The drumbeat is getting louder as tech journalists declare the downfall of seat-based pricing and the rise of consumption. Some say it’s the end of seat models altogether. Based on the conversations I’m having, that’s not true, but more producers are now asking how to implement a hybrid monetization strategy that incorporates consumption.

This shift comes in the wake of so-called “Claude Shock,” with AI agents capable of generating far more output than human users on the same subscription. As agentic usage scales, infrastructure costs climb while producers struggle to monetize the additional value gained by customers.

Usage-based pricing is a practical way to cover overheads and offer flexibility, but consumption isn’t a switch you can flip overnight.

Here’s my 10-step guide to launching a hybrid monetization strategy that safeguards recurring revenue, protects margins, and ensures long-term profitability while maintaining customer value.

1. Start with Predictability

For all the momentum behind consumption, subscriptions aren’t going anywhere fast. Annual recurring revenue still underpins how software businesses are built, forecast, and valued, which is why most companies aren’t abandoning seats altogether.

Tenant-level subscriptions with fixed seats or limits on concurrent users provide financial stability and align with how customers already buy. This gives your CFO confidence in revenue forecasting and avoids introducing volatility too early.

At this stage, the focus is on establishing a solid commercial foundation while customers adopt your product. Complexity can come later.

2. Study Usage Before Making Changes

On the face of it, usage-based monetization sounds simple: you charge for usage. But unless you know how your product is used, you won’t understand what usage really means.

A hybrid monetization strategy requires licensing-level telemetry that records:

• Concurrency peaks and average user numbers
• Session or workflow duration
• Feature or workflow adoption
• Storage consumption trends, etc.

Without these insights, pricing decisions become guesswork, leaving you vulnerable to margin erosion.

3. Introduce Credits as a Control Layer

Credits, tokens, units, whatever you call them, they’re the currency of consumption, and in a hybrid strategy, they operate as a layer on top of subscriptions.

Early on, their purpose is to:

• Track and model consumption
• Protect against high-cost usage spikes
• Normalize pricing across different types of usage
• Support peak demand bursts through on-demand scalability

Credit consumption should reflect real cost drivers, such as computationally intensive AI workflows consuming more tokens than lightweight processes. This ensures pricing remains defensible and cost-aligned, which is why many companies are choosing to sell tokens for AI capabilities while maintaining subscriptions for core product functionality.

Over time, credits can evolve from a tracking mechanism into a primary monetization lever where appropriate.

4. Think Beyond Individual Products

Credits shouldn’t be trapped inside a single application.

When you open the gates to your entire portfolio, pricing is simplified, adoption increases, and margins are protected regardless of where usage occurs.

This approach helps drive profitability without exposing customers to complexity.

5. Evolve Trials and Evaluations

Unlimited trials don’t hold up well when usage incurs significant underlying cost, especially when teams of AI agents can send consumption into overdrive.

Offering token-based trials allows you to see exactly where customers are spending their credits, which features and workflows matter, and where value is actually landing.

That’s a much stronger signal than whether someone logged in a few times, allowing you to have more informed conversations that can boost conversion rates.

6. Move to a Hybrid Monetization Model

This is where your hybrid monetization model takes shape, combining a predictable subscription foundation with targeted consumption-based pricing.

In practice, this usually takes one of two forms.

Scenario A: Subscription Features + Consumption for Advanced Capabilities

Customers subscribe to a core set of features for day-to-day operations. More advanced, high-value capabilities are monetized separately on a consumption basis.

This approach works well when advanced features create variable cost or deliver outsized value, such as AI functions, data processing, simulations, or premium analytics. Customers only pay for these capabilities when they use them, avoiding the need to majorly commit upfront.

Benefits of this hybrid monetization strategy:

• Finance retains predictable ARR from subscriptions
• High-cost or premium features are monetized in line with usage
• Customers gain flexibility without being locked into oversized plans

Scenario B: Subscription Features + Usage-Based Overages

Here, customers subscribe to a defined level of usage for the features they rely on every day. When usage exceeds those contracted limits, additional consumption charges apply.

This model is ideal where usage patterns vary over time, but customers still want cost certainty for normal operations. Consumption only activates when customers exceed agreed thresholds.

Benefits of this hybrid model:

• Base subscriptions provide revenue stability
• Overuse is monetized rather than absorbed as margin erosion
• Customers scale temporarily without renegotiating contracts

Why Hybrid Monetization is Better than Pure Consumption

Pure consumption strategies can introduce unpredictability that many enterprise buyers are not comfortable with, especially for mission‑critical systems. Few customers want their core platforms running on an open meter with no guardrails.

A hybrid monetization model avoids this issue by combining stability with flexibility. Subscriptions set clear expectations, while usage‑based elements allow for controlled expansion. This prevents bill shock, protects customer relationships, and aligns pricing more closely with real value delivered.

By blending subscriptions with either feature‑based consumption or overage billing, suppliers and customers both get predictable foundations with on‑demand scalability.

7. Drive Revenue with Data

Once consumption data is recorded, the insights can elevate your business, actively driving:

• Upsell conversations when customers approach limits
• Renewal defense by identifying churn risk early
• Proactive Customer Success engagement based on behavior

This is where a hybrid monetization strategy comes into its own, connecting product usage directly to revenue outcomes. To achieve this, notifications from your monetization analytics platform should feed directly into your Sales and Customer Success workflows.

8. Use Incentives, Not Discounts

Discounting may help close deals, but it can quickly erode pricing integrity and long-term profitability.

Instead of reducing headline subscription prices, use credits as a flexible incentive to:

• Support renewals
• Encourage adoption of higher tiers
• Incentivize exploration of advanced features

This approach preserves your pricing structure while delivering tangible value, fitting naturally within a hybrid monetization model.

9. Give Customers Control

As soon as consumption is introduced, governance becomes a priority.

Customers need budget caps, per-user or per-team limits, and clear ways to allocate spend, whether by department or geography. Without these guardrails, your hybrid monetization strategy can quickly break down.

Controls build trust, reduce friction, and ensure customers manage token consumption without chaos, so putting them in place is essential.

10. Keep Moving

If there’s a consistent theme across all of this, it’s that pricing is no longer static.

This is especially true for AI pricing models, which are constantly evolving as underlying costs fluctuate, usage patterns change, and customer expectations rise.

The companies adapting best are building systems designed to adjust over time, starting with predictability, layering in measurement, and gradually introducing consumption.

At its core, hybrid monetization is how you grow revenue while delivering flexibility and value to customers, creating a model that works for both sides.

Hybrid Monetization Strategy 101

Seat-based pricing won’t disappear overnight, but it’s no longer sufficient on its own. Consumption is rising quickly, driven by AI monetization, shifting cost structures, and changing customer expectations. The answer isn’t choosing one over the other. It’s combining them.

Start with predictable revenue. Measure everything. Introduce consumption in a controlled way, and evolve toward a hybrid monetization model that aligns price with value while protecting margins.

Tokens or credits play a key role in this shift, helping manage peak usage bursts where subscription limits are exceeded, giving customers the flexibility to scale without committing to permanently increased capacity.

