and alerts to monitor analytical workloads

Security, identity, and compliance

Translates existing access policies into AWS Identity and Access Management (IAM) and Lake Formation roles, permissions, and fine-grained access controls
Modernizes identity management by mapping legacy authentication models to identity flows compatible with IAM or Amazon Cognito
Configures SageMaker built-in encryption, key management (AWS Key Management Service (AWS KMS) or AWS CloudHSM), audit trails, and compliance templates aligned to regulatory standards

AI and ML services

Establishes an enterprise generative AI foundation through Impetus’s Strategy, Design, and Build Labs that map business use cases to working prototypes in weeks
Integrates Amazon Bedrock models by generating prompt flows, orchestration logic, and evaluation frameworks
Automates provisioning of Amazon SageMaker AI components, training pipelines, and inference endpoints needed for scalable AI adoption

Internet of Things (IoT)

Configures AWS IoT Core, AWS IoT Greengrass, and AWS IoT FreeRTOS integrations by mapping legacy sensor flows to real-time ingestion pipelines
Builds edge-to-cloud pathways that help transform streaming IoT data into actionable insights using AWS analytics and ML services
Facilitates SageMaker-ready IoT data pipelines for training, inference, and continuous optimization

Security and compliance

Customers implementing LeapLogic must follow AWS security best practices and the AWS Well-Architected Framework Security Pillar. Deployments should adhere to the principle of least privilege for IAM roles, implement encryption for data at rest and in transit, and use AWS security services such as AWS IAM Access Analyzer to validate generated policies. LeapLogic operates within customer AWS account boundaries, and customers retain full responsibility for their security configuration and compliance requirements under the AWS Shared Responsibility Model.

Conclusion

Modernizing legacy workloads to Amazon SageMaker can be a strategic step for enterprises seeking to scale ML adoption while reducing operational complexity. SageMaker provides a managed foundation for consistent training, deployment, and monitoring of ML models across the enterprise.With Impetus LeapLogic, organizations can approach this transformation with greater confidence. By automating key aspects of workload analysis, transformation, and validation, Impetus helps reduce migration risk and shorten time to value enabling enterprises to operationalize ML on AWS in a controlled, production ready manner.

As an AWS Premier Tier Services Partner with certified Machine Learning and Data & Analytics competencies, Impetus helps enterprises modernize and operationalize ML workloads on SageMaker with speed, accuracy, and operational discipline.

To learn more about how Impetus LeapLogic supports automated modernization of legacy workloads on AWS, contact Impetus.

Impetus Technologies – AWS Partner Spotlight

Impetus Technologies is an AWS Premier Tier Services Partner who enables the Intelligent Enterprise with innovative data engineering, cloud, and enterprise AI services. By helping enterprises modernize workloads and leverage cutting-edge AWS technologies, Impetus empowers businesses to innovate, streamline operations, and unlock new opportunities.

Contact Impetus Technologies | Partner Overview | AWS Marketplace

Unlock revenue insights in the new Attributed Revenue Dashboard

Jahnai Bilovsky — Tue, 21 Apr 2026 17:13:34 +0000

By Jahnai Bilovsky, Kelly Bornholdt, and Chris Xu – APN/MP Tech Business Development, AWS

As the cloud industry evolves, Partners have asked for more ways to demonstrate the business impact and return on investment of their solutions. Yet many lack visibility into the actual revenue impact their AWS solutions drive, making it difficult to optimize their go-to-market strategies.

To support this need, we launched Partner Revenue Measurement—a set of capabilities that help Partners measure attributed revenue, demonstrate impact, and unlock mutual growth opportunities.

Partner Revenue Measurement is now how we measure partnership success across Partner programs and benefits, creating a direct line between Partner impact and AWS investment. It provides transparent, data-driven measurement that recognizes Partners based on actual AWS service consumption across Partner and customer accounts.

Now, we’re excited to announce the Attributed Revenue Dashboard in AWS Partner Central, bringing Partner Revenue Measurement insights to life with automated revenue data by Partner product, AWS service, and billing period.

In this blog, we’ll cover the benefits of Partner Revenue Measurement, how the Attributed Revenue Dashboard works, and how to get started.

Benefits

Partner Revenue Measurement delivers precise, automated measurement of the AWS consumption you’re driving—eliminating guesswork and ensuring your full impact is recognized. This enables faster funding decisions with higher investment levels.

This data-driven approach transforms how Partners and AWS collaborate. Here’s what that means for your business:

Revenue impact visibility: See exactly which products and services are driving AWS consumption, with monthly data by Partner product and AWS service to inform strategic decisions.
Smarter co-selling: Demonstrate solution value with quantifiable metrics to AWS sales teams, helping you identify high-performing co-sell areas to focus your resources and drive growth.
Strategic partnership growth: Use performance data to drive business reviews with AWS, optimize your product and go-to-market strategies, and unlock more targeted investment and recognition based on actual impact.
Automation and faster payments: Reduce administrative burden and get the proof points you need to grow your AWS business—eliminating manual reporting, reconciliation, and tracking across multiple programs.
Customer cost management visibility:  Enable customers to identify Partner-provisioned resources and map costs for spend management.

How it works

The Attributed Revenue Dashboard, accessible through Partner Analytics in AWS Partner Central in the AWS Console, provides visibility into the revenue impact your solutions generate.

Figure 1 – Attributed Revenue Dashboard in AWS Partner Central.

The dashboard displays monthly attributed revenue by Partner product, AWS service, and billing period. View consumption patterns, workload-level performance metrics, and growth opportunities—all in one place.

Figure 2 – AWS attributed revenue by AWS service.

Partners can get started with Partner Revenue Measurement using one of three implementation capabilities: Resource Tagging, User Agent string, and AWS Marketplace Metering—providing flexibility to choose the one that works best for their solution architecture.

Resource Tagging: Apply standardized tags to AWS resources using the product code from your AWS Marketplace listing.
User Agent string: Measure the AWS revenue impact of your solution across certain services by embedding a unique product code as a user agent—with options to update application code directly or set an environment variable.
AWS Marketplace Metering: Automatically measures Amazon EC2 and Amazon SageMaker AI service consumption across customer-managed environments when customers purchase and use Amazon Machine Image (AMI) and machine learning (ML) products via AWS Marketplace. No additional implementation effort required.

Figure 3 – Partner product onboarding status by source.

What Partners are saying

Feedback from pilot Partners played a key role in helping us develop the dashboard and Partner Revenue Measurement capabilities. Partners tell us that Partner Revenue Measurement represents a fundamental shift in how AWS recognizes Partner value.

“We see Partner Revenue Measurement as a turning point. It aligns Pier Cloud and AWS on a shared goal: enabling customers to scale, modernize, and succeed with AWS,” says Marcelo Scharan, Founder & CEO, Pier Cloud.

Beyond the strategic value, Partners also appreciate the ease of implementation.

Dack Busch, VP Systems Engineering & Solution Architecture at Qumulo, shares: “This APN ID tag is so easy to implement and what I’ve wanted from AWS for several years to track the demand we drive as an AWS Partner for Amazon S3 and Amazon EC2 services.”

Get started

To access the Attributed Revenue Dashboard, Partners need to migrate to the new AWS Partner Central experience.

To get started with Partner Revenue Measurement:

Have a public listing in AWS Marketplace;
Link your AWS account to your AWS Partner Central account;
Implement Resource Tagging, User Agent String and/or AWS Marketplace Metering capabilities.

Partners with multiple AWS Marketplace seller accounts can use Subsidiary Account Connections to gain visibility of their entire solution portfolio across all seller accounts.

Learn more about the dashboard’s functionality and the latest Partner Revenue Measurement updates in the Onboarding Guide.

Accelerate customer experiences powered by Adobe and AWS agentic AI collaboration

Ranjith Raman — Mon, 20 Apr 2026 14:31:06 +0000

By: Ranjith Raman, Principal Solutions Architect – AWS
By: Anuj Dewangan, Principal Solutions Architect – AWS

Adobe

New product availability and integrations bring enterprise-grade agentic AI capabilities to marketers and developers, enabling seamless workflows across Adobe and AWS.

Adobe and Amazon Web Services (AWS) today announced a strategic collaboration to accelerate enterprise adoption of agentic AI, that enable businesses to build, deploy, and orchestrate AI agents across marketing and customer experiences. The announcements, made at Adobe Summit 2026, demonstrate the companies’ shared commitment to making agentic AI accessible and actionable for enterprises worldwide.

The collaboration introduces bidirectional integration between Adobe Experience Platform Agent Orchestrator and Amazon Bedrock AgentCore, along with a purpose-built Adobe Marketing Agent for Amazon Quick that transforms campaign insight discovery from weeks to seconds. Adobe Experience Platform Agent Orchestrator is now available on AWS, enabling customers to build and deploy intelligent AI agents at scale with the enterprise-grade security, performance and reliability of AWS. Together, these innovations enable enterprises to create sophisticated, cross-platform AI workflows that combine Adobe’s creative and marketing expertise with AWS’s cloud infrastructure and AI capabilities.

