As adoption of AI accelerates, pricing remains one of its most misunderstood elements. While the underlying models evolve at lightning speed, the way they’re priced and perceived often lags behind.

Outdated assumptions, vague terminology, and a lack of transparency make it hard for buyers to forecast costs, compare vendors, or scale responsibly. It’s not just frustrating, it can also stall innovation and create unnecessary financial risk. 

Nearly 70% of SaaS companies that offer AI features are currently testing or monetizing AI products, so there is a large amount of pricing initiatives to draw inspiration from. But the reality is that AI pricing strategies are far from uniform. What works for one product or workload might break the budget for another. “Rather than seeing convergence around AI monetization, the trend seems to be the reverse: a proliferation of different flavors,” says Kyle Poyar, author of  Growth Unhinged. Yet many companies still rely on legacy playbooks, assuming credits equal flexibility, or that usage-based pricing always means transparency.

In this article, we’re cutting through the noise. Let’s break down five persistent myths about AI pricing, and reveal what resilient, future-ready pricing strategies actually look like today.

Myth 1: “Static pricing tiers are still the industry standard”

Reality: The AI landscape has moved on.

Traditional SaaS pricing was built around predictable usage, charging per user, seat, or storage limit. But AI workloads don’t play by those rules. They spike during training, fluctuate during inference, and scale unevenly across teams and use cases. That kind of volatility quickly exposes the limitations of static tiers.

Forward-thinking vendors are replacing outdated models with more adaptive options, like provisioned throughput, usage-based metering, and credit-based billing. Many even offer real-time usage tracking or burst pricing to handle sudden demand without surprise penalties.

Gartner predicts that over 25% of new enterprise software spend will shift to credit-based models by 2027. 

The smartest AI pricing strategy today reflects how AI is actually used: dynamically, unpredictably, and across diverse workloads. It’s not about fixed boxes. It’s about flexibility, visibility, and cost alignment.

Myth 2: “Credit-based billing gives me more flexibility”

Reality: Without transparency, credit systems can backfire.

On the surface, credit-based AI pricing plans sound like a win: buy a pool of value and apply it where you need it. But the reality depends on how those credits are defined, and how clearly the rules are communicated.

Some vendors attach different multipliers to different products or services. One API call might consume a single credit, while a more advanced feature burns through ten. Others quietly change credit values mid-contract or restrict rollover, turning a flexible-looking system into a rigid one.

This lack of consistency can frustrate customers, erode trust, and lead to overspending, especially when there’s no way to track prices in real time or forecast usage accurately. It also makes it nearly impossible to competitively price offerings or compare vendors side by side.

A strong pricing model doesn’t just offer credits, it also includes clear pricing tools, stable conversion rates, and safeguards to prevent unexpected price adjustments. The best AI providers treat flexibility as a real benefit, not just a marketing promise.

Myth 3: “If I know my usage, I can predict my costs”

Reality: AI workloads are too volatile for precise forecasting.

It’s easy to assume that if you can estimate usage, you can control spend. But that logic doesn’t hold up when it comes to AI. Workloads tied to training new models, serving real-time inference, or reacting to unexpected user demand rarely behave in predictable ways.

Even small shifts in customer behavior or sudden spikes in demand can cause costs to balloon, especially if pricing lacks mechanisms to handle burst capacity or usage anomalies. Relying solely on forecasts without built-in flexibility creates risk.

This is why modern AI pricing strategies often include burst pricing, spot usage, or forgiveness policies. These features give buyers room to scale while avoiding harsh overages if their predictions fall short.

To manage volatility, pricing models need to account for uncertainty, not just volume. Vendors that allow you to track usage and set prices or thresholds dynamically are better positioned to support both agility and budget control.

Myth 4: “Tokens and credits mean the same thing across vendors”

Reality: Units vary wildly, and so does what you're paying for.

One of the biggest sources of confusion in AI pricing is terminology. A “token” might represent characters, words, seconds, compute time, or even a blend of these, depending on the vendor. Similarly, credits can be counted by API calls, throughput, or bundled services.

These inconsistencies make it difficult to compare pricing plans across providers or evaluate the true cost of a feature. And when there’s no standard definition, it’s easy to make poor assumptions that lead to overspend or underutilised capacity.

Without clarity on what each unit represents, or how it’s calculated using customer data, compute power, or time, teams are left guessing. That ambiguity affects customer perception, erodes trust, and makes procurement unnecessarily difficult.

To avoid surprises, buyers should push vendors for transparency: What exactly does a token or credit represent? Is the definition consistent across services? Can usage be tracked in real time? The best AI pricing strategies start with precise definitions, and give customers tools to monitor, plan, and optimize accordingly.

Myth 5: “Billing is just a finance issue”

Reality: Pricing impacts adoption, satisfaction, and long-term value.

When pricing models are unclear or overly complex, the ripple effects go far beyond finance. Teams hesitate to experiment. Product usage stalls. Renewals slow down, not because the tech doesn’t deliver, but because the pricing doesn’t support real-world usage.

This is where billing becomes a product decision. Clear, predictable pricing helps teams feel in control. It boosts customer satisfaction, reduces friction, and turns pricing into a driver of growth rather than a barrier.

It also enables smarter planning. When teams have visibility into cost structure, they’re more confident experimenting with new features or expanding use cases. Vendors, in turn, can maximize revenue by building pricing that adapts to usage patterns, different customer segments, and real-time needs.

AI pricing isn’t just about the numbers, it’s about how those numbers shape adoption, trust, and long-term loyalty. The most effective strategies recognize that and treat billing as part of the product experience, not a line item buried in the contract.

Conclusion: Strategy Starts with Truth

AI pricing is evolving, but many assumptions haven’t caught up.

Static tiers are disappearing. Credit systems aren’t always as flexible as they seem. Predicting costs based on usage alone is risky, and vague definitions across vendors make it tough to compare offers or plan ahead.

Resilient organizations are rethinking pricing not just as a procurement detail, but as a strategic design decision. That means asking better questions, demanding transparency, and aligning pricing with how AI is actually used, being volatile, complex, and constantly shifting.

At Vayu, we believe the best AI pricing strategies are built on clarity, adaptability, and trust. They reflect customer reality, support experimentation, and scale as AI use grows. Whether you're evaluating vendors or designing your own pricing model, success starts by leaving the myths behind, and building smarter from the start.