The hybrid monetization shift is already underway. The differentiator now is how quickly companies can operationalize it, building the processes needed to keep pace.

If you’d like expert advice on how to implement your hybrid monetization strategy, please contact Revenera today.

Introduction to AI Tokens

Over the years, I’ve worked with both software-native companies and traditional hardware companies that have transitioned to software. Across industries, most are either already shipping AI-enabled capabilities or racing to add them, and many run into the same challenge: figuring out how to go to market (and how to adapt once they do) without their monetization strategy enforcement model becoming the bottleneck.

What that experience has taught me is this: if you’re offering AI-enabled capabilities (or planning to), you need a strategy that connects usage, value, and cost, before customers do it for you. Token-based licensing is one of the cleanest ways to do that. It gives customers flexibility while giving you a practical mechanism to price consumptive usage (especially when compute costs and adoption patterns can swing wildly). In this guide, I’ll share how I think about AI Tokens, where they fit, and the pitfalls to avoid when you move beyond static licenses.

Why You Need an Enterprise Strategy for Monetizing Usage

What I’ve learned working with teams through this shift is that the real value isn’t in finding the “perfect” pricing model, it’s building an enterprise strategy (and system) that lets you change your go-to-market motion without breaking quoting, provisioning, billing, or reporting. At a minimum, you need a reliable source of truth for what a customer bought and what they’re entitled to use, so you can monetize usage (and hybrid subscription + usage models) and reconcile charges with confidence. To do that well, you need flexibility and speed to:

• Change pricing and packaging in real time (flexible rate tables)
• Provision access immediately when a customer buys, upgrades, or consumes
• Capture granular usage events for analytics, billing, and reconciliation
• Integrate cleanly with your existing business systems (CRM, CPQ, billing, finance)
• The goal isn’t to change for change’s sake. It’s responsiveness: learn what customers value, understand what it costs you to deliver, and keep your packaging aligned as both evolve; especially in AI, where “usage” can become “cost” very quickly.

AI Tokens Explained

How the Consumptive Token Model Works

When I’m evaluating whether tokens for AI capabilities make sense, I look for a few core building blocks:

• Tokens as Currency: Customers buy tokens and use them to “pay” for what they need, when they need it.
• Rate Table: A configurable table that determines how much AI tokens cost per item or feature.
• Real-Time Adjustments: Producers can update pricing and packaging instantly by modifying the rate table.
• Usage Visibility: Every token transaction is tracked, providing detailed insights into customer behavior and product adoption.

Benefits for Software Producers and Buyers

For Software Producers

• Flexibility: Offer a wide range of products and features without complex licensing.
• Faster Time-to-Market: Launch new offerings and adjust pricing rapidly.
• Reduced Friction: Lower barriers to entry for small and mid-sized customers.
• Dynamic Pricing: Align pricing with value and usage patterns.
• Piracy and Overuse Control: Real-time tracking minimizes unauthorized usage.

For Buyers

• On-Demand Access: Use only what’s needed, when it’s needed.
• Scalability: Easily scale usage up or down without renegotiating contracts.
• Streamlined Procurement: Simplified purchasing and budgeting.
• Cost Optimization: Pay for actual usage, minimizing waste.

Common Monetization Strategies Using Tokens

In my experience, tokens are less about a single “pricing tactic” and more about giving customers a sane way to handle variability, variability in teams, projects, seasons, and now AI consumption. Below are a few common strategies I have seen work (and why).

Strategy 1: Portfolio Access (Expanding Customer Engagement)

With tokens, customers can access a broader range of products within a vendor’s portfolio. Instead of purchasing individual licenses for each product, they use tokens to “unlock” what they need. This approach encourages exploration, cross-sell, and upsell opportunities.

Use case: Simplifying Portfolio Access and SKU Management

Managing multiple products and SKUs can be complex and costly. With a token model, a software producer “tokenizes” the entire portfolio:

• All products are listed in the rate table with associated token costs.
• Customers purchase tokens and exchange them for any product or feature as needed.
• SKU management is simplified, and entitlement processes are streamlined.

Strategy 2: Peak Usage (Solving Capacity and Licensing Challenges)

Many organizations experience periodic spikes in demand, such as during project launches or onboarding new teams. Traditional licensing models can’t easily accommodate these fluctuations. Tokens provide a flexible “overflow” mechanism, allowing customers to purchase additional capacity only when needed.

Use case: Managing Periodic Capacity Overloads

A customer has a seat-based license for a product but occasionally needs extra capacity during peak periods for projects. Rather than purchasing additional annual licenses (which may go unused), they buy tokens to cover temporary spikes.

• The base subscription covers standard usage.
• Tokens provide flexible, on-demand access during high-demand periods.
• This reduces project delays, minimizes unused licenses, and aligns costs with actual needs.

Strategy 3: Monetizing AI Capabilities (Aligning Cost, Value, and Usage)

AI is where weak monetization strategy gets exposed. Many AI capabilities have real marginal cost (compute, model calls, GPUs) and unpredictable demand. If you price AI like a static feature bundle, you’re either going to scare customers away (overpricing), subsidize heavy users (underpricing), or spend cycles explaining overages. AI tokens can be a workable middle ground: customers get transparency and control, and you get a mechanism to keep value and cost in the same conversation.

Use case: Launching New AI-Driven Products with Consumptive Pricing

A software producer offers a core product (Product A) as a base subscription. They develop two new products (B and C), with Product C featuring advanced AI capabilities. Unsure of the optimal pricing and market fit, they use AI tokens to allow customers to try B and C.

• Customers receive free tokens to experiment with the new products.
• Usage data is collected to assess popularity, peak periods, and cloud costs.
• Over time, the producer adjusts the rate table, setting appropriate token costs as the AI pricing strategy is refined.
• This approach accelerates market feedback, supports dynamic pricing, and drives adoption, which is why producers are increasingly adopting tokens in AI strategies.

Overcoming Common Monetization Challenges

Managing Cloud and Compute Costs

AI and cloud-based features can drive up internal costs. Token models help producers:

• Track usage and align pricing with actual costs.
• Adjust pricing dynamically as costs change.
• Ensure profitability while delivering value.

Adapting Pricing to Usage and Customer Feedback

You’re also not going to get the pricing strategy right the first time, and that’s okay. The point is to learn quickly, then adjust without product rearchitecting. That’s why you need a system that can handle variance (rate changes, packaging changes, and entitlement changes) safely and fast. The dynamic nature of software markets demands agility. With tokens:

• Pricing and packaging can be updated in real time.
• Customer feedback and usage analytics inform decision-making for new models and quick responses to market shifts.

Ensuring Value Alignment and Customer Retention

Tokens create a direct link between value delivered and price paid. This transparency:

• Builds trust with customers.
• Encourages ongoing engagement and adoption.
• Supports long-term retention and growth.

Best Practices for Token-Based Pricing

When considering a token pricing strategy, you should treat consumptive tokens as an operating model, not a one-time pricing decision. The sections below focus on the practical mechanics – rate tables (and enforcement), real-time analytics, and hybrid packaging – so you can iterate quickly.