“At Adobe, we’re committed to empowering marketers and creatives with AI-driven insights and automation to deliver personalized customer experiences at scale. By bringing Adobe Experience Platform Agent Orchestrator to AWS and integrating our marketing intelligence into Amazon Quick, we’re enabling enterprises to uncover campaign insights in seconds—all while maintaining the enterprise-grade security and scale our customers demand,” says Ankur Jain, Director of Product Management, Ecosystem Solutions, Adobe. “This collaboration represents the next evolution of customer experience, where intelligent agents work seamlessly across platforms to deliver measurable business outcomes.”

Adobe Experience Platform Agent Orchestrator on AWS
Agent Orchestrator in Adobe Experience Platform (AEP) powers customer experience orchestration with purpose-built agents, an advanced reasoning engine, and a powerful conversational interface. Agent Orchestrator, available now on AWS, enables enterprises to deploy Adobe’s agentic AI capabilities with the security, scalability, and global reach of AWS cloud services.

Adobe Marketing Agent for Amazon Quick
Adobe’s AI Agents meet marketers where they are. Adobe Marketing Agent for Amazon Quick enables marketers and business practitioners to discover marketing campaign insights in seconds—a process that traditionally takes days or weeks. Adobe Marketing Agent brings Adobe’s marketing intelligence directly into Amazon Quick’s AI-powered workspace, allowing teams to query campaign performance, audience insights, and content effectiveness through natural language. We’re collaborating with select customers and partners in a private beta to validate scenarios and deployment models ahead of wider availability.

“The future of enterprise AI lies in intelligent agents that work together across platforms to solve real business problems. Through our collaboration with Adobe, we’re making it possible for developers and marketers to build sophisticated agentic workflows that combine Adobe’s deep marketing expertise with AWS’ cloud infrastructure and AI capabilities. With Experience Platform Agent Orchestrator on AWS and integrations with Amazon Quick and Amazon Bedrock AgentCore, we’re delivering the interoperability and scale that enterprises need to accelerate their AI transformation.” – Chris Grusz, Managing Director, Technology Partnerships, AWS

Adobe and Amazon will continue to innovate and bring new agentic integrations across their product portfolios that enable brands to deliver standout customer experiences.

Interacting with Adobe CX Enterprise from within Amazon Bedrock AgentCore Runtime
Developers will be able to access the agentic capabilities in Adobe CX Enterprise within Amazon Bedrock AgentCore Runtime through Model Context Protocol (MCP) servers. This will open new possibilities for developers to build specialized agentic workflows tailored to their unique use cases.

Amazon Bedrock AgentCore agents in Adobe
Agents hosted on Amazon Bedrock AgentCore Runtime will be accessible within Adobe Agent Orchestrator, enabling seamless cross-platform agent workflows. For example, marketers can invoke an agent hosted in Amazon Bedrock AgentCore, to run semantic searches on past campaign brief documents archived in Amazon Simple Storage Service (Amazon S3) directly from their Adobe workflows, combining Adobe’s customer experience agents with the data management capabilities of AWS.

Adobe Customer Experience Skills
Adobe Customer Experience Skills—pre-built capabilities for common marketing tasks—will be accessible from Amazon Bedrock AgentCore Runtime. Developers can incorporate these skills into their custom agents, accelerating development time and ensuring enterprise-grade quality for Adobe-powered workflows.

These collaborations build on the longstanding partnership between Adobe and AWS, which has enabled millions of customers to use Adobe’s creative and marketing solutions on AWS.

With these new agentic AI integrations, the companies are extending that partnership into the next era of enterprise AI, where intelligent agents work together across platforms to automate complex workflows and deliver actionable insights.

Learn more about the innovations that Adobe and AWS are driving together at Adobe Summit 2026.

Adobe – AWS Partner Spotlight

Adobe is an AWS Competency Partner that helps customers create, deliver, and optimize compelling content and applications.

Contact Adobe | Partner Overview

Cisco conversational AI powered by Amazon Lex

Satish Subramanian — Wed, 08 Apr 2026 21:58:56 +0000

By: Aaron Keeton, Business Development Manager – Cisco
By: Jessica Smith, Director Product Marketing – Cisco
By: Soundarya Muthuvel, Product Manager Webex Developer Experience – Cisco
By: Ravi Thakur, Sr. Solutions Architect – AWS
By: Satish Subramanian, Manager Partner Solutions Architects – AWS

When customers call your support line, they’re not thinking about the technology behind it. They want answers – fast, accurate, and without the runaround.

But contact center leaders face a familiar challenge: you’ve invested in Cisco Webex Contact Center, your teams have expertise around it, and it delivers the experience your customers want. Meanwhile, conversational AI is reshaping what customers expect-instant responses, natural conversations, and uninterrupted availability without waiting in line. The pressure to modernize is real but ripping out what’s working isn’t a beneficial strategy.

That’s why Cisco and Amazon Web Services (AWS) took a different approach. They brought AWS AI capabilities into the Cisco environment, so you can modernize without starting over.

The new open source Amazon Lex connector for Webex Contact Center brings intelligent virtual agents into your existing Cisco environment, without rebuilding from scratch. Amazon Lex now plugs directly into Cisco’s Bring Your Own Virtual Agent (BYOVA) framework. With the Amazon Lex integration, you can access a powerful AI-driven virtual agent alongside your contact center workflows without any additional work, providing consistency across voice experiences. Your customers get natural, voice-driven interactions. Your team retains their existing Webex workflows, reporting dashboards, and agent desktop configurations while gaining AI capabilities that would otherwise require months of integration work.

For the more than 10,000 organizations running Cisco Webex Contact Center and Contact Center Enterprise today, this solution removes the trade-off between protecting your investment and adopting AI. You can deploy across many AWS Regions globally, and it supports over 25 languages and automates routine inquiries so that your agents can focus on conversations that really need a human.

Here’s how the integration works, where it fits across Cisco Webex Contact Center and Cisco Contact Center Enterprise, and where customers are already seeing results.

Cisco and AWS integration architecture

This integration connects your Cisco Webex Contact Center to AWS AI services through an open-source connector. The architecture comprises three integrated layers:

Customer interaction layer – Webex Contact Center and Contact Center Enterprise connect through Webex Contact Center AI (CCAI), which manages the virtual agent integration and handles escalations routing to an agent when human assistance is needed.
Connector layer – The open-source connector serves as the technical bridge between platforms, authenticating between Webex and AWS environments, managing real-time gRPC media streams for audio, and orchestrating the conversation flow between platforms.
Conversational AI layer – Amazon Lex provides conversational intelligence through automatic speech recognition (ASR), natural language understanding (NLU), and integration with large language models (LLMs) using Amazon Bedrock. Speech synthesis is handled by Amazon Polly, delivering natural-sounding responses in dozens of voices across multiple languages.

This architecture is illustrated in the following diagram.

Figure 1: Cisco Webex Contact Center integration with AWS AI services

Customer call flow

When a customer calls in to the Webex Contact Center, the following flow takes place:

Audio streams in – The caller’s voice flows from Webex Contact Center through Webex CCAI to the open-source connector. The connector establishes a gRPC media stream to carry the audio in real-time to AWS services. This stream also enables bidirectional metadata exchange, including caller information, historical journey, and subscription data between Webex CC and AWS.
Amazon Lex interprets intent – Amazon Lex processes the customer’s speech through three stages:
1. Speech recognition – ASR converts voice to text, supporting more than 25+ languages
2. Intent understanding – NLU determines what the customer wants, now enhanced with LLM-powered assisted NLU for faster, description-based intent classification
3. Response generation – For straightforward requests, Amazon Lex responds from configured intents. For complex queries requiring nuanced responses, Amazon Lex invokes Amazon Bedrock foundation models (FMs) to generate contextual, conversational replies that go beyond scripted answers.
Response flows back – Amazon Lex formulates the response and passes it to Amazon Polly. Amazon Polly transforms text into natural-sounding speech using neural text-to-speech (NTTS) technology, with support for Speech Synthesis Markup Language (SSML) to control pronunciation, emphasis, and speaking rate. The audio streams back through the connector to the customer using Webex Contact Center.
Escalation when needed – If the virtual agent can’t resolve the issue, the call routes back through Webex CCAI to a live agent within your Cisco Webex Contact Center environment. The agent receives full conversation context—including customer intent, information gathered, and dialog history—eliminating the need for customers to repeat themselves.

This architecture supports multi-turn conversations with contextual awareness requests like “I want to change my appointment from Tuesday to Thursday,” which work without requiring customers to start over.

The customer call flow is illustrated in the following flow diagram.

Figure 2: Cisco Webex Contact Center and Amazon Lex integration call flow

This solution integrates with AWS in the following ways:

AI/ML services – Amazon Lex handles ASR and NLU, converting customer speech to text and determining intent. Amazon Polly delivers lifelike voice responses using Neural Text-to-Speech (NTTS).
Agentic developer tools – Amazon Bedrock AgentCore provides the framework for building and orchestrating AI agents.
Generative AI with Amazon Bedrock – For more complex interactions, Amazon Bedrock connects your virtual agent to FMs, enabling responses that go beyond scripted intents.

The value of this architecture lies in the control it gives you. The Amazon Lex chat assistant lives in your AWS account. You control the intents, responses, and data. The solution is designed for responsive, natural conversations. Security is through cross-account authentication, which means your customer data stays in your environment. This solution is available in AWS Regions that support Amazon Lex endpoints to meet your compliance and data residency requirements.

