AI Pricing Strategy: Balancing the Cost Crisis to Drive Profitability

The results are in – and they paint a brutal picture for software producers who need to get on top of their AI pricing strategy.

After surveying 501 product leaders, Revenera’s Monetization Monitor 2026 Outlook indicates 70% of those who currently offer AI-driven capabilities are struggling with delivery costs – particularly cloud spend – undermining profitability.

At the same time, Flexera’s IT Priorities Report highlights tension from the buyer’s perspective, with 36% of enterprise IT decision-makers believing they overspend on AI applications – the single biggest area of over-investment.

We’re at a critical juncture, as both buyers and suppliers feel the strain of a financial paradox where each side reports a raw deal, prompting industry analysts to advise on choosing new models that reflect value as producers seek better AI pricing strategies.

AI Pricing Strategy Evolution

During the first wave of innovation, many tech companies introduced AI capabilities to existing products and subscriptions, enabling fast launches and early experimentation.

However, as the landscape matures and operational expenses are factored in – from cloud resources and compute power to data modeling and performance enhancements – there’s been a shift toward usage-based pricing models that ensure overheads are covered while providing greater clarity over consumption, cost, and control.

Although subscription remains the most common framework for AI today, the Monetization Monitor forecasts pure subscription plans to decline by five percentage points over the next 12 months, with a more nuanced AI pricing strategy emerging in the form of blended subscription and usage-based models – which are set to grow by 5%.

In total, usage-based approaches – whether prepaid, post-paid, or combined with subscriptions – are projected to make up 62% of AI product pricing strategies by 2027, marking a shift from traditional software licensing models.

On the surface, adopting a consumption mindset solves the issue of squeezed margins, as producers can set prices to ensure resource-intensive AI functionality is appropriately charged. Furthermore, as cost is directly linked to usage, customers can measure value more accurately, perhaps alleviating concerns around overspending.

However, implementing a usage-based AI pricing strategy requires careful execution, as it carries some risk while also generating insights that can potentially unlock new revenue opportunities.

Managing Hybrid AI Pricing Strategies

Much in the same way that bundling high-cost AI features into subscriptions risks eroding profitability, moving completely to consumption-based licensing creates uncertainty as revenue becomes unpredictable.

This is why many companies are evaluating hybrid models that combine subscriptions with consumption-based elements, offering core features through annual or monthly plans while providing AI capabilities on a pay-per-use basis.

This layered approach maintains recurring revenue for standard access while mitigating costs for premium functionality.

Benefits aren’t limited to cost recovery, though, as selling units of usage (i.e., credits, consumptive tokens, API calls, or whatever terminology you prefer) introduces a flexible revenue stream that scales with demand, making this a commercially viable AI pricing strategy that supports sustainable growth.

However, the biggest growth lever lies in the depth of information captured by usage-based systems, allowing product leaders to conduct meaningful customer data analysis that identifies value drivers, highlights differentiators, and guides roadmap innovation.

Monetizing AI with Data Insights

While there’s urgency to adopt AI pricing strategies that align profitability with value – especially amid a wave of media speculation about the durability of AI investment levels – data-driven software producers are looking at the bigger picture: not simply balancing costs, but studying usage to understand the outcomes users truly benefit from.

This strategic approach turns raw data into actionable insights, enabling you to:

• Pinpoint high-impact features: Identify which capabilities drive adoption and deliver the greatest customer value – and shape your sales strategy accordingly.
• Refine pricing and packaging: Ensure offerings reflect how customers actually use your product, improving revenue capture and pricing confidence.
• Strengthen retention: Detect underutilized features or engagement gaps and proactively reduce churn by educating customers on what they’re missing.
• Prioritize innovation: Direct roadmap decisions toward areas with the greatest commercial and customer impact to ensure your product remains ‘sticky’.
• Unlock growth: Reveal upsell and cross-sell opportunities by analyzing signals that indicate readiness to buy, such as approaching usage thresholds, activating advanced functionality, or expanding user numbers across accounts.

With the ability to capture missed revenue and learn how customers derive value through real-time data, it’s easy to see why usage-based models are becoming a core pricing strategy for AI companies and mainstream software producers.

However, as consumption inherently means variable revenue, monetizing AI increasingly involves balancing flexibility with predictability by layering consumption-based components onto subscriptions.

When customer outcomes are clearly understood and continuously optimized, usage-based revenue becomes easier to scale over time.

Practical AI Pricing Strategy Advice

There’s no denying software pricing models are changing for the AI era and more flexibility is on the way, with à la carte options gaining traction – allowing customers to select precise packaging configurations and only pay for the functionality they need.

AI tooling, hosting, storage, and power creates significant expense, so cost recovery is an important starting point, but the real opportunity lies in monetizing outcomes while the market is still taking shape.

Revenera’s Dynamic Monetization is designed with flexibility in mind, supporting hybrid approaches that extend beyond flat-fee subscriptions. In this short video, Jim Berthold demonstrates how it enables AI pricing strategies via consumptive tokens:

As you explore the best AI pricing models for your business, it’s advisable to choose a system that ensures transparency through clear usage reporting that validates spend and has guardrails to prevent unexpected charges.

With adjustable rate tables, real-time data, and easy API integrations, Revenera’s Dynamic Monetization provides a flexible platform to scale AI monetization initiatives.

If you you’d like expert guidance on implementing your AI pricing strategy and to learn more about Dynamic Monetization, please contact us to arrange a discovery call.