Setting Up the Rate Table

• Define clear token costs for each product, feature, or capability.
• Start with simple pricing and refine based on usage data.
• Define enforcement policy for token limits (hard → soft): choose whether to block at zero, throttle, allow overages with alerts/grace, or allow unlimited overages.
• Consider offering free or discounted tokens to encourage adoption.

Leveraging Real-Time Analytics for Pricing Decisions

• Monitor token transactions to identify popular features and peak usage periods.
• Use analytics to optimize pricing, packaging, and product development.
• Share insights with sales and customer success teams to drive engagement.

Combining Traditional and Token-Based Models

• Many producers use a hybrid monetization strategy, blending subscriptions with pay-as-you-go tokens.
• This provides stability for core offerings and flexibility for add-ons or premium features.
• Regularly review and adjust the mix to maximize revenue and customer satisfaction.

Conclusion

The future of software monetization is dynamic, flexible, and customer-centric. Consumptive tokens empower software producers to innovate, adapt, and grow in an ever-changing market. By aligning pricing with value, simplifying access, and leveraging real-time data, organizations can unlock new opportunities and drive sustainable success.

If you take one thing from this guide, I hope it’s this: consumption-based pricing works best when it’s part of a broader strategy – clear entitlements, defensible unit economics, and an honest story customers can understand. Get that right, and tokens become a tool for growth. Skip them, and tokens become yet another pricing experiment that creates friction instead of reducing it.

If you’d like further guidance on how AI tokens work and how to introduce consumption-based models to your portfolio, please book a call today.

While many device manufacturers have become highly effective at building digital capabilities, far fewer have mastered how to monetize them. Closing that gap is driving a new wave of digital transformation trends, as producers aim to turn software, data, and AI into measurable business value.

Chris Rommel, Executive VP, IoT & Industrial Technology at VDC Strategy recently joined Michael Goff, Principal, Product Marketing at Revenera to discuss this shift and the implications it creates.

Watch the recording and read a breakdown of key insights below.

From Connectivity to Continuous Value

According to VDC’s research, in 2023 only 33% of devices were designed to be IoT connected. Today, that figure stands at 67%. This rapid movement shows just how quickly digital transformation trends are reshaping the market.

What’s changed more fundamentally is the nature of products. Devices are no longer static assets that deliver value at the point of sale. They’ve become dynamic systems that continue to generate value over time through software updates, analytics, and data-driven services.

This shift changes the relationship between manufacturers and customers. Instead of a one-time transaction, it becomes an ongoing exchange of value. That opens the door to recurring revenue and new digital business models, but only if companies can align pricing with how products are actually used.

The Monetization Gap Widens with AI

AI is amplifying both the opportunity and the challenge. Adoption is moving quickly, with many organizations already experimenting or deploying AI-enabled features. However, monetization strategies are struggling to keep pace, which is a defining theme across current digital transformation trends.

A significant portion of companies are still figuring out how to charge for AI capabilities. Some are offering features without any associated pricing model, often because they’re unsure what customers will pay. Others are experimenting but haven’t landed on a consistent approach.

This creates a familiar pattern. It mirrors the early days of IoT monetization, when companies embedded software into products without a clear plan for profitability. The difference now is speed. AI is evolving faster, and the cost implications are more immediate.

Underlying infrastructure costs tied to AI workloads can scale quickly, so when AI pricing models don’t reflect usage, margins come under pressure. As consumption grows, revenue doesn’t always keep up. That’s forcing companies to rethink how they measure, price, and deliver value.

You Can’t Price What You Can’t Measure

At the center of modern monetization is software usage tracking. Without visibility into how products are used, pricing becomes guesswork. With it, companies gain the ability to align pricing with value. This shift is one of the most important digital transformation trends for monetization.

More manufacturers are investing in telemetry and analytics to track activation, feature usage, data consumption, and user engagement. That’s a major change from the past, when products often disappeared from view after shipment.

Usage data unlocks a range of opportunities. Companies can identify which features drive the most value, uncover upsell potential, and detect underutilization that may signal churn risk. It also supports more sophisticated pricing models that reflect actual consumption rather than assumptions.

Of course, collecting data is only part of the story. The real advantage comes from turning that data into actionable insight and connecting it to pricing and product roadmap decisions.

The Rise of Flexible Monetization Models

Traditional pricing is no longer enough for software-led devices. Fixed tiers and static packages struggle to keep up with the complexity of modern offerings, especially as AI and IoT continue to evolve. This shift is another clear signal in ongoing digital transformation trends.

In response, companies are exploring a broader mix of monetization models. Consumption-based pricing is gaining traction, alongside subscriptions and hybrid approaches that combine multiple elements. Outcome-based pricing is also emerging, with charges tied to value delivered.

This reflects a need for flexibility. Different customers use products in different ways, and pricing needs to adapt. A one-size-fits-all model limits both revenue potential and customer satisfaction.

This is especially true when it comes to monetizing software in devices, where usage patterns vary depending on deployment, scale, and customer needs.

Pricing Agility is a Competitive Advantage

The pace of innovation in AI and IoT isn’t slowing down. New features are rolling out quickly, and customer expectations are shifting just as fast. In this environment, pricing can’t stay static. This is one of the most critical digital transformation trends for device manufacturers to address.

Organizations need the ability to adjust pricing models as their offerings evolve. That doesn’t mean constant changes that confuse customers. It means having the flexibility to respond when needed, backed by data and clear communication.

Pricing agility depends on both technology and alignment across teams. Product, finance, and operations need to work together early in the process. Monetization shouldn’t be an afterthought.

Companies that treat pricing as a core capability are far better positioned to capture the full value of their innovations.

Infrastructure as the Hidden Constraint

One of the most overlooked barriers to monetization is infrastructure. Many organizations are trying to implement modern pricing strategies on systems designed for traditional hardware transactions. This mismatch continues to show up across digital transformation trends.

Legacy billing, entitlement, and licensing systems often can’t handle real-time usage data or dynamic pricing. As a result, companies rely on manual processes and workarounds that slow everything down.

The impact is significant. In some cases, it can take months to implement a pricing change. In a market where products evolve rapidly, that delay becomes a serious competitive disadvantage.

Companies with purpose-built software monetization systems have a clear edge. They can experiment with pricing, align revenue with value much faster, and accelerate time-to-market for new releases.

Balancing Innovation, Cost, and Profitability

As AI adoption grows, so does the need to balance innovation with profitability. Rising infrastructure costs, combined with uncertain pricing strategies, can quickly erode margins. Managing this balance is becoming a defining factor in today’s digital transformation trends.

Usage-based monetization helps align revenue with cost, but only if companies can measure usage accurately and act on it. Without that foundation, it’s easy to scale usage without scaling profit.

Connecting usage data to pricing decisions is becoming a key differentiator, giving organizations control over costs while unlocking the full revenue potential of AI-driven offerings and hybrid monetization strategies.

Digital Transformation Trends That Matter

The evolution of intelligent devices is creating huge opportunities. Software, data, and AI are transforming products into platforms that deliver ongoing value, but capturing that value requires a shift in mindset that sits at the heart of modern digital transformation trends.

Monetization needs to be treated as a strategic capability, supported by the right infrastructure and built into the product from the start.