Industry specific use cases and key benefits

Organizations across industries can find benefits in pursuing this approach, including in healthcare, finance, and government.

Healthcare organizations can enhance patient experience with intelligent routing and reduced wait times by consolidating outdated systems that need a live agent for every task. The solution is integrated with scheduling systems for seamless appointment management using automated booking, rescheduling, or canceling.

Finance organizations can improve customer self-service in situations where they don’t need to talk to a live agent by using intelligent interactive voice response (IVR), chat-based assistants, and virtual agents. Customers can check balances, update personal details, or reset passwords without a live agent. Reduce complexity through unified customer experience platforms and cloud-based solutions.

Governments can operate in Cisco’s secure, FedRAMP-authorized data centers for reliability and compliance. This solution enables migration from on premises to the cloud at the agency’s pace with an intuitive administration portal for faster operations and reduced IT burden.

Business outcomes

Conversational AI transforms routine customer interactions, including billing inquiries, order tracking, and appointment scheduling into seamless self-service experiences. Customers get immediate responses without waiting in queue, while your contact center achieves higher first-contact resolution rates. The result is consistent, quality support available around the clock without proportional staffing costs.

	Step	Traditional process	With Amazon Lex integration
1	Customer initiates	Waits in queue (4–8 min average)	Immediate virtual agent response using Webex Contact Center
2	Authentication	Agent manually verifies (2–3 min)	Amazon Lex authenticates conversationally (30 seconds)
3	Resolution	6–8 minute handle time	60–90 seconds for routine inquiries

Similar efficiency gains apply to order status and appointment scheduling interactions.

Natural language understanding in Amazon Lex goes beyond recognizing what customers say, it understands the intent of the customers. By connecting to customer data, the virtual agent delivers contextual responses. It recognizes repeat callers, references recent interactions, and anticipates needs. This intelligent, empathetic approach builds loyalty by making every customer feel known, not processed.

Conclusion

In today’s Cisco Webex Contact Center deployments, organizations face a defining moment. The need to address how to safeguard existing investments and resources but adopt AI capabilities to get natural, voice-driven interactions. Through the open-source Amazon Lex connector for Webex Contact Center, we’ve demonstrated how customers can bring powerful AI-driven intelligent virtual agents into their existing Cisco environment without significant investments or re-architecture.

Organizations implementing this open-source Amazon Lex connector show improved AI agent efficiency and enhanced customer experience. This solution integrates with proven AWS AI capabilities to improve every interaction and maintain context and intent across complete conversations, improving your customer experience.

Refer to this blog for step by step instructions on how to integrate Webex Contact Center with open-source Amazon Lex connector.

Ready to transform your customer experience strategy? Connect with Cisco and your AWS account team today to schedule a personalized consultation and discover how this integration drives customer satisfaction and business growth for your organization.

Cisco Systems – AWS Partner Spotlight

Cisco Systems is an AWS Specialization Partner that helps customers optimize their cloud strategy by bringing together networking, security, analytics and management.

Contact Cisco | Partner Overview | AWS Marketplace

AI-Powered Test Automation with Rapise and Amazon Bedrock

CJ Sturgess — Wed, 08 Apr 2026 17:20:29 +0000

By Adam Sandman, CEO – Inflectra
By Jessica Moore, Director of Global Alliances – Inflectra
By CJ Sturgess, Partner Solutions Architect – AWS

Inflectra

As organizations accelerate their transition to cloud-based application delivery and agile DevOps, automated testing has become a vital part of maintaining quality at speed. As development teams adopt agentic coding tools such as Kiro, Cursor, Cline and Windsurf, the testing bottleneck has only become greater. However, traditional test automation often requires heavy scripting by human developers, result in brittle tests that require significant maintenance effort, and lack the intelligence needed to support evolving applications. In this post, you will learn how Rapise by Inflectra uses agentic AI powered by Amazon Bedrock to automate test creation, adapt to application changes, and reduce QA cycle times by up to 70%.

Inflectra, an Amazon Web Services (AWS) ISV Accelerate Partner, developed Rapise to bring agentic AI capabilities to enterprise software test automation. Rapise uses Amazon Bedrock and Amazon Nova to automatically generate test scripts, adapt to application changes, and flag compliance issues. This solution addresses the challenges faced by traditional test automation for modern applications.

In regulated industries especially, such as healthcare, life sciences, manufacturing, and financial services, testing is integral to delivery assurance and compliance. But legacy automation frameworks struggle to keep up with the speed of modern, agentic AI enabled software pipelines or the increasing complexity of enterprise systems. Inflectra’s Rapise test automation system, now with agentic AI capabilities through connection with Inflectra.ai, uses foundation models (FMs) hosted on AWS to help bridge this gap.

How Rapise Uses Amazon Bedrock and Amazon Nova

Rapise uses Inflectra’s new cloud-based AI service, Inflectra.ai, built on Amazon Bedrock, which users can use to perform a variety of generative AI and agentic AI enhanced software testing tasks.

Both Rapise and Inflectra.ai use the Amazon Nova Pro and Claude Opus by Anthropic in Amazon Bedrock family of models to perform several key generative AI tasks. These include creating automated test scripts from manual test cases, generating synthetic test data, analyzing test results for patterns, and identifying potentially brittle or underdeveloped tests.

The following screenshot highlights the use of AI capabilities to create automatic test cases.

Figure 1: AI-powered automated test creation in Rapise

With Inflectra.ai, Rapise offers several more advanced agentic AI features that are powered by Amazon Bedrock and the Claude Sonnet by Anthropic in Amazon Bedrock family of models. Testers can use these AI agents to:

Autonomously test an application visually from business scenarios and manual test cases, handling key exploratory testing use cases
Simulate user flows using natural language and carry out test actions without the need for traditional, brittle object-based test automation
Flag usability, accessibility, and compliance issues automatically using visual models based on risk profiles
Automatically refactor existing automated test scripts to adapt to application changes using visual identification, a process known as “self-healing” tests

The following screenshot demonstrates how a tester can include qualitative tasks as part of an automated test case. For example, testers can ask the testing agent: “How many input fields do you see?” or “How does the page look?”

Figure 2: Using qualitative tests and tasks in an automated test case

Agentic AI for Smarter, Scalable Test Automation

Traditional test scripts are often fragile. A minor UI update or API schema change can break test flows. But with the use of agentic AI in Rapise, the automation suite adapts dynamically. Rapise agents learn from prior test executions and application metadata, using this context to self-correct and propose alternative test paths by visually inspecting the application.

This reduces test flakiness, maintenance overhead, and ultimately boosts team productivity. For enterprises in regulated industries, these agents work in concert with the wider Inflectra.ai solution to help enforce traceability and compliance. To do so, Rapise and Inflectra.ai track requirements coverage, automatically tag risks, and log interactions in audit-ready formats.

A leading biotech firm recently adopted Rapise with Inflectra.ai running on Amazon Bedrock to replace their existing, aging test infrastructure. Using Amazon Bedrock powered test agents, their quality assurance (QA) team successfully reduced test case creation time by 60% and identified 35% more critical defects prior to product release. More importantly, Inflectra.ai-generated validation documentation and Rapise-backed traceability reports streamlined their FDA validation workflows.

Rapise and AWS Architecture Overview

The following architectural diagram highlights how Inflectra.ai integrates with Rapise, including support for customer-deployed instances of Rapise. Notably, Inflectra.ai connects to the customer’s Rapise API server over a secure connection, allowing direct integration with their testing environment. Inflectra.ai offers support for the entire Inflectra range of solutions, including Rapise and Spira. Today, supported models include the Amazon Nova and Anthropic Claude family of models.

Figure 3: Rapise with Inflectra.ai running on Amazon Bedrock and integrated with Rapise

Conclusion

By integrating Amazon Nova and Claude models through Inflectra.ai, Rapise can generate automated test scripts from manual test cases, autonomously test applications using visual models, self-heal brittle tests by adapting to UI changes, and flag usability and compliance issues automatically. For teams navigating complex compliance requirements or rapid development timelines, this means smarter QA, faster product releases, and higher confidence.

To get started with Rapise and Inflectra.ai, explore Inflectra in AWS Marketplace or contact an AWS Partner specialist to see how Rapise can support your modernization journey.

Inflectra – AWS Partner Spotlight

Inflectra is an AWS Advanced Technology Partner and AWS Competency Partner that provides comprehensive software delivery and quality assurance solutions for both cloud and on-premise deployments. Inflectra’s integrated portfolio of application lifecycle management (ALM), testing, and automated testing systems helps organizations speed up development while maintaining quality standards. Inflectra offers end-to-end traceability and agility in the software development lifecycle through seamless cloud deployment and extensive plugin capabilities.

Contact Inflectra | Partner Overview | AWS Marketplace

How AWS Partners can use generative AI to scale content enablement

Jonathan Bach — Mon, 06 Apr 2026 18:46:55 +0000

By Jonathan Bach, Global Content Lead AWS AI & Data Knowledge Center

In today’s digital landscape, organizations need effective ways to deliver timely, relevant knowledge to their teams and customers. Content enablement at scale creates valuable opportunities to expand reach and impact through efficient knowledge sharing—but managing and distributing content effectively requires the right tools and processes.

