Technology vendors are adjusting pricing models to offset escalating cloud costs driven by rising AI workloads
The rise of AI, from large language models to real-time digital agents, is pulling cloud usage into uncharted territory. Generative AI and agentic systems are computationally expensive, and that means one thing for everyone in tech: higher cloud bills. According to TD Cowen, public cloud spending is projected to quadruple in just three years. That’s not sustainable unless we rethink how we approach infrastructure and pricing models.
Enterprise IT leaders are feeling the heat. As AI moves from experiment to necessity, the underlying architecture must scale efficiently. That might mean shifting to purpose-built setups designed specifically for AI workloads. It also means cost optimization has to be deeply integrated into architecture decisions, not something we think about after deployment.
Vendors aren’t just watching this shift, they’re on the front lines. The cost of delivering AI services eats directly into margins. What used to be sustainable with traditional cloud pricing models is now a bottleneck. And when Revenera names rising cloud costs as the single biggest challenge to climbing annual recurring revenue, you know it’s time to act.
The challenge is simple: deliver transformative AI-powered user experiences without letting infrastructure costs sink profitability. Solving this means evolving how we charge for software, getting smarter about how we measure usage, how we tie it to pricing, and how we innovate around customer demand. This is where the opportunity lies. The vendors who nail this shift will do more than reduce cost exposure, they’ll unlock entirely new revenue paths.
Usage-based pricing models are increasingly replacing traditional subscription-based models for AI monetization
Subscription pricing, particularly per-user subscriptions, has been the default for years. It’s familiar, stable, and easy to forecast. But AI changes the rules. A handful of users can drive enormous compute usage, especially when tapping into high-cost AI services. That disconnect between user count and underlying resource consumption makes per-user pricing unsustainable in high-demand scenarios.
This is where usage-based models gain traction. According to Revenera, nearly 75% of tech suppliers have started adopting usage-based pricing in some capacity. These approaches tie revenue to actual consumption, not rough estimates based on user licenses. That means aligning price to real-world load on systems, an economic model that works better for both providers and customers.
For vendors, this isn’t just about covering operational costs, it’s about unlocking monetization. AI is expensive to run. Every inference, every training loop adds to the bill. Charging based on usage ensures that costs scale in step with revenue, not against it. And as AI engagement grows, so does the potential to capture value in proportion.
C-suite leaders should take this seriously. If your business relies on delivering AI-powered services, usage-based pricing reflects technical reality better than any seat-based license could. It enables flexible pricing structures that align with outcomes customers care about, speed, scale, and performance. It also encourages internal investment in usage tracking, cost transparency, and analytics. These are all systems you’ll need in place to stay competitive.
There is significant misalignment between current pricing structures and the value derived by customers from AI technologies
AI offers real outcomes, automation, better decisions, faster execution. But if the pricing model doesn’t reflect those outcomes, customers get frustrated and vendors leave money on the table. According to Revenera, only 36% of companies report strong alignment between what they charge and the value their customers perceive. That gap has to close.
Right now, many tech providers still rely on pricing models built around access, things like per-user licenses, rather than models built on value delivered. That may have worked in traditional SaaS, but AI changes expectations. Customers aren’t paying for a tool. They’re paying for performance, outcomes, and improved workflows. Whenever there’s a disconnect between price and impact, trust erodes and expansion gets harder.
To fix this, companies need clear usage intelligence. They have to know what features drive high value, which workloads are being used the most, and how those things evolve over time. Paul Bland, Senior Director of Product Management at Revenera, put it simply: “As soon as you’ve got a high-value experience, you’ve got a monetization opportunity.” Most vendors already have the data, they just aren’t acting on it yet.
For executive teams, the priority should be building pricing systems that scale with perceived and delivered value. That means investing in usage analytics, customer feedback loops, and pricing teams that can adapt quickly. When pricing aligns with impact, it becomes easier to justify expansion and upsell. AI makes this alignment more urgent, but also more achievable, if teams step up operationally.
Main highlights
- Escalating cloud costs demand pricing shifts: AI workloads are pushing cloud spending to unsustainable levels, with forecasts showing a 4x increase in three years. Leaders should reevaluate their infrastructure and pricing strategies now to prevent margin erosion.
- Usage-based pricing is the new standard: Traditional subscription models no longer scale with the cost of running AI services. Executives should accelerate transition to usage-based models that tie revenue directly to customer consumption and system load.
- Pricing must reflect customer value: Only 36% of companies report strong alignment between what they charge and the value users get. To drive growth, leaders should invest in usage analytics and build pricing structures that clearly correspond to customer outcomes.