The companies that succeed will be the ones that have the ability to measure usage, adapt pricing, and align business models with how customers actually use their products. This foundation will enable them to grow software-led recurring revenue and develop profitable AI monetization strategies at scale.

If you’d like expert advice on growing your software business and keeping up with the latest digital transformation trends, please contact Revenera today.

I’ve lost count of the number of times I’ve sat with product and engineering teams debating whether to build or buy software monetization systems.

The room starts optimistic. Someone sketches out a diagram on a whiteboard. A few APIs here, some licensing logic there, maybe a usage tracker bolted on. It all feels manageable. But as monetization models evolve and expectations change, the seemingly clean architecture turns into a tangle of workarounds.

From my perspective as a Solution Architect, the issue isn’t whether teams can build monetization systems. It’s whether they fully account for compounding complexity as they’re expected to drive growth in line with market trends.

This short video summarizes the key points you need to consider when deciding whether to build or buy software:

The Software Build or Buy Tipping Point

What makes this debate so fascinating right now is that the stakes have never been higher. Recurring revenue, consumption-based pricing, and AI-driven offerings are pushing companies to think carefully about how flexible and future-ready their systems need to be.

This is where custom software builds often struggle. In-house solutions are typically designed for a moment in time, optimized for a specific go-to-market motion. When strategies change, those systems can become a constraint rather than an enabler.

At the same time, the hesitation around buying a commercial platform is understandable. Teams fear losing control, even when roadmaps outpace what their tooling can realistically support. This tension fuels the build or buy software debate, and friction starts mounting as:

• Systems built around a single monetization model struggle to adapt
• Engineering becomes a bottleneck for pricing and packaging updates
• Product roadmaps start to bend around technical limitations

The issue of governance is also easy to overlook.

When software monetization technology is developed in silos, product teams often create their own approaches. One handles entitlements one way, while another tracks usage differently. Without clear, shared documentation, these differences are often understood only by the individuals who built them rather than being captured in consistent, repeatable processes.

What starts as autonomy can quickly turn into fragmentation. Over time, this lack of governance makes it harder to maintain consistency, visibility, and control across the business, while also being entirely dependent on individual expertise instead of well-documented, scalable frameworks.

The Cost Equation and the Bigger Picture

Cost discussions are often viewed too narrowly, with a focus on upfront investment rather than overall impact. However, the bigger effect plays out over time as the hidden costs of homegrown software stack up:

• Slower releases hinder your sales pipeline
• Ongoing maintenance pulls engineering away from innovation
• Limited analytics reduce the ability to uncover growth opportunities

I’ve seen how quickly simple requests snowball. A new pricing tier, more detailed reporting, greater packaging flexibility. All reasonable, but in a DIY system, each one can trigger weeks of rework, and what starts as a one-time investment quickly turns into a perpetual CapEx sinkhole.

Time spent maintaining the system is a distraction from driving innovation. And when the business needs to shift, the build or buy software conversation resurfaces, but now under pressure instead of from a place of control.

In contrast, commercial monetization platforms help you reduce technical debt with a more predictable OpEx model, where ongoing investment is aligned to value delivered rather than constant reconfigurations just to keep the lights on.

Read the Case Study: SimCorp Grows ARR with Capacity-Based Subscriptions

Control and Coordination

When monetization is treated as a shared capability rather than a set of product-specific implementations, teams gain consistency in how they package, price, and deliver software. They also benefit from centralized visibility into usage and entitlements, which makes it easier to adapt without reengineering multiple systems.

The benefits of a coordinated system include:

• Less duplicated effort across teams
• Centralized data improves visibility and decision-making
• Shared infrastructure supports faster, more consistent execution

Just as importantly, it improves the customer experience. Homegrown systems often result in clunky activation flows, limited or no self-service portals, and inconsistent entitlement management. These gaps don’t just create internal friction, they directly impact how customers interact with your product.

When you build everything yourself, you also take on all the responsibility that comes with it. Every update, every integration, every scaling challenge. Over time, that responsibility adds friction. It slows down how quickly you can respond to change, right when speed and adaptability matter most.

That’s the real tension in the build or buy software dilemma. It’s not just about what you can build, it’s about how quickly you can adapt without constantly reworking your foundation, allowing you to embrace new strategies like consumption licensing with relative ease.

Where Revenera Changes the Game

Many companies today aren’t operating in a single model. They’re managing SaaS, on-prem, and increasingly monetizing software in devices – often all at once. That’s a level of complexity most DIY systems weren’t designed to handle.

The challenges are rapidly increasing as organizations start experimenting with AI pricing models, where value isn’t always linear or predictable.

This is where Revenera’s monetization platform shifts the equation. Instead of forcing trade-offs, it gives you the flexibility and intelligence to move faster without losing control, with benefits including:

Hybrid Flexibility

Manage entitlements across on-prem, SaaS, and embedded environments while offering hybrid monetization strategies such as subscription-plus-consumption with superior flexibility.

Security and Compliance

Revenera is ISO 27001 certified, delivering enterprise-grade security, data protection, and compliance. Commercial platforms are designed to stay ahead, which is crucial as the EU Cyber Resilience Act and EU AI Act come into force.

Monetization Analytics

Go beyond basic reporting with built-in software usage tracking that shows how customers actually interact with your product. Use the insights to identify upsell opportunities, flag churn risk, and refine products based on real behavior.

Example dashboard from Revenera’s Monetization Analytics

A More Strategic Approach

A homegrown system might get you pieces of this over time, but rarely all of it, and almost never without significant effort. More often, critical capabilities like analytics and security get deprioritized in favor of getting something live.

This is where the build or buy software conversation evolves. It stops being about control for its own sake and starts being about what actually enables the business to grow.

That’s where purpose-built platforms create a clear advantage. According to Forrester’s Total Economic Impact™ Study of Revenera, customers achieved a 426% ROI over three years, fueled by faster delivery, reduced operational overhead, and stronger monetization insights.

The Build or Buy Software Decision

Most companies take a measured approach to build vs. buy software decisions. They realize building everything internally isn’t sustainable, but they still want flexibility where it matters. The goal becomes less about total control and more about strategic control.

The teams that get this right tend to think differently. They focus on building what their customers love, what truly differentiates them from competitors, and choose to buy what accelerates time-to-market for everything else.

Because in the end, the build or buy software decision isn’t a one-off choice. It’s an ongoing strategy. And the companies that treat it that way are the ones that move faster and avoid getting stuck with systems that can’t keep up.

If you’re currently debating whether to build or buy software monetization infrastructure, feel free to reach out to Revenera to see how we can help.

Device manufacturers are struggling to scale software revenue because monetizing software is fundamentally different from selling hardware.

As products become intelligent and software-defined, value creation is accelerating faster than monetization models can keep up.

Engineering teams are embedding AI, connectivity, and analytics into products designed to operate for 10 to 15 years in the field. Commercial models, however, often remain anchored to hardware-era assumptions. Features are built first. Pricing models are bolted on later.

The gap between digital innovation and monetization architecture is where structural friction accumulates.

Monetization is an Architectural Decision

When entitlement governance, pricing flexibility, and lifecycle strategy are retrofitted rather than designed in, companies accumulate monetization debt. These structural constraints limit agility, complicate compliance, and slow digital expansion across portfolios.