For AWS Partners, content enablement represents a key opportunity to accelerate business growth. Partners create and distribute content across multiple markets, supporting product launches, enabling sales teams, and delivering customer training resources. By automating workflows and streamlining processes, AI helps teams quickly localize content, create digestible summaries of complex material, improve content discoverability, and accelerate distribution.

Use cases

Generative AI turns operational friction into measurable gains. With shorter publishing cycles and streamlined processes, teams can quickly move from concept to delivery. Here are key examples organizations can easily adopt:

Video creation and localization: Create and localize content and demo videos rapidly, supporting product launches or customer engagement initiatives, e.g., converting a technical workshop into multiple languages. This can reduce production time from weeks to days.
Content optimization for rapid access: Use generative AI to accelerate preparation and tagging of assets, so internal and customer-facing teams find the right information faster.
Workflow automation with Amazon Bedrock: Deploy AI agents to automate repetitive publishing or content-update workflows, reducing manual effort.
Enhanced search and discovery: Implement AI-powered indexing can help field teams and stakeholders instantly surface relevant assets from large internal repositories.

When Partners implement these AI-powered capabilities, the business impact is both immediate and measurable. Results include faster enablement on product launches, consistent access to updated training, and reduced operational overhead.

The transformation is particularly evident in content production timelines—what once took weeks can now be accomplished in days. For example, converting a 20 slide deck into a localized 4-5 minute walkthrough can be accomplished within a day.

Beyond speed, the value of generative AI lies in enabling teams to innovate and differentiate. When AI manages routine, manual enablement tasks, teams can focus on building new solutions, deepening customer engagement, and driving measurable growth.

Figure 1 – Avatar being used for training material in AWS Partner Central.

Get started with AI-powered content solutions

To capture these strategic and operational benefits, AWS Partners have several paths to get started. Here are four ways Partners can implement these capabilities:

Leverage business automation sales plays. Access targeted sales plays in AWS Partner Central (login required) that guide the implementation of AI-driven efficiencies for enablement, marketing, and operations.
Explore AWS Marketplace solutions. Many third-party offerings provide click-to-buy automation, content generation, and translation tools that accelerate enablement.
Tap into the AI & Data Knowledge Center (formerly the Generative AI Center of Excellence). Access best practices, architecture guides, and success stories demonstrating how AWS AI services can power your enablement workflows.
Build your own AI-driven enablement engine. Use Amazon Bedrock and Amazon Nova to integrate foundation models into your workflows, including tasks like summarizing documentation, creating personalized training, generating visual content, and categorizing assets automatically.

Explore the AWS AI & Data Knowledge Center

As generative AI continues to evolve, organizations that embed these capabilities into their daily operations will be equipped to scale faster and deliver more consistent, localized, and high-impact enablement.

In the AWS AI & Data Knowledge Center, Partners have access to hundreds of enablement resources, training materials, and practical guides to help implement AI-powered content workflows and transform how teams create, manage, and distribute knowledge at scale. “The Knowledge Center is the engine that powers our customer conversations—we download, customize, and present AWS-approved content to win deals,” said Denise Willet, Global Alliance Head at LeapFrog Technology.

Explore additional success stories and download the AI Partner Playbook to accelerate your own enablement journey (login required).

Transforming Enterprise Payments with Stripe and SAP on AWS

Ganesh Suryanarayanan — Thu, 02 Apr 2026 15:50:00 +0000

Stripe

By: Danny Smith, Global Specialist Solution Architect – Stripe
By: Kalyani Koppisetti, Principal Solutions Architect – AWS
By: Ganesh Suryanarayanan, Principal Solutions Architect – AWS

Many enterprise organizations face challenges with legacy payment infrastructure despite having robust back-end systems for core business processes. This outdated payment architecture often results in missed revenue opportunities, operational inefficiencies, and barriers to market expansion. This post examines how integrating Stripe and SAP on AWS helps enterprises modernize their payment operations.

Understanding Enterprise Payment Challenges

Large enterprises commonly encounter several key payment processing challenges:

Manual reconciliation of payments across multiple systems and bank accounts
Complex regulatory requirements across different regions
Integration requirements between payment systems and existing SAP implementations
Growing customer demands for modern payment experiences

These challenges are particularly significant for organizations running SAP as their core business platform. While SAP effectively manages critical business functions from finance to supply chains, organizations increasingly need modern payment capabilities that integrate seamlessly with their existing SAP environment.

Figure 1: Integration architecture showing how Stripe, SAP, and AWS work together to transform enterprise payments

Combining Stripe, SAP, and AWS

The integration of Stripe’s financial infrastructure platform with SAP environments on AWS addresses the limitations of legacy payment systems. AWS provides the secure, scalable foundation that enables seamless connectivity between SAP’s business processes and Stripe’s modern payment capabilities.

AWS: The Foundation for Innovation

AWS serves as the comprehensive foundation that powers SAP workloads, Stripe’s payment infrastructure, and the integration capabilities that connect these solutions. Through its global infrastructure, AWS delivers the performance, security, and scalability required for both mission-critical SAP operations and Stripe’s payment processing worldwide. AWS’s strategic partnerships with both SAP and Stripe ensure continuous platform optimization and innovation.

SAP: The enterprise core

For enterprises operating at scale, SAP remains the backbone of critical business operations. With over 400,000 customers in more than 180 countries, SAP powers approximately 84% of global transactions. From finance to supply chain, manufacturing to customer experience, SAP solutions have been the trusted system of record for decades.

SAP’s largest deployment of SAP Business Technology Platform (SAP BTP) is on AWS, offering 80+ services to help customers innovate around their ERP core. From the first SAP deployments on AWS to today’s innovative offerings like RISE with SAP and SAP GROW, the strategic partnership has helped thousands of customers accelerate their digital transformation and unlock new business value.

Stripe: The financial infrastructure

While most people recognize Stripe as a payment processor powering online and in-person shopping experiences, enterprise leaders are discovering that Stripe is much more – it’s a complete financial infrastructure platform that transforms how businesses move money.

Stripe processes more than a trillion of dollars annually for millions of businesses worldwide, from startups to Fortune 500 enterprises. Their APIs and services not only process payments but provide tools for managing subscriptions and invoicing, preventing fraud, optimizing revenue, and more – all through a unified platform that scales with your business.

Additionally, Stripe provides real-time transaction visibility and reporting capabilities that help streamline operational efficiency. The platform’s architecture enables end-to-end payment tracking, from initial capture through to final payout, giving organizations clear insight into their payment operations. According to Stripe’s analysis, businesses implementing their payment solutions have experienced an average 11.9% increase in revenue through improved payment acceptance and optimization.

Powered by AWS, Stripe offers a 99.9999% uptime reliability, ensuring that businesses can provide frictionless commerce experiences that convert more customers and unlock new revenue streams.

Transformative Impact Across the Organization

The integration of these three powerful platforms delivers significant benefits throughout the enterprise:

For Executive Leadership

The integration of Stripe with SAP on AWS delivers strategic advantages that directly impact the bottom line:

Revenue Growth: Beyond the 11.9% average revenue uplift, expanding payment options and reducing checkout friction translates to improved conversion rates and customer satisfaction.
Operational Efficiency: End-to-end automation of bank reconciliation, from receivables, payables to your month-end report, let finance teams spend less time on manual tasks and more time on strategic initiatives.
Global Expansion: Instantly access local payment methods in 40+ markets without the complexity of establishing banking relationships in each region.

For Technology Leaders

For CTOs and enterprise architects, this integration addresses critical technical challenges:

Reduced Complexity: Integrate once with Stripe’s API and gain access to the entire financial ecosystem, eliminating the need for multiple payment integrations.
Improved Security: Offload payment security and compliance requirements to Stripe, reducing your compliance scope and risk exposure.
Scalability: The AWS foundation ensures performance at scale, even during peak transaction periods.
Innovation: Enable agentic workflows, automating signing up for subscriptions, optimizing payment acceptance, or proactively handling customer refunds.

For Finance Leaders

CFOs and finance teams gain immediate operational benefits:

Faster Month-End Close: Automated reconciliation reduces manual efforts and speeds up financial closing processes. ZoomInfo fully automated their B2B reconciliation process with Stripe + SAP, dramatically improving efficiency in their financial operations.
Improved Cash Flow: Faster payment processing and reduced payment failures improve working capital efficiency.
Enhanced Reporting: Real-time visibility into payment status across channels and geographies simplifies financial management.

Three Transformative Use Cases

We’ll explore three high-impact use cases that demonstrate the tangible value of integrating Stripe with SAP on AWS:

1. B2B Payments Made Easy with SAP Digital Payments & Stripe

B2B payments remain surprisingly manual and inefficient for many enterprises. Paper-based processes, complex approval workflows, and limited payment options create friction that impacts both buyer and supplier experiences.

