AI pricing is undergoing a rapid evolution. As with every big industry shift, the last word’s not spoken on what exactly the monetization models will look like in the future. What is clear is that consumption will play a much bigger role.
Software companies that offer AI capabilities have already reacted and started to offer options, between pay-as-you-go (postpaid), prepaid consumption models, fixed price subscriptions and enterprise contracts.
I believe that in the enterprise world most AI pricing models will ultimately converge on prepaid structures for consumption, in conjunction with a base subscription, not because vendors prefer them, but because customers demand them.
The Appeal and Limits of Postpaid AI Pricing
The early wave of AI pricing has followed the same pattern as cloud: pay-as-you-go, billed in arrears. It’s a simple model that feels inherently fair; customers only pay for what they use. Large infrastructure providers like AWS, Microsoft, or Google have popularized this approach, OpenAI and Anthropic followed. Their scale and market power allow them to absorb variability in both usage and revenue.
This approach has worked so well because it lowers the barrier to entry, encourages experimentation, and aligns cost with value. But as AI usage scales across enterprises, this model introduces a fundamental challenge: unpredictability. And that exact problem has come to surprise more and more companies as they pushed to drive adoption of AI internally.
The issue is that end customer teams don’t have to plan for one or two solutions (as in the Cloud world) but for basically any application that introduces consumption-based components for their AI capabilities. For an enterprise that will be in the hundreds of applications.
From a customer perspective, postpaid AI pricing makes it difficult to forecast spending. From a vendor perspective, it creates volatility in revenue streams. These issues are manageable at small scale but become significant as AI adoption grows.
The CFO Challenge: Lack of Budget Predictability
For enterprise customers, the biggest concern with AI pricing isn’t flexibility – it’s governance and control.
CFOs and finance teams need to answer basic questions with confidence:
- What will our AI spend be this quarter?
- How do we budget for next year?
- How do we prevent unexpected cost spikes?
Postpaid models make these questions harder to answer. AI usage can vary significantly based on user behavior, automation levels, or new use cases. Without clear guardrails, costs can escalate quickly.
We’ve seen this before. In the cloud era, unpredictable usage-based billing led to the rise of FinOps: A dedicated discipline focused on managing and optimizing cloud spend. Organizations were forced to build processes and teams to regain financial control.
AI pricing is now recreating that same challenge, but at a larger scale. Procurement teams will increasingly push back on models that lack predictability.
The Vendor Challenge: Revenue Volatility
Unpredictability cuts both ways. While customers struggle to forecast costs, software providers face challenges forecasting revenue.
Postpaid AI pricing introduces:
- Variability in monthly and quarterly performance
- Dependence on uncertain usage patterns
- Reduced forward visibility for investors and boards
For most software companies, this is a problem. Markets reward predictable, recurring revenue streams, not volatility.
Hyperscalers can tolerate this dynamic due to their size and market position, but mostly because of the barriers to exit of their business model. Where and how applications and data are hosted cannot be changed overnight, hence cloud providers get less volatility than application providers whose product can be replaced more easily. As AI becomes a larger share of revenue, vendors will need more stable monetization models.
The Customer Backlash Moment
In the early stages, customers accept postpaid AI pricing because the stakes are low. Usage is limited, and costs are incremental. But as AI becomes embedded in core workflows, spending increases, and so does scrutiny. This is when behavior changes:
- Procurement teams demand clearer cost structures
- Finance teams push for predictable budgets
- Unexpected bills trigger internal escalation
At this stage, postpaid AI pricing begins to create friction. Customers don’t just want flexibility, they want certainty.
Even AI-forward companies like ServiceNow have recognized this dynamic early, structuring much of their AI monetization around prepaid or committed models.
The Evolution of AI Pricing
A consistent pattern is emerging in how AI pricing strategies evolve. Most vendors explore different options, but some trends are solidifying:
- Tiered subscription (good/better/best) with usage guardrails. This model does not call out consumption but the subscription terms allow a certain amount before customers get pushed up to the next subscription tier. This model is prepaid by definition and highly predictable, but it might not work if customer usage varies heavily between different end customer cohorts, as those whose use of metered AI capabilities is very limited might be put at a disadvantage.
- Subscription and Consumption: While very limited consumption is included in the subscription base price, consumption of certain add-on capabilities is metered and charged. These extra consumption packages could theoretically be sold as prepaid or postpaid, but for all the reasons discussed above, vendors often lean towards selling consumption packages ahead of time.
- Full switch to a credit-based model introducing a custom-currency for anything, with some consumptive areas. Salesforce is now offering FlexCredits for their Agentforce solutions and more vendors are following by the day. This model takes out the complexity of having different terms and metrics for different products and capabilities and introduces a credit model in which the customer buys, upfront, a pool of credits, which can be leveraged for almost anything, from different products within the portfolio (lowering the barrier to cross-sell options) to AI capabilities. That model, because it is sch a big change for the vendor and the end customers, will always be a prepaid model.
What is clear, though, is that neither customers nor vendors will tolerate the free-for-all access to AI capabilities. The models outlined below enable a measured move to consumption-based pricing components, without inflating that move with risk. These models are:
- Free Access to Drive Adoption
AI features are initially offered for free to encourage experimentation and gather usage data.
- Premium Packaging
AI capabilities are introduced as paid features, either in higher tiers or as add-on modules.
- Usage Caps
Vendors begin to limit the amount of AI usage included in subscriptions, introducing credits or quotas.
- Overage Charges
Some vendors experiment with post-paid billing for usage beyond these limits, often leading to customer pushback when costs exceed expectations.
- Flexible Add-Ons
To mitigate this, vendors offer usage packs or mid-term upgrades, giving customers more control.
- Prepaid Commitments
Ultimately, vendors shift toward prepaid models, where customers commit to a defined level of usage upfront, often annually, in exchange for discounts and predictability.highly predictable for both parties. The challenge is that they only work well for the vendor if the end customer consumes – on average – as much as they planned to. If not, churn is on the radar as these consumption packages or higher subscription tiers don’t get renewed. These models, almost like anything, are bound to function if they complement a product-led growth strategy, in which customer value and adoption is encouraged and supported. Sold as shelfware, any consumption components holds significant risk to the vendor.
Designing AI Pricing for the Future
The key question for software providers is not whether to adopt pricing and packaging for their new AI capabilities, it’s how to evolve AI monetization effectively.
The most successful companies will design their pricing strategies with the end state in mind:
- Start with flexible, low-friction models to drive adoption, hand in hand with a product-led growth strategy
- Introduce structure through tiers, caps, and add-ons
- Transition toward prepaid commitments as usage scales
The winners will offer models that are predictable but also flexible to scale up or down, and they will offer full end customer transparency and controls.
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For this, or for any pricing change, to be successful, three things must happen:
- Product teams must own the transition to new models and it must be based on data and customer insights.
- Sales and GTM teams must be enabled to sell in a different way. While it may sound small, it’s a different approach and a new muscle to train.
- The systems and the automation must be in place to guarantee a great, data-driven customer experience without any friction.
Bottom Line
Postpaid usage-based models have been the natural starting point for AI pricing. But they are not the final destination.
As AI becomes mission-critical, and spending becomes material, both customers and vendors will prioritize predictability. The result is inevitable: a shift towards a smart combination of flat-fee subscriptions and commitments and consumption components.
The opportunity now is to get ahead of it.