Manufacturers at the forefront of digital transformation trends treat monetization infrastructure as foundational, alongside connectivity, cybersecurity, and analytics.

The following six architectural decisions determine whether monetizing software becomes a competitive advantage or a structural bottleneck.

1. Define Software as a Strategic Asset

Before debating pricing models, leadership must determine the structural role software plays in the value proposition.

Is software a differentiator? A modular enhancement? Or a service platform that evolves independently of hardware?

Organizations that continue to bundle software implicitly with hardware often limit their ability to evolve packaging and pricing over time. Elevating software to a strategic asset requires alignment across product, engineering, finance, and sales.

Siemens Building Technologies recognized that increasing value was coming from digital services and formalized software as a revenue driver rather than an accessory. That shift required governance and cross-functional ownership, not just feature innovation.

For industrial OEMs, this transition is cultural as much as technical. Hardware organizations are optimized for physical SKUs and transactional revenue. Monetizing software demands lifecycle ownership, portfolio governance, and executive sponsorship, so the move from hardware to software monetization needs a coordinated shift in mindset.

2. Align Monetization with Customer Perception of Value

Industrial monetization succeeds or fails based on where value is positioned.

Recurring fees tied directly to device-level functionality often encounter resistance. Customers investing in capital equipment are cautious about incremental recurring charges layered onto hardware assets.

By contrast, monetization positioned at the enterprise or service layer, such as centralized visibility, predictive analytics, alarm filtering, performance optimization, and managed services, resonates more strongly. Customers are more willing to invest in measurable operational outcomes than in feature access alone.

Monetization architecture must therefore support layered value capture, enabling differentiation between embedded capabilities and enterprise-level digital services without engineering dependency.

3. Architect Pricing for Long-Lifecycle Product Markets

Intelligent device manufacturers operate under constraints that differ significantly from SaaS-native environments:

• 10-to-15-year product lifecycles
• Multi-generation installed bases
• Channel-driven distribution
• Hybrid CAPEX and OPEX procurement
• Regulatory oversight

Selecting a pricing model such as subscription, usage-based, tiered, or outcome-aligned pricing requires deliberate alignment with these structural realities.

Teradici’s transition from hardware sales to monetizing software via subscriptions illustrates the operational shift required. By implementing structured entitlement governance, the company enabled recurring revenue while supporting both on-premises and cloud deployments.

Organizations that automate entitlement and licensing infrastructure have reduced licensing enablement time for new product releases by up to 90%, converting administrative delay into measurable revenue acceleration.

In long-lifecycle markets, monetization maturity is operational as much as commercial. Without lifecycle-aware architecture, pricing flexibility erodes and portfolio complexity compounds over time.

4. Establish Monetization as an Enterprise Capability

When device manufacturers start monetizing software, many build licensing internally, embedding it within engineering teams. While this may address short-term requirements, it diverts focus from core product innovation and rarely scales across portfolios or acquisitions.

Compounding the challenge, organizations often lack a clearly defined licensing owner. Responsibilities are fragmented across engineering, IT, and sales operations. Without centralized accountability, entitlement strategy evolves reactively, resulting in manual workflows, inconsistent packaging logic, and delayed self-service initiatives.

Entitlement governance intersects commercial systems, compliance frameworks, lifecycle management, and customer experience. It is a specialized enterprise capability, not merely a firmware feature.

Treating monetization infrastructure as enterprise architecture rather than product code reduces technical debt and supports long-term scalability.

Industry analysis indicates that organizations can recapture approximately 4% of annual software revenue lost to noncompliant use by identifying that unlicensed use and converting it into new or additional revenue. In large portfolios, that percentage can represent tens of millions in unrealized or recoverable revenue. Monetization architecture is not theoretical risk mitigation. It is balance sheet protection.

5. Design for Governance, Compliance, and Self-Service

In regulated industries, feature activation is not simply a commercial action. It can impact validation workflows, audit trails, export controls, and cybersecurity posture.

Manual, file-based licensing models remain common in industrial environments. When provisioning, upgrades, and entitlement changes require support intervention, scalability becomes constrained by internal capacity rather than market demand.

Self-service entitlement provisioning enables customers to purchase, activate, and upgrade capabilities without manual order creation. This shifts monetization from operational friction to scalable digital execution.

A global medical equipment manufacturer launching AI-powered SaaS solutions across more than 200 markets faced this challenge. By leveraging enterprise-grade entitlement management solutions rather than building ad hoc systems internally, the company accelerated time to market while maintaining compliance and traceability.

Benchmarks from technology companies monetizing software indicate that implementing structured entitlement governance can achieve payback on infrastructure investment in less than six months due to operational efficiency gains and revenue acceleration.

Governance designed early prevents friction later.

6. Unlock AI-Driven, Outcome-Aligned Monetization

The next phase of industrial differentiation will increasingly be driven by embedded AI capabilities such as predictive maintenance, proactive alert filtering, dynamic performance optimization, and real-time operational adjustment.

Unlike traditional feature licensing, these capabilities generate measurable operational impact. For example, AI-driven optimization in energy-intensive systems can be monetized based on measurable savings rather than feature access. As energy efficiency improves, downtime decreases, and maintenance costs decline, monetization can align directly with realized outcomes.

When entitlement governance integrates with usage visibility, organizations gain the flexibility to introduce performance tiers, usage thresholds, and outcome-aligned AI pricing models without rewriting firmware or multiplying SKUs.

As AI becomes embedded across intelligent devices, monetization architecture must enable adaptive value capture across the product lifecycle.

Monetizing Software to Drive Growth

In many industrial organizations, thoughts about monetizing software only occur after digital products are built. Yet the ability to evolve pricing, activate features remotely, govern compliance, support self-service provisioning, and monetize installed bases often determines which manufacturers scale digital revenue successfully – and which remain constrained by legacy commercial models despite digital innovation.

The most effective strategies do not force digital business models onto hardware. They align pricing with perceived value, decouple entitlement governance from product code, design for regulatory complexity, and enable enterprise-layer monetization where ROI is clearest.

As differentiation shifts from mechanical performance to software intelligence, competitive advantage will not belong to those who build the most features. It will belong to those who architect how those features are commercialized.

Monetization is not a pricing exercise. It is strategic infrastructure.

If you’d like expert advice on monetizing software across your device portfolio, please contact Revenera.

SAP recently signaled a major shift in how it plans to charge for AI, moving away from subscription pricing in favor of usage-based monetization. The rationale is simple and hard to ignore: as AI automates work, the number of users no longer reflects the value delivered.

That message matters because SAP isn’t experimenting on the margins. When a company of that scale publicly rethinks pricing around consumption, it confirms what many software leaders already sense: usage-based monetization is moving from optional to inevitable.

But here’s the part that often gets overlooked. If you’re planning a move to consumption-based pricing, your first step isn’t pricing. It’s usage analytics.

Usage-Based Monetization Starts Long Before Pricing Changes

Across SaaS, enterprise software, and AI-driven products, teams often treat usage-based monetization as a commercial decision. They debate terminology, such as tokens versus credits, prepaid versus postpaid, or how to position hybrid pricing models.