In our first deep dive, we’ll explore how the SAP Digital Payments integration with Stripe eliminates these challenges by enabling secure, flexible B2B payment options that work seamlessly with existing SAP workflows. Imagine enabling your customers to pay invoices instantly with their preferred payment method, all automatically reconciled on your end. Faster revenue for your business, frictionless payments for your buyers, and no manual work for your operations teams

Take Splunk, for example. The company was facing challenges with excessive customization, increasing costs, and inefficient payment processes that created reconciliation discrepancies. By implementing Stripe with SAP Digital Payments, Splunk reduced custom development timelines by 75%, automated refund processing, and significantly improved operational efficiency across their customer service, finance, IT, and sales teams.

2. Enabling Modern Business Models

Many enterprises struggle to implement modern pricing models, finding their traditional systems unable to handle the complexity of combining usage-based, subscription, and conventional billing. The integration between Stripe and SAP solves this challenge by seamlessly connecting subscription management with enterprise financial systems.

This solution enables organizations to easily deploy sophisticated pricing strategies while maintaining financial control and visibility. Real-time usage tracking, automated billing, and streamlined reconciliation remove the technical barriers to business model innovation. Companies can now confidently launch new pricing models knowing they have an infrastructure that maintains enterprise-grade financial rigor while supporting rapid business evolution.

3. Unlocking Data-Driven Insights

Many enterprises today find themselves sitting on a goldmine of data, yet unable to fully leverage it due to disconnected systems. By unifying Stripe’s rich payment data with SAP business data on AWS, companies can finally bridge this gap. This integration creates a holistic view of the customer journey, from initial engagement to final payment and beyond.

With this unified data ecosystem, businesses can unlock powerful insights. Imagine predicting cash flow with unprecedented accuracy, or tailoring customer experiences based on comprehensive purchase history. AWS’s analytics capabilities, including Amazon Redshift and Amazon Bedrock, transform this data into actionable intelligence. This enables faster, smarter decision-making across the organization, from finance to marketing.

Looking Ahead

The integration of Stripe with SAP on AWS represents more than just a technical connection between systems. It’s about transforming how enterprises manage their financial operations and customer experiences.

Want to get started now? Contact your AWS representative or reach out to Stripe to learn how these solutions can work for your business.

Stripe – AWS Partner Spotlight

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Headquartered in San Francisco and Dublin, the company aims to increase the GDP of the internet.

Contact Stripe | Partner Overview | AWS Marketplace

Say Hello to 143 New AWS Competency, Service Delivery, Service Ready, and MSP Partners Added in March

Nick Paris — Wed, 01 Apr 2026 16:43:49 +0000

By Nick Paris, APN Marketing Manager – AWS Partner Network

The AWS Partner Network (APN) is a global community that leverages Amazon Web Services (AWS) technologies, programs, expertise, and tools to build solutions and provide services for customers.

NEW! AWS Competency Partners

To succeed with cloud adoption in today’s complex IT environment and continue to advance in the future, customers can team up with an AWS Competency Partner.

Team up with our newest AWS Competency Partners:

AWS AI Competency

AceCloud | APAC | Generative AI Consulting Services
Acroquest Technology | Japan | Generative AI Consulting Services
Adamas Tech Consulting | APAC | Agentic AI Consulting Services; Generative AI Consulting Services
APRÓ | EMEA | Generative AI Consulting Services
ATOS | NAMER | Agentic AI Consulting Services
Avnet | NAMER | Generative AI Consulting Services
Bexprt | EMEA | Generative AI Consulting Services
BigData Boutique | EMEA | Generative AI Consulting Services
Celebal Technologies Americas | NAMER | Generative AI Consulting Services
CitiusTech | NAMER | Generative AI Consulting Services
CleanSlate Technology Group | NAMER | Generative AI Consulting Services
CloudArmee | NAMER | Generative AI Consulting Services
CloudArmee | NAMER | Agentic AI Consulting Services
CloudJournee | APAC | Generative AI Consulting Services
CloudKeeper (a.k.a. TO THE NEW) | APAC | Generative AI Consulting Services
Cloudza | EMEA | Generative AI Consulting Services
Cloudza | EMEA | Agentic AI Consulting Services
CNID SAS | NAMER | Generative AI Consulting Services
Commerce Architects | NAMER | Generative AI Consulting Services
Commerce Architects | NAMER | Agentic AI Consulting Services
DBiz Solutions | APAC | Generative AI Consulting Services
Fusemachines | NAMER | Generative AI Consulting Services
GlobalTask | NAMER | Generative AI Consulting Services
Labyrinth Labs | EMEA | Generative AI Consulting Services
Morris & Opazo | LATAM | Agentic AI Consulting Services
Nagarro | EMEA | Generative AI Consulting Services
Necko Technologies | EMEA | Generative AI Consulting Services
Ness A.T | EMEA | Generative AI Consulting Services
O2B – Operation to Business | LATAM | Generative AI Consulting Services
Polarity | EMEA | Generative AI Consulting Services
Provectus | NAMER | Agentic AI Consulting Services
QloudX – A Systems Plus Group Company | EMEA | Generative AI Consulting Services
Quantiphi | NAMER | Agentic AI Consulting Services
Renova Cloud Company | China | Generative AI Consulting Services
RYSUN LABS | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
Sedmi Odjel | EMEA | Generative AI Consulting Services
Serverworks Co. | Japan | Generative AI Consulting Services
Smile IT Solutions | EMEA | Agentic AI Consulting Services
SoftGEM Global Technologies | EMEA | Generative AI Consulting Services
Sopra Steria | EMEA | Generative AI Consulting Services
Soulax | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
Strata Analytics Group | NAMER | Agentic AI Consulting Services
THINKSAAS TECHNOLOGIES | APAC | Generative AI Consulting Services
XalDigital | NAMER | Agentic AI Consulting Services
YAQEEN TECHNOLOGY CONSULTING | NAMER | Agentic AI Consulting Services
agenticai | APAC | Agentic AI Applications; Foundation Models and App Development; Generative AI Applications
Ajax Network Solutions | APAC | Generative AI Applications
Arize AI | NAMER | Agentic AI Applications
autobotAI | NAMER | Agentic AI Tools
Cloudera | NAMER | Generative AI Applications
CloudSmart | NAMER | Agentic AI Applications
Cockroach Labs | NAMER | Generative AI Applications
Firefly | NAMER | Generative AI Applications
Glia Technologies | NAMER | Generative AI Applications
Panther | NAMER | Agentic AI Applications
Pieces Technologies | NAMER | Generative AI Applications
Quantiphi | NAMER | Agentic AI Applications
rhino.ai | NAMER | Agentic AI Applications
SECURITI AI | NAMER | Agentic AI Tools
Vistring | China | Generative AI Applications
Workato | NAMER | Agentic AI Applications; Agentic AI Tools; Generative AI Applications

AWS Automotive Competency

PlaxidityX (Formerly Argus Cyber Security) | EMEA | Connected Mobility; Software Defined Vehicle

AWS Cloud Operations Competency

Applogika | NAMER | Cloud Governance; Monitoring & Observability
Cloutive Technology Solutions | EMEA | Monitoring & Observability
EPAM Systems | NAMER | Cloud Financial Management; Cloud Governance; Compliance & Auditing; Monitoring & Observability; Operations Management
MyOps | EMEA | Monitoring & Observability
Persistent Systems | NAMER | Cloud Financial Management
Rego Consulting Corporation | NAMER | Cloud Financial Management; Cloud Governance; Compliance & Auditing; Monitoring & Observability; Operations Management
Triangu | NAMER | Cloud Financial Management; Cloud Governance; Compliance & Auditing
Velocis Systems | APAC | Cloud Financial Management; Cloud Governance; Compliance & Auditing; Monitoring & Observability; Operations Management
Grafana Labs | NAMER | Monitoring & Observability

Consumer Goods Competency

Bloomreach | NAMER | Marketing; Unified Commerce

AWS Data & Analytics Competency

Redeploy AB | EMEA | Consulting Services
SCSK Corporation | Japan | Consulting Services

AWS DevOps Competency

Aivar Innovations | APAC | Consulting Services
F9 INFOTECH | EMEA | Consulting Services
Necko Technologies | EMEA | Consulting Services
Pace Wisdom Solutions | APAC | Consulting Services
PRECISION INFOMATIC M | APAC | Consulting Services
TechBrein | APAC | Consulting Services

AWS Digital Sovereignty Competency

Zühlke (Zuehlke/Zuhlke) Group | EMEA | Consulting Services

AWS Education Competency

EPAM Systems | NAMER | Consulting Services
Docebo | NAMER | Teaching and Learning

Energy & Utilities Competency

Datadog | NAMER | Data Analytics and Insights; Health, Safety & Environment; Upstream
Gorilla | EMEA | Power & Utilities
Snowflake | NAMER | Core Energy Business Applications; Data Analytics and Insights
TigerData | NAMER | Data Analytics and Insights

AWS Financial Competency

Kore.ai | NAMER | Banking; Capital Markets; General Financial Services
Resilient (Smartnumbers) | EMEA | Banking; Insurance

AWS Government Competency

KBR | NAMER | Citizen Services; Defense & National Security

AWS Government Competency

PA Consulting | EMEA | Defense & National Security
ScaleCapacity | NAMER | Citizen Services
Xtremax | APAC | Citizen Services

