Last year I completed research about the emerging pricing practices for agentic solutions, which I captured in two articles, “ Demystifying AI Agent Pricing ” and “ Headwinds and Tailwinds Driving the Future of AI Agent Pricing .” The trigger for that research was the disruption of the per-user-per-month packaging we had come to expect from chat-based AI solutions. It was the power of reasoning models, combined with semi-autonomous agentic harnesses, that effectively broke the business model for per-user pricing. AI vendors needed to make changes to maintain their margins as aggressive AI build-out continues.

My initial goal with this research was to dig a bit deeper and understand the challenges facing enterprises as they deploy more agent-based solutions. Would it be easy to use price as a selection criteria? Are product stacks comparable enough for a true apples-to-apples comparison? And what suggestions could I provide for enterprises heading into the next budgetary cycle? It turns out this is an almost impossible task without deep discussions and negotiations between customers and vendors. Even something as simple as AI credits — the new billing metric being used by many vendors including Salesforce, Microsoft, and SAP — is not easy to define, compare, or forecast.

(Note: My firm, Moor Insights & Strategy, provides advisory services to many companies in the tech industry — including, for this article, AWS, Google, Microsoft, Salesforce and SAP.)

Simply put, the industry is still figuring this out. In fact, in every vendor briefing about pricing I have received this year, the only area of consensus is that pricing remains a work in progress. My core takeaway one year in: Pricing has not gotten easier to compare, and the rise of AI credits is a big part of why. Given that sobering realization, my focus one year into my work on agentic pricing is to assess what was predicted, provide insights on the current market, and update my predictions for the next 12 months. This is part one of a two-part series: This piece grades last year’s predictions and sizes up today’s market; part two covers what’s coming next and what enterprises should do about it now.

Agentic Pricing Predictions From 2025

Overall, my predictions from “ Where Is AI Agent Pricing Headed? Predictions for the Next 3 Years ” from last year were solid, but unfortunately that does not mean that comparing different solutions is any easier now, and in fact it is arguably harder in some cases given the advent of AI credits. Also as I suggested last year, the ability to predict and forecast future costs without metering or better FinOps technology remains a challenge.

Let’s take a look at my individual predictions and what has happened in the interim.

Prediction 1: Time-Based Pricing Will Merge Into Consumption-Based Pricing

  • Rationale: Time-based (per-seat, per-month) pricing lacks viability for agents given inconsistent usage. Vendors would move toward “reserve pricing,” a monthly fee for a reserved capacity block plus overage charges beyond it, with pooling strategies letting teams share a consumption allocation rather than paying per user.
  • What has actually happened: Largely confirmed. Every vendor we examined now has some form of metering for agent usage that’s separate from a base price. However, the billing for that metering is often based on somewhat obscure units, sometimes referred to as “credits.” While credits make capacity more fungible across a vendor’s stack, they are also more complex and less predictable. (Billing for text- versus voice-based interactions is an example of this.)

Prediction 2: Action-Based Pricing Has Potential

  • Prediction: A per-action model, comparable to what Salesforce was signaling, has real viability, particularly where agent cost can be measured against the cost of a human doing the same task. This works best in domain- or industry-specific solutions, with pricing that scales according to task complexity and replacement risk.
  • What has actually happened: Partially confirmed as a viable model, but still a single-vendor pattern. Salesforce has now adopted the Flex Credit “standard action,” which costs a flat 20 credits ($0.10) regardless of how many tokens or LLM calls it took. This improves cross-product fungibility but is also less transparent and predictable than customers want.

Prediction 3: Consumption-Based Pricing Will Not Change Much

  • Prediction: The core consumption model (tokens, compute, API calls) would remain largely intact for enterprise applications, but vendors would increasingly bundle multiple products (IDEs, platforms, observability tooling) into simplified, multi-product packages rather than changing the underlying metering.
  • What has actually happened: Confirmed. AWS’s stack is still priced almost entirely in literal, granular consumption units (vCPU-hour, per-1,000-invocation, per-token), essentially unchanged in structure. Google prices its infrastructure layer the same way. The bundling half of the prediction landed even more clearly than expected: All three tiers of vendors have consolidated and rebranded their agent platforms into single umbrella SKUs within the last 12 months. The underlying meter didn’t change; the packaging around it did, exactly as predicted.

Where Agent Pricing Stands Today

So, what does this landscape look like today? As I stated earlier, vendors still understand that this remains a work in progress. In fact, we are now dealing with more options than before. This continued experimentation puts an extra burden of research on enterprises trying to figure out what will work best for them at the right price.

Credits-Based Systems Have Challenges

In an attempt to simplify pricing across a wide range of AI and SaaS products, some vendors have introduced “credits.” Unfortunately, the simplicity this achieves within that vendor’s own ecosystem has also produced incompatible cross-vendor comparisons (see the graphic below as an example). While making an apples-to-apples choice difficult, the bigger controversy with credits is the lack of predictability over time. Without a true countable metric to attach to (such as users, servers, or minutes), credits can be priced somewhat arbitrarily and used to incentivize what the vendor wants to achieve rather than the customer’s desired outcome. This could yield good or bad results for the customer.

For instance, a credit for an agent to interact with a third-party MCP server could be priced significantly higher than a vendor-provided one. Conversely, we could also see vendors assign different credit amounts for using less-demanding models, similar to how tokens are applied to a request in Claude or ChatGPT. Every vendor we’ve spoken to that uses this model concedes these challenges and expresses willingness to get credit-based billing properly tuned over time.

Be Ready To Go Deep On Pricing During Vendor Evaluation

Price shopping is nearly impossible unassisted because of the inconsistencies in pricing models and, often, the lack of easily accessible pricing information. While some vendors are being very transparent about pricing and prerequisites, others are not. Many products will say something like “Contact our sales team for pricing.” So, informal price shopping often requires vendor sales involvement, and even that is not always enough to conduct a proper sizing.

It’s worth noting that the investment in and rollout of forward-deployed engineering teams by major vendors is partially driven by a desire to help customers understand which products they need and how much of them. And while vendors are providing end customers with valuable insights into better agent adoption, the real goals for the vendor are (1) a purchase agreement and (2) insights on the agents themselves so they can improve products and pricing models.

SaaS Versus Hyperscaler Is Not A Prequalifier

Given that credit-based systems tend to be common in SaaS, and that consumption-based models are supported by hyperscalers, it might be easy to preselect one type of vendor versus the other. But I would not let the pricing model sway your decision too much, as these models are still evolving. For instance, we are seeing consumption-biased cloud vendors like AWS experimenting with new pricing models for products like Amazon Quick. And, interestingly enough, we are also seeing both SaaS and hyperscaler vendors refocus their attention on the full AI stack versus relying on their core positioning and value propositions. A good example of this is how SAP is currently repositioning itself as its traditional SaaS business model continues to evolve with AI. What this means is that technical strategy is more homogenous while pricing strategies remain fluid. To me, this suggests that it’s wise to consider a wider range of vendor types for an enterprise’s agent needs.

Part two of this series covers where agent pricing goes from here, with specific consideration of sovereignty and IP protection pressure, advanced bundling, accuracy-driven premium pricing, vendor lock-in incentives, and even surge pricing, along with concrete recommendations for how enterprises should approach agent pricing heading into 2027.

Moor Insights & Strategy provides or has provided paid services to technology companies, like all tech industry research and analyst firms. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking and video and speaking sponsorships. Of the companies mentioned in this article, Moor Insights & Strategy currently has (or has had) a paid business relationship with AWS, Google, Microsoft, Salesforce and SAP.