Those conversations matter, but they are downstream.

Consumption-based licensing only works when organizations understand how their products are actually used. Without this foundation, pricing becomes guesswork, and guesswork leads to customer friction, revenue leakage, and internal mistrust.

Before finalizing your AI pricing strategy, you need clear answers to basic questions, including:

• What’s being used, and by whom?
• Which actions correlate with value, renewal, and expansion?
• What does normal usage look like across customers and segments?
• Where do usage spikes occur, and why?

Without this strategic insight, usage-based monetization fails to deliver value for both buyers and suppliers.

The Tree You Should be Planting Today

As the proverb goes: the best time to plant a tree was 20 years ago. The second-best time is today.

For software monetization, the modern version is straightforward:

If you think usage-based monetization is in your future, you should already be collecting usage analytics.

Even if your pricing model isn’t changing this year, the data you collect now becomes the baseline for future decisions. It gives product teams evidence to define value metrics. It gives finance teams confidence in forecasting. It gives customers transparency when consumption becomes part of the commercial relationship.

Delaying the implementation of software usage tracking until pricing decisions are made effectively means you’re pricing blind.

AI Makes Usage Analytics Non-Negotiable

AI doesn’t just increase usage. It changes usage patterns entirely.

AI workloads can scale rapidly and unpredictably, often driven by automation rather than human interaction. That volatility is why AI monetization conversations focus so heavily on AI tokens, capacity allocation, and consumption limits.

But none of those mechanisms work without accurate, real-time usage visibility, measured consistently, transparently, and in a way customers can trust.

You cannot monetize what you cannot measure.

Billing isn’t the Hard Part

Many usage-based monetization initiatives focus too narrowly on billing. Billing answers one question: how much was used?

Modern software monetization must also answer a more important one: what is the customer allowed to do right now?

That real-time entitlement decision is where consumption-based licensing either builds confidence or erodes it. Customers expect clarity. They want to see usage as it happens, understand limits, and avoid surprise costs. Internally, finance teams need auditable data, and product teams need the freedom to evolve packaging without reengineering applications.

This is why entitlement management and usage analytics must be designed together. Usage-based monetization isn’t just about invoices. It’s about governing access, consumption, and value delivery in real time.

Your Path to Usage-Based Monetization

For technology companies preparing for usage-based AI licensing, the most effective approach is incremental and evidence-driven.

1. Establish a usage truth layer. Start collecting granular, accurate usage analytics now, even if pricing isn’t changing yet.
2. Connect usage to entitlements. Define what customers are entitled to consume and under what conditions.
3. Introduce consumption mechanics with guardrails that protect both customers and revenue.
4. Iterate pricing and packaging based on real-world usage patterns, not assumptions.

This sequence allows teams to move toward usage-based monetization with confidence instead of urgency-driven compromises.

Alignment with Revenera

Revenera’s approach to software monetization is grounded in the understanding that usage-based pricing succeeds only when measurement, entitlement management, and governance work together.

Revenera enables real-time, auditable usage capture that supports consumption-based licensing models. Pricing and packaging can be adjusted through configurable rate tables without disrupting applications or customer workflows. Usage insights are designed to support both operational decision-making and customer transparency.

This foundation allows organizations to introduce usage-based monetization deliberately, adapt as markets evolve, and avoid the trust issues that arise when monetization outpaces measurement.

Download Your Free eBook: AI Monetization, Done Right

The Big Takeaway

SAP’s move toward usage-based monetization isn’t just news. It’s a signal.

If introducing AI tokens and consumption-based pricing is part of your roadmap, the most strategic move you can make today isn’t to finalize a pricing model.

It’s to start collecting the usage analytics you’ll need to price, package, and govern consumption effectively.

The best time to plant that tree was years ago. The second-best time is now.

If you’d like to discuss how to implement usage-based monetization, please contact us and a member of our team will be happy to talk.

Software usage tracking used to be fairly straightforward. You collected data, generated reports, and reviewed them periodically to understand how your software was being used.

That approach no longer holds up.

As technology companies move toward SaaS, hybrid, and usage-based monetization models, usage data has become central to how value is delivered, measured, and renewed. The question isn’t whether you’re tracking usage, it’s whether that data is giving you a clear, shared view of what’s actually happening across your customer base. Detailed software usage tracking should reveal engagement levels, feature adoption, service denials, and upcoming renewals – allowing you to identify churn risk and potential expansion opportunities.

Recent updates to the Monetization Analytics Dashboard in FlexNet Operations reflect this shift, focusing on making usage insight easier to access, interpret, and share across teams.

Key Benefits of Software Usage Tracking and Reporting

1. Clearer insight into customer value: In modern software businesses, revenue doesn’t stop at the point of sale. Renewals, expansion, and long‑term growth all depend on how customers adopt and use what they’ve purchased. Software usage tracking helps teams see whether customers are getting real value, rather than relying on assumptions or surface‑level signals.
2. Earlier visibility into risk and opportunity: Usage patterns often tell a story long before renewal conversations begin. Strong adoption can point to expansion potential; while declining or uneven usage can highlight risk early enough to act. With the right reporting in place, teams can move from reactive conversations to proactive engagement.
3. More confident, data‑driven decisions: When usage data is clear and accessible, decisions become grounded in evidence rather than anecdotes. Product, revenue, and customer success teams can align around the same understanding of what’s working, what isn’t, and where to focus next.

How to Track Software Usage More Effectively

The real challenge isn’t collecting data; it’s making it usable.

Usage insight is often fragmented across systems and reports, with each team seeing only part of the picture. Product teams might focus on feature adoption, finance teams on entitlements and revenue, and customer success teams on engagement signals. When those views aren’t connected, teams are left piecing things together manually, often without the clarity or time needed to act.

In this kind of environment, the data quickly loses impact. Static reports and siloed insights make it difficult to answer even basic questions about how customers are using what they’ve purchased. What’s needed isn’t more tracking, it’s shared visibility that teams can trust and interpret in their own context.

Modern, flexible software usage tracking helps close that gap. Not by giving everyone the same dashboard, but by creating a shared operational view of entitlements, usage, and adoption that different teams can explore the same underlying data in ways that align with their role and priorities. Customizable views and interactive experiences make it possible for teams to work from a single source of truth, while still focusing on the metrics and KPIs that matter most to them.

Renewal risk becomes visible earlier because declining adoption or unmet entitlements stand out in real time. Expansion opportunities are easier to identify because usage patterns clearly show where customers are getting value, and where there’s room to grow. Product decisions become more grounded, guided by how software is actually being used rather than assumptions or anecdotal feedback.

Software Usage Reports That Go Deeper

Revenera’s Analytics Dashboard offers several smart software usage tracking reports, including:

• Service Denials: When customers are denied access due to insufficient capacity or use rights, this is a clear signal of an upsell or cross-sell opportunity – especially if it’s repeated behavior over a prolonged period.
• License Server Usage: Drill into specific feature usage to see whether key customers are making the most of your product, or if action is needed to boost adoption. This data can also inform overage billing scenarios.
• Fulfillment: By comparing available quantities to activations, you can see if customers are maxed out and could benefit from more, or if you need to intervene to reduce churn – either by driving adoption or recommending right-sizing, which improves satisfaction and lowers cancellation risk.
• Renewals: A real-time view of upcoming renewals helps your team stay ahead. You can also conduct customer churn data analysis by filtering churn by region, allowing you to audit expired entitlements and assess your options for re-engagement campaigns.