AWS Healthcare Competency

Dedalus | LATAM | Consulting Services

AWS IoT Competency

XalDigital | NAMER | Consulting Services

AWS Machine Learning Competency

Minfy Technologies | APAC | Consulting Services

AWS Manufacturing and Industrial Competency

Innovapptive | NAMER | Enterprise Solutions; Operational Technology; Smart Manufacturing; Supply Chain Management

AWS Migration and Modernization Competency

Flatiron Software Corporation | NAMER | Infrastructure Automation
Sensedia | LATAM | Application Mobility
Changeis | NAMER | Migration Services
eMaster | NAMER | Migration Services
INADEV Corporation | NAMER | Modernization Services
Initech Global | NAMER | Migration Services
Scott Logic | EMEA | Migration Services
Sonata Software | NAMER | Migration Services; Modernization Services

AWS Nonprofit Competency

Protagona | NAMER | Consulting Services

AWS Oracle Competency

IBM | NAMER | Consulting Services

AWS SAP Competency

Delaware Consulting International CV | EMEA | Consulting Services
skie.io | NAMER | Consulting Services

AWS Security Competency

Rego Consulting | NAMER | Compliance and Privacy; Infrastructure Protection; Threat Detection and Response

AWS Small and Medium Business (SMB) Competency

Ankercloud | EMEA | Small and Medium Business
CloudHero | EMEA | Small and Medium Business
Com7 | APAC | Small and Medium Business
F6S Advisors | EMEA | Small and Medium Business
Innovation Cloud Services – ICS Compute | APAC | Small and Medium Business
NCLOUD THREE INFORMATION TECHNOLOGY | EMEA | Small and Medium Business

AWS Storage Competency

Zaptoz Technologies | APAC | Consulting Services

AWS Travel and Hospitality Competency

XalDigital | NAMER | Data360; Digital Customer Engagement; Internet of Things; Technology Migration and Modernization
Riskified | NAMER | Digital Customer Engagement

NEW! AWS Managed Service Providers (MSPs)

The AWS Managed Service Provider (MSP) Program validates AWS Partners with a proven track record and experience providing end-to-end AWS solutions to customers at any stage of the cloud journey, including planning and design, building and migration, operations and support, and automation and optimization.

To achieve this level of specialization, AWS MSP Partners must complete an extensive independent audit to ensure business health and technical capabilities meet a high bar, and they must maintain this status through annual audits.

Say hello to our newest AWS MSP Partners:

AWS Managed Service Provider Program

2bcloud | EMEA
AppSquadz Software | APAC
Big Blue Marble (Insys Video Technologies) | EMEA
Bynet Data Communications | EMEA
Cloudelligent | NAMER
E&M Computing | EMEA
Geniusee | NAMER
Help.NGO | EMEA
Hostin Services | APAC
Idea 11 | APAC
Modality | EMEA
Mytech | NAMER
Ness A.T | EMEA
One | EMEA
Quadrasystems.net India | APAC

NEW! AWS Service Delivery Partners

The AWS Service Delivery Program validates AWS Partners that have deep technical knowledge, experience, and proven success in delivering specific AWS services to customers.

To achieve this specialization, AWS Partners must undergo service-specific technical validation by AWS Partner Solutions Architects, including review of architecture, customer documentation, and customer case study details to ensure they follow AWS best practices for each AWS service.

Work with our newest AWS Service Delivery Partners:

Amazon DynamoDB Delivery Partners

Allata | NAMER
Myridius | NAMER

Amazon ECS Delivery Partners

Cogniv Technologies | APAC | Containers
Myridius | NAMER | Containers

Amazon EKS Delivery Partners

Myridius | NAMER

Amazon Kinesis Delivery Partners

Insbuilt | LATAM

Amazon QuickSight Delivery Partners

YAQEEN TECHNOLOGY CONSULTING | NAMER

Amazon RDS Delivery Partners

Adamas Tech Consulting | APAC | Amazon RDS for MySQL; Amazon RDS for PostgreSQL
Romexsoft | EMEA | Amazon RDS for MariaDB; Amazon RDS for MySQL

More Value, Greater Profitability for AWS Partners

Learn how AWS is transforming the partner experience >>

Accelerate US Federal cloud migrations with Nutanix GC2 on AWS GovCloud (US)

Mike Corey — Mon, 30 Mar 2026 18:10:44 +0000

By: Mike Corey, WWPS Partner Solutions Architect – AWS
By: John Stockard, Public Sector Hybrid Multi-Cloud Systems Engineer – Nutanix

Nutanix

Technologists in the US federal government and Department of War (DoW) face unique challenges in delivering secure, compliant, and cost-efficient cloud infrastructure and services for their programs. These organizations encounter several obstacles. They must balance stringent security requirements with IT modernization needs. They face increasing costs from VMware licensing changes. Additionally, they must manage hardware replacement and reduce data center footprints.

Many federal customers face constraints with the mission-critical applications they support. These applications run on outdated, unsupported, or legacy operating systems. Such systems are difficult to migrate to Amazon Elastic Compute Cloud (Amazon EC2) or other AWS services. These organizations need a solution that bridges their current environment to the cloud while maintaining security and compliance requirements achieved on premises. At Amazon Web Services (AWS), we’re committed to helping our customers meet these challenges wherever they are in their cloud adoption journey.

Nutanix Government Cloud Clusters (GC2) on AWS GovCloud (US) provides a pathway, enabling Federal customers to accelerate their cloud adoption without compromising security or compliance. In this blog post, we’ll explore how GC2 helps Federal customers securely migrate their virtualized and containerized workloads to AWS GovCloud (US), with speed, simplicity, and efficiency.

Use cases for GC2 on AWS GovCloud (US)

Customers use GC2 to accelerate workload migrations to AWS GovCloud (US) and modernize applications faster and without compromise to compliance posture or regulation. GC2 enables the following common pathways to AWS GovCloud (US):

Migrate and modernize applications with AWS GovCloud (US) – Accelerate migration to AWS by relocating workloads without refactoring or rewriting applications. Quickly get on-premises workloads to AWS virtualization and modernize applications with direct AWS service integrations.
Hybrid cloud – Implement a single pane of glass to operate and manage your virtual machine workloads in your on-premises data center and GC2 on AWS GovCloud (US) environments.
Disaster recovery (DR) to AWS GovCloud (US) – Configure GC2 on AWS GovCloud (US) for data replication to ensure rapid recovery of business-critical workloads during disaster recovery events. Take advantage of the global presence and elasticity of AWS for your DR configuration through a scalable and always-on pilot light recovery environment.
On-demand capacity bursting – Traditional infrastructure requires weeks or months to purchase, install, and connect the necessary equipment and operationalize it on premises. With GC2, you can reduce hardware procurement lead time. It’s seamless to consume GC2 capacity when needed and return it when you don’t.

For more information, refer to Nutanix Use Cases.

GC2 on AWS GovCloud (US) architecture

GC2 deploys the complete Nutanix Cloud Platform directly on bare-metal EC2 instances, using EC2 instance store for primary Nutanix AOS Storage capacity. GC2 features built-in integration with Amazon Virtual Private Cloud (VPC). This integration allows direct consumption of cloud-based services from GC2 hosted workloads.

Federal customers can use familiar Nutanix tools across their hybrid environment, facilitating operational consistency between on-premises and AWS GovCloud (US) deployments. The inclusion of Nutanix Move facilitates smooth workload migration from existing infrastructure to GC2, minimizing operational disruption during transition periods.

The following diagram illustrates the Nutanix GC2 architecture, showing hybrid cloud integration with AWS services on AWS.

Figure 1: Nutanix GC2 architecture diagram

Benefits of GC2 on AWS GovCloud (US)

Nutanix GC2 on AWS GovCloud (US) offers enterprises a seamless hybrid cloud experience that bridges their on-premises Nutanix environment with the scalability and services of AWS.

The key benefits of the GC2 solution are:

Cluster management:

A single console for private and AWS resources
Burst into AWS to meet temporary increases in demand
Use AWS for high availability and disaster recovery
Simplified cluster deployment within customers’ VPC

Seamless application mobility:

Direct lift and shift without application refactoring
Consistent performance across environments
Simplified virtual machine (VM) migration using Nutanix Move
Maintain existing application configurations

Flexible cost control:

Usage-based payment options for EC2 instances
Multiple purchasing channels such as AWS Marketplace, Joint Warfighting Cloud Capability (JWCC), or partners
Use existing private pricing agreements and enterprise discounts
Nutanix license portability and compatibility between on-premises infrastructure and GC2

Supported EC2 instances

Whether an organization is running a small department operation or managing massive defense programs, customers are provided the flexibility and choice for rightsizing GC2 clusters to balance cost and performance. These instance types provide varying combinations of compute, memory, and storage capabilities to match your workload requirements. Organizations can choose the appropriate instance type based on their specific performance needs and budget constraints.

The following table lists the EC2 bare-metal instance types supported by Nutanix in the AWS GovCloud (US) Regions.

Figure 2: Amazon EC2 support for Nutanix on AWS GovCloud (US)

GC2 also supports the following AWS tenancy options:

Default tenancy:

Standard EC2 instance deployment
Instances might migrate across hosts during power cycles
Supports Windows Server License Included (LI) and Linux-only instances

Dedicated host:

Fixed hardware allocation of EC2 instance
Instances remain on assigned host through power cycles
Enables use of existing software licenses (per-socket, per-core, or per-VM)

Migration paths

There are a few different paths enterprises can take to migrate to GC2 on AWS GovCloud (US):

Nutanix Acropolis Hypervisor (AHV) to GC2 on AWS GovCloud (US) – Ideal for customers running the Nutanix AHV hypervisor on premises. Nutanix’s built-in replication capabilities enable VM replication from on-premises AHV to GC2. This maintains workload configurations and operational consistency on AWS GovCloud (US).
On-premises ESXi to GC2 – Organizations running VMware on premises can also migrate to GC2 using the Nutanix Move application mobility tool. Nutanix Move converts VMs directly into AHV format and replicates the workloads for a streamlined migration from on premises to GC2 on AWS GovCloud (US).
Microsoft technology to GC2 on AWS GovCloud (US) – Customers running Hyper-V or Microsoft Azure Cloud resources can migrate to GC2 by using Nutanix Move as well. The automated workload conversion maintains application consistency while consolidating workloads from multiple source locations.

Nutanix Move

For many enterprise customers, automated VM conversion and orchestrated migration are critical capabilities that make moving workloads to AWS far more straightforward.

Nutanix Move is a complimentary migration tool that streamlines workload transition to GC2 on AWS GovCloud (US). The tool automatically handles VM conversion from ESXi to AHV format while orchestrating the entire migration process from end to end. As a zero-cost solution, Move enables straightforward lift-and-shift operations, alleviating common migration barriers by automating conversion processes and coordinating data transfer. This enterprise-grade tool makes hybrid cloud adoption straightforward and efficient. Organizations can transition workloads seamlessly with minimal complexity and disruption.

Security and compliance

AWS GovCloud (US) provides the foundation and infrastructure controls that help organizations achieve and maintain critical compliance standards, including Federal Risk and Authorization Management Program (FedRAMP) High, Criminal Justice Information Services (CJIS), International Traffic in Arms Regulations (ITAR), and Department of War Impact Levels 4/5—all essential for protecting sensitive government workloads.

Nutanix GC2 enhances these capabilities by deploying directly to the customer’s Amazon VPC, minimizing the complexity and scrutiny of a separate software-as-a-service (SaaS) control plane. This direct integration allows for seamless security consistency between on-premises and cloud environments. GC2 is deployed preconfigured with Security Technical Implementation Guide (STIG) settings and aligns with NIST 800-53 standards. Combined with AWS GovCloud (US) support from US-based personnel, GC2 creates a comprehensive security framework that meets the demanding requirements of federal agencies. This means that organizations can embrace cloud scalability and innovation without compromising their security posture.

Most importantly, federal customers can use GC2 to confidently migrate their mission-critical workloads to AWS GovCloud (US) while maintaining the robust security controls and compliance standards established in their on-premises environments. This facilitates a more secure and compliance-aligned transition to the cloud without sacrificing operational security requirements.

Conclusion

Nutanix GC2 on AWS GovCloud (US) provides federal agencies with a path to accelerate their cloud adoption while meeting their mission requirements. The solution combines the familiarity of Nutanix operations with the innovation and scale of AWS GovCloud (US), so agencies can confidently migrate and modernize their mission-critical workloads.

Ready to get started?

To learn more about how GC2 can help your agency:

Contact your AWS or Nutanix account teams
Visit the Nutanix GC2 on AWS GovCloud (US) solution page
Download the GC2 solution brief

Nutanix – AWS Partner Spotlight

Nutanix is an AWS Technology Partner. Nutanix Enterprise Cloud OS software melds private, public and distributed cloud operating environments and provides a single point of control to manage IT infrastructure and applications at any scale. Nutanix solutions are 100% software-based, and are built on the industry’s most popular hyperconverged infrastructure (HCI) technology to power any application, at any scale.

Contact Nutanix | Partner Overview | AWS Marketplace

Expanding the BOX Program to Business Consulting and Advisory Partners

Amanda Lowe — Thu, 19 Mar 2026 20:39:05 +0000

By Amanda Lowe – WW BD Lead, Business Outcomes, AWS
By Dmitry Roma – Principal ASP Core Industries, AWS
By Duygu Cibik – Principal WW Strategic Initiatives Leader, AWS

At Amazon Web Services (AWS), we’re committed to helping customers achieve their desired business outcomes. To continue delivering on this commitment, we’re excited to expand the Business Outcomes Xcelerator (BOX) Program for Business Consulting and Advisory Partners—bringing specialized industry and line-of-business (LOB) expertise directly to customers to accelerate their business outcomes.

This expansion, which is invite-only, reflects a fundamental shift in how customers are approaching transformation. Line-of-business leaders are no longer seeking technology deployments alone; they’re demanding end-to-end solutions that deliver measurable, strategic business outcomes. Business Consulting and Advisory Partners (BCAPs) are uniquely positioned to meet this demand, and BOX now provides them with a dedicated journey and distinctive benefits designed specifically for how they operate and grow.

These Partners bring deep domain expertise in strategic advisory, operating model design, and organizational change management—the capabilities that drive lasting transformation. By expanding the BOX Program for BCAPs, we’re enabling multi-partner collaboration that positions business advisory expertise at the forefront of customer engagements. This creates a powerful end-to-end journey for our Partners—combining business consulting with AWS services and at-scale technical implementation, unlocking the full multiplier potential and delivering complete, business outcome-driven customer success.

Benefits

The BOX Program provides a unique, dedicated journey for BCAPs with distinctive benefits:

Funding designed to support customers across the full business transformation journey, including customer assessments to engage clients early in the transformation lifecycle, execution funding for process redesign, and outcome-based funding that addresses the people and organizational change required to achieve lasting business results. In addition, Partners gain access to all existing BOX milestone-based incentives spanning solution ideation, build, readiness, go-to-market, and delivery.
Go-to-market and co-sell support, which includes access to existing BOX mechanisms, such as Partner Matchmaking events and Partner Connections, to identify and collaborate with complementary Partners, as well as direct engagement pathways with AWS sales leaders. Partners also benefit from AWS-funded demand generation campaigns designed to help BCAPs reach and engage LOB buyer personas, driving awareness and pipeline for their transformation offerings.

Launch Partners

Partners are already helping customers bridge the gap between AI investment and business outcomes.

Bain & Company highlights that the biggest barrier to scaling AI isn’t technology—it’s the business and functional transformation required to unlock its value.

“Through the BOX Program, Bain and AWS combine complementary strengths: Bain’s expertise in strategy, tech and AI, operating model transformation, and change management with AWS’ world-class infrastructure and AI capabilities,” said Matthew Leybold, Partner at Bain & Company. “Together, we help organizations across multiple industries build AI-ready enterprises and realize lasting value from their AI investments. For example, partnering with AWS, we helped a major European bank unlock the value of its data and improve customer engagement. Using an Agentic AI workflow solution, the bank achieved a 98% reduction in campaign cycle time, cutting the time to launch personalized marketing campaigns from more than 60-100 days to just one day, while delivering 15% income growth and 2x uplift in cross-sell.”

Boston Consulting Group (BCG) demonstrates how aligning strategy with technology drives practical business outcomes, as evidenced by their work with a major APAC telecom.

“Transformation delivers the greatest value when strategy and technology move in sync,” said Stephen Robnett, Managing Director & Partner at BCG. “BCG and AWS bring together the expertise required for practical business change, combining BCG’s ability to create competitive advantage, BCG X’s technological expertise, and the AWS Cloud and AI capabilities that power businesses worldwide. Partnering with Meta and AWS, we built a GenAI Accelerator on Amazon Bedrock that empowers enterprises to rapidly deploy and scale generative AI use cases in just weeks. Leveraging this solution, an APAC telecom giant with more than 350 million users achieved a 10x increase in conversion rates and a 3x reduction in acquisition costs.”

Customers: Engage BCAPs early in your transformation

Organizations today aren’t struggling to invest in AI—they’re struggling to realize its value. The missing link isn’t technology; it’s the strategic expertise to connect AI capabilities to business priorities that drive revenue growth, cost optimization, and competitive differentiation.

The BOX Program expansion addresses this directly. Customers gain access to a structured, AWS-enabled pathway to engage Partners who specialize in delivering business transformation by working backwards from the business goals—not just deploying technology.

Partners who work backwards from business goals. BCAPs bring deep industry and line-of-business expertise, combined with technology design-and-build expertise, enabling them to engage directly with C-suite and LOB executives, translate business priorities into AI investment strategies, and quantify business value.
End-to-end transformation support. Customers are supported at every phase, from early-stage vision and feasibility, through solution design and execution, to business outcome realization.
Positive ROI on technology investments. BCAPs help customers redesign operating models and processes, ensure employee adoption, and transform the workforce to achieve measurable returns on their AI investments.

Get started

For customers wanting to engage BCAPs, find out more about what these Partners offer in AWS Marketplace or contact your Account Manager.

For BCAPs interested in joining the BOX Program, reach out to your PDM. For more information, review the benefits and requirements in the Program Guide (AWS Partner Central login required).