Each report supports interactive drilldowns and filtering by key attributes, such as Product and Account, making it easy for teams to derive insights relevant to their specific needs. These reports become even more powerful when paired with conditional notifications. Instead of reviewing trends after the fact, teams can define rules, such as repeated service denials over time or declining usage ahead of renewal, and receive alerts as soon as those conditions are met, enabling faster, more targeted follow-up.

Ready to Get Started?

As analytics capabilities continue to evolve, focusing on visibility and accessibility lays the groundwork for deeper insights in the future. More advanced analysis, predictive signals, and intelligent decision support all depend on having a solid foundation in place first.

The latest Analytics Dashboard update is built with this reality in mind. A more modern, interactive experience makes it easier to explore entitlements, usage, activation, and renewal data in one place, with customizable views that reflect different products, customers, and business priorities. This helps teams work from a shared, real‑time view of adoption and value, rather than relying on static reports that quickly fall out of sync in hybrid monetization models.

If you’d like more information on how Revenera’s software usage tracking capabilities can help you grow recurring revenue with insights delivered to your inbox, let’s talk.

Software monetization is at an inflection point, as the challenge of AI licensing creates new layers of risk, uncertainty, and opportunity.

Traditional monetization strategies are struggling to keep pace with the demands of scaling AI-powered solutions. Costs are unpredictable, usage patterns are dynamic, and customers expect flexibility without surprises.

In this environment of rapid experimentation, producers face a critical question: How do you capture economic value while continuing to drive innovation?

The Economic Value Principle

At its core, AI licensing should ensure fair value for customers and sustainable profit for producers.

Historically, perpetual software licenses shifted most of the financial risk to the customer. Buyers paid upfront and then assumed responsibility for deployment, integration, maintenance and long-term infrastructure investment, regardless of whether the application ultimately delivered the expected business value.

Once the license was sold, the producer’s revenue was largely fixed. If the customer achieved exceptional outcomes, the producer did not participate in that upside. If the customer underutilized the software, the financial burden remained with them.

Subscription models improved this dynamic by lowering upfront commitments and spreading payments over time. However, they still rely on relatively stable cost structures. When underlying costs fluctuate significantly – as they do with AI workloads – fixed subscription pricing can create margin pressure for producers or misalignment with customer value.

Why Subscription Alone Isn’t Enough

AI breaks the assumption of delivery cost stability. Compute and storage requirements can spike unpredictably, so fixed subscription pricing (even if recurring) can erode margins.

Subscriptions also don’t serve customers well when usage varies significantly over time. Some customers overpay relative to value received, while others generate costs that outpace the subscription price. Neither scenario is sustainable.

AI is changing the economics of software. When customer usage directly impacts producer costs, AI pricing models must evolve accordingly.

AI Licensing: Aligning Price with Value

To address this shift, leading producers are moving toward hybrid AI licensing models that combine subscription foundations with consumption-based pricing.

This approach achieves four critical AI licensing goals:

• Fairness and Transparency: Customers pay for what they use, aligning cost with delivered value.
• Margin Protection: Producers recover variable costs without sacrificing profitability.
• Revenue Predictability: Subscription protects base ARR (Annual Recurring Revenue) while consumption captures upside.
• Flexibility for Innovation: Customers can adopt new AI capabilities without large upfront commitments.

In practice, this often means monetizing AI with prepaid/postpaid consumption layered onto a subscription base.

This approach also opens the door to premium pricing. When producers can tie pricing to measurable outcomes, they create economic value that customers are willing to pay for.

Revenera’s Role in Enabling Value Capture

Revenera helps technology companies implement hybrid AI pricing strategies through a combination of:

• Flexible Monetization Models: Node-locked, floating, metered, and token-based models tailored for AI and SaaS, with seamless hybrid combinations.
• Advanced Analytics for Value Reporting: Producers can measure usage and outcomes, enabling data-driven pricing decisions.
• Dynamic Monetization: Modern platforms designed for connected and regulated environments, supporting elastic access, surge pricing, and diverse deployment scenarios.

See how Revenera’s Dynamic Monetization enables flexible, usage-based AI licensing models in this short video:

The Road Ahead

The future of AI licensing agreements will be defined by strategies that align price with measurable value.

The industry is moving toward hybrid models that layer consumption or outcome-based pricing on top of traditional subscriptions. This evolution isn’t just about covering costs. It’s about capturing the full economic value of innovation.

Producers who embrace this shift will not only protect margins but also strengthen customer relationships by aligning price with measurable success.

Read Now:
AI Monetization, Done Right: A Practical Guide for Product and Pricing Leaders

Smarter AI Licensing Models

If you’re a software producer navigating the complexities of AI monetization, start by asking:

• How much risk are you removing for your customers?
• Can you measure and communicate the value you deliver?
• Are your monetization models flexible enough to adapt to unpredictable costs and dynamic usage?

Those who answer and act on these questions will lead the next era of software monetization and set the blueprint for AI business strategy.

If you’d like more information on how Revenera’s monetization platform enables consumption-based AI licensing models, please contact us today. 

Engineering teams don’t need convincing that billing is hard.

Recently, I read an article from Lago Billing – “Why Billing Systems Are a Nightmare for Engineers”, which clearly explains that what starts as “simple subscription billing” quickly turns into a maze of proration rules, usage calculations, retries, refunds, tax compliance, edge cases, and race conditions. Billing systems live at the intersection of money, contracts, and time, and that alone is enough to keep teams busy for years.

Yet many companies make software billing even harder by asking it to do more than it was ever designed to do.

One of the most common and costly mistakes?
Embedding entitlement management inside the billing system.

Software Billing and Entitlements Solve Different Problems

Let’s be very clear: billing and entitlement management are not the same thing.

Billing answers one question:

How much does the customer owe us, and how do we collect it?

Billing systems are responsible for:

• Pricing calculations
• Invoices and payments
• Subscriptions and renewals
• Usage aggregation for charges
• Taxes, credits, refunds, and revenue recognition

This domain is already complex and highly regulated. Any added responsibility increases risk and slows engineering velocity.

Entitlement management answers a very different question:

What this customer is allowed to use, and how they are actually using it?

Entitlement management focuses on:

• Who can access which products, features, or bundles
• Usage rights across SaaS, on-prem, embedded, and hybrid deployments
• License enforcement and fulfillment
• Tracking real usage against purchased rights
• Providing insight into adoption, expansion, and compliance

Trying to force this logic into a billing system is like asking your payment gateway to manage feature flags and access control. It’s the wrong abstraction, and this is evident over time.

Why Mixing Entitlements into Software Billing Systems Makes Everything Worse

“Billing answers who pays and how.
Entitlements answer who can do what. Mixing them is like asking the cashier to run security.”