Modernizing Healthcare Workflows with WRITER AI on AWS

Gokhul Srinivasan — Tue, 17 Mar 2026 19:58:03 +0000

By Nolan Chen, Partner Solutions Architect – AWS
By Gokhul Srinivasan, Sr.Partner Solutions Architect – AWS
By Divya Srungaram, VP of Healthcare Solutions – WRITER

WRITER

The healthcare industry stands at a crossroads of complexity and opportunity. As organizations transition from traditional care delivery models to personalized medicine, they face new levels of operational complexity while managing stringent regulatory requirements and resource constraints. Healthcare providers, payers, and life sciences companies must balance operational efficiency with patient safety and regulatory compliance. Healthcare is expecting staffing shortages exceeding 200,000 nurses and 100,000 physicians by decade’s end.

Built specifically for regulated environments and powered by Amazon Web Services (AWS) infrastructure, WRITER delivers the transparency, control, and governance that healthcare teams need to implement AI at scale. In this post, we explore how WRITER, an AWS Partner Network member, transforms how healthcare organizations operate in this evolving landscape.

Addressing inefficiencies and patient care quality

Healthcare organizations today grapple with operational bottlenecks that directly impact patient care quality and organizational efficiency. Manual processes create significant workflow inefficiencies, from member letter creation that burdens clinical staff to request for proposal (RFP) responses that historically take weeks to complete. These administrative tasks consume multiple hours weekly for each physician, contributing to clinician burnout while reducing time available for direct patient interaction.

Healthcare regulations add another layer of complexity. Healthcare organizations must maintain compliance with multiple frameworks and various clinical documentation standards. Traditional automation approaches struggle to address these healthcare-specific considerations but stay flexible enough to adapt to evolving compliance requirements.

Healthcare organizations manage petabytes of clinical, imaging, and other data across disparate systems, creating integration challenges that impact care coordination and operational efficiency. Electronic health records, claims data, clinical documentation, and administrative systems often operate in isolation, limiting organizations’ ability to use comprehensive patient information for care optimization. This evolving landscape demands a new solution, one that understands these nuances and is built on a foundation of security and control.

WRITER differentiation

WRITER distinguishes itself in the healthcare AI market through its purpose-built approach to regulated environments. Unlike competitors that layer AI capabilities onto existing platforms, WRITER employs a vertically integrated architecture that provides unified system deployment with direct control over accuracy, reliability, and security.

Healthcare organizations require AI systems that support their compliance obligations while operating at scale. WRITER is a secure, enterprise-grade, end-to-end agent system, purpose-built from the ground up to operate in high-complexity, regulated environments. It’s designed and built for full transparency, control, and governance, helping give healthcare teams the confidence to deploy AI at scale.

Four years of experience working with national payers and major healthcare leaders allowed WRITER to understand healthcare workflows. This led them to build Palmyra Med, a domain-specific large language model (LLM) specialized in real-world medical use cases.

WRITER architecture on AWS

WRITER’s technical architecture uses AWS infrastructure to deliver enterprise-grade healthcare AI capabilities with robust security, scalability, and compliance features. The integration provides healthcare organizations with a platform that maintains data sovereignty while delivering advanced AI capabilities through managed services.

Palmyra Med is a domain-specific LLM trained specifically on real-world medical use cases. This 70-billion parameter model with a 32,000-token context window achieves 85.9% accuracy across medical benchmarks and outperforms other comparable models.

WRITER’s AWS architecture centers on Amazon Bedrock integration, providing healthcare organizations with access to Palmyra-Med-70B-32K through a fully managed API service. This integration enables seamless deployment of healthcare-specific AI capabilities without requiring organizations to manage underlying infrastructure or model hosting responsibilities.

Amazon Bedrock serves as the foundation for WRITER’s healthcare AI services, offering enterprise-grade security, privacy, and responsible AI safeguards. Healthcare data remains secure and private; inputs and outputs aren’t shared with model providers or used to train base models.

The following diagram illustrates the solution architecture.

Figure 1: WRITER Palmyra One Med Model Architecture on AWS

Full-stack architecture for healthcare workflows

WRITER employs a comprehensive full-stack architecture specifically designed to integrate AI into complex healthcare enterprise workflows. The system consists of three primary layers:

Agent system and intelligent interface – Serves as the primary interface where both technical and non-technical healthcare teams can build and deploy AI agents for clinical and administrative workflows. The system includes over 100 prebuilt agents developed with enterprise customers for specific healthcare workflows.
Intelligence layer and governance framework – Manages AI governance across healthcare organizations through comprehensive controls designed for regulated environments. This includes AI guardrails specifically configured for healthcare requirements, a compliance engine that addresses medical regulatory standards, and real-time monitoring of agent behavior and outputs.
Core technology layer – Powers the agent system and includes Palmyra, WRITER’s family of LLMs trained for healthcare-specific applications. This layer connects agents to healthcare business data, reducing inaccuracies.

The architecture uses AWS PrivateLink so healthcare organizations can maintain complete network isolation while accessing AI capabilities. Customers access Amazon Bedrock as if it were within their Amazon Virtual Private Cloud (Amazon VPC) connection, without requiring internet gateways, NAT devices, AWS VPN connections, or AWS Direct Connect.

Benefits and outcomes

Healthcare organizations implementing WRITER’s AI solutions report increased efficiencies and improved performance across several areas, including clinical outcomes, security and compliance, operations, and finance.

WRITER addresses healthcare’s stringent regulatory requirements through comprehensive compliance certifications including SOC2, General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), Payment Card Industry (PCI), and the International Organization for Standardization (ISO) trust triad standards. The offering keeps healthcare data within the AWS environment and doesn’t store it or use it for training when accessing through Amazon Bedrock from an Amazon VPC connection. To prevent hallucinations, WRITER uses a proprietary Knowledge Graph and domain-specific models:

Knowledge Graph – Anchors AI responses in an organization’s verified internal data such as clinical trial data and approved medical libraries, achieving over 86% accuracy on clinical benchmarks.
Traceability and auditability – AI-generated drafts are fully auditable, providing links back to the source evidence, making claims defensible.
Palmyra Med – The WRITER medical-specific LLM is trained in standards such as ICD-10-CM, SNOMED CT, and RxNorm to provide technical precision in coding and documentation.

Real-world implementations demonstrate WRITER’s transformative impact on healthcare operations. CirrusMD’s deployment achieved a 234% increase in physician benefits recommendations and a fifteen-fold improvement in patient benefits engagement, with implementation timelines reduced from 1 year of in-house development to 6 months using WRITER’s platform.

Healthcare organizations report up to 70% productivity increases in medical record summarization processes, while a top 5 health plan achieved 60% productivity gains and reduced legal compliance review from 6 weeks to 5 days.

WRITER embeds AI into core workflows that unlock new capacity and streamline operations:

Utilization management letters – Traditionally time-intensive and compliance-heavy processes are now generated with both clinical accuracy and human nuance.
RFP responses – Previously taking weeks, these are now completed faster while maintaining quality and regulatory alignment.
Clinical documentation – Automated generation of high-quality clinical summaries reduces physician administrative burden while improving documentation consistency.

Palmyra Med is priced at $10 per 1 million output tokens compared to GPT-4’s $60 per 1 million output tokens. This pricing structure means that healthcare organizations can deploy AI capabilities at scale without prohibitive operational costs. The service addresses healthcare organizations’ financial pressures through demonstrable efficiency improvements and cost reductions. Healthcare organizations’ productivity gains translate directly to cost savings through reduced processing time, improved staff utilization, and enhanced operational throughput.

By automating routine tasks, WRITER frees healthcare professionals to focus on high-value activities that require human judgment and clinical expertise, directly addressing the industry’s structural labor shortages.

Conclusion

As healthcare evolves toward personalized medicine and integrated care delivery, organizations need more than automation. They need intelligent systems that understand clinical nuances, administrative complexities, and regulatory demands. WRITER’s purpose-built approach to healthcare AI, combined with the robust infrastructure and security of AWS, creates a foundation for healthcare organizations to lead this transformation.

The documented results speak for themselves: 234% increases in physician engagement, 70% productivity improvements in clinical workflows, and 60% gains in administrative efficiency. These aren’t theoretical benefits—they’re measurable outcomes achieved by healthcare organizations already using WRITER’s capabilities on AWS.

The platform’s comprehensive compliance framework, specialized medical AI models, and proven implementation methodology address the unique challenges healthcare organizations face while enabling them to capture the opportunities of AI-driven care delivery.

The future of healthcare is being written today by organizations that embrace intelligent automation while maintaining the highest standards of patient care and regulatory compliance. WRITER on AWS provides the foundation for this transformation, offering healthcare organizations a proven path to operational excellence and improved patient outcomes.

Ready to transform your healthcare workflows with AI? With WRITER, AWS Partners have a powerful ally in bringing this future to life. Begin your journey with implementing AI in healthcare today by requesting a demo and learning more about WRITER’s Palmyra on Amazon Bedrock.

Start your healthcare AI transformation today and join the organizations already realizing the benefits of intelligent, compliant, and effective healthcare intelligence.

WRITER – AWS Partner Spotlight

WRITER is an AWS Advanced Technology Partner that provides a full-stack generative AI platform that empowers enterprises to deploy AI agents and workflows with built-in security, governance, and control across highly regulated industries.

Contact WRITER | Partner Overview | AWS Marketplace