When entitlement logic is buried inside billing systems, teams start to experience:

• Tightly coupled code paths where a pricing change can break access control
• Slower releases because billing changes are high-risk
• Poor visibility into real product usage
• Workarounds and spreadsheets to track who is entitled to what
• Billing systems turning into pseudo-licensing engines, which they were never designed to be

The result is not just technical debt, it’s monetization debt. Teams lose the ability to evolve pricing, packaging, and product strategy quickly.

How Pricing, Software Billing, and Entitlements Should Work Together

The right approach is not “either billing or entitlements.”
It’s a clear separation with strong integration.

A healthy model looks like this:

1. Pricing lives in billing
   • Plans, tiers, usage rates, discounts, invoices
2. Entitlements live in an entitlement management system
   • Access rights, feature limits, usage rights, and enforcement
3. Usage data flows from entitlements to billing
   • Accurate, auditable usage signals not raw product noise
4. Analytics inform business decisions
   • Renewals, upsells, churn risk, and packaging strategy

Each system does what it does best, and together they enable modern monetization.

Why Entitlement Management Deserves Its Own System

Modern products are no longer simple. They have various complex requirements to support.

• SaaS + on-prem hybrid deployments
• Usage-based and outcome-based; hybrid pricing
• Feature-level packaging
• Device-based, user-based, and capacity-based models
• Customer self-service expectations, smooth integrations, and activations

Entitlement management becomes the source of truth for customer rights and product usage. It gives product, engineering, sales, and finance teams a shared understanding of:

• What was sold
• What was delivered
• What is actually being used

Without this clarity, monetization becomes guesswork.

How Revenera Gets This Right

Revenera’s entitlement management platform (FlexNet Operations) is built around one core idea:
Don’t overload software billing systems, empower monetization through entitlements.

What makes it effective:

• Designed for SaaS, IDM, and hybrid products
  Supports subscriptions, perpetual licenses, usage-based models, and everything in between.
• Centralized entitlement and usage visibility
  A single source of truth across cloud, on-prem, and embedded environments.
• Usage intelligence, not just enforcement
  Monetization analytics help teams identify adoption gaps, expansion opportunities, and churn risk.
• Clean integration with billing and CRM
  Entitlements inform billing; they don’t compete with it.
• Scales with business complexity
  As pricing models evolve, entitlement logic doesn’t become a bottleneck.

This separation allows companies to move faster, price smarter, and monetize more effectively without turning billing into an unmaintainable monolith.

Final Thought: Complexity Is Inevitable, Confusion Is Optional

Software billing will always be complex.
AI and outcome-based pricing will always add a new nuance.
Hybrid monetization will always evolve.

But confusion comes from poor boundaries, not from complexity itself.

By keeping billing and entitlement management separate and letting each system excel at its purpose, you don’t just reduce engineering pain, you unlock better monetization.

Artificial intelligence is moving at breakneck pace, and software leaders are racing to embed new capabilities into their products. But as innovation accelerates, monetization strategies often lag behind, leading to unpredictable costs, squeezed margins, and value-driven revenue left on the table.

To keep up, your AI business strategy must prioritize:

• Robust entitlement management
• Allocation controls
• Hybrid monetization models

These foundations are non-negotiable, allowing you to scale AI-driven offerings without exposing your company to unnecessary risk.

The Monetization Gap

When monetization is an afterthought to innovation, ROI and long-term growth are undermined, leading to increased C-suite scrutiny of AI initiatives.

Consequences of this misaligned approach include:

• AI features are launched before AI pricing models are defined.
• Revenue forecasts become unreliable as usage grows without controls.
• Subscription models tied to users or seats don’t scale when “agents” or automated processes replace human users.
• Usage-based billing can lead to runaway customer costs if not managed.
• Customers want to pay for value but fear unpredictable bill shock.

How these issues are addressed will shape the success of your AI business strategy. The impact is already evident for many organizations, with 70% of Monetization Monitor respondents saying the cost of delivering AI functionality is destabilizing profitability.

Respondents describe these pressures in their own words:

“We face challenges aligning AI costs with revenue, managing complex pricing, and unifying usage analytics across multiple systems efficiently.” – Engineering/Development Executive, Device/Hardware company with $250m+ in annual revenue

“Key challenges include effectively pricing AI-powered features, optimizing subscription models, and leveraging data for revenue generation while maintaining user satisfaction.” – Operations Director, SaaS/Cloud company with $25m-$100m in annual revenue

“We don’t [currently price our AI offering]. It’s bundled in the core product price as clients expect to have basic capabilities just like reporting.” – Director of Strategy, SaaS/Cloud company with $250m+ in annual revenue

Why Billing Isn’t Enough

Billing is about collecting money; monetization is about aligning pricing with value, usage, and outcomes. Without flexible models and controls, even the best AI features can become a liability.

During her recent AI Monetization Unlocked webinar, IDC’s Tiffany McCormick discussed the confusion around billing for AI usage in the context of hybrid monetization models, stating:

“When vendors are considering these different pricing models, they’re leaning onto their billing companies, not realizing they actually have an entitlement problem. At the point where you decide the threshold of where subscription ends and usage starts, that’s not a billing problem. It’s an entitlement problem.” – Tiffany McCormick, Research Director, AI Monetization, Pricing Strategies and Business Models, IDC

In hybrid subscription and usage-based models, the real challenge isn’t billing – it’s defining and managing who has access to what, when, and under which conditions. Without a centralized entitlement management system, usage data is fragmented across siloed systems, making it difficult to accurately measure consumption, establish meaningful usage baselines, or understand how subscription and usage interact.

Centralizing entitlements creates a single source of truth, enabling reliable software usage tracking, clearer thresholds between subscription and consumption, and a more informed AI pricing strategy over time.

What Your AI Business Strategy Needs

As your AI business strategy moves from experimentation to monetization, it’s essential to build these commercial foundations:

• Hybrid pricing models: Combine subscription, consumption, and outcome-based approaches to match how customers use and benefit from AI.
• Self-service budget controls: Empower customers to set limits, allocate spend by team or product, and avoid surprises.
• Flexible allocation: Support prepaid, postpaid, and mixed models, with the ability to allocate by team, product, or environment.
• Value reporting: Show customers not just what they used, but what they achieved, helping justify spend and build trust.
• Margin protection: Vendors should ensure they can scale AI offerings without sacrificing profitability.

Empowering Hybrid AI Business Strategies

Revenera enables software producers to close the AI monetization gap by:

• Enabling flexible models that monetize not just users, but agents, transactions, and outcomes.
• Providing enterprise-ready solutions for cross-portfolio access, burst capacity, and allocation by team, product, or environment.
• Supporting usage-based pricing that covers variable costs and preserves margins as AI consumption fluctuates.
• Delivering self-service controls and governance for both producers and end customers.

You can learn more about Revenera’s Dynamic Monetization in this short video:

Monetize at the Speed of Innovation

AI is changing the game, and only those who adapt their monetization strategies will win. A practical response starts with the following steps:

• Make monetization and pricing a core discipline in product planning, not just a feature in the backlog .
• Build flexibility and transparency into your models.
• Empower customers and protect your margins.

To discuss how Revenera’s technology can empower your AI business strategy, please contact us today.