Can your buyer's CFO put your product in a budget spreadsheet?
Zylo's 2026 SaaS Management Index found that 78% of IT leaders reported unexpected charges tied to AI features in the past year, up from 65% in 2025. Gartner predicts that over 40% of agentic AI projects will be canceled by end of 2027. The primary drivers: escalating costs and unclear business value.
The deals are not dying in product evaluation. They are dying in procurement. A CFO who cannot model the spend will not sign the contract. The engineering team's enthusiasm does not override that veto.
Last Updated: April 2026
The Conflict
Usage-based pricing is the builder's favorite model. Cost scales with value delivered. Light users pay less. Heavy users pay more. The economics feel fair.
Enterprise buyers hate it.
A CFO needs a number for a quarterly budget. "It depends on usage" is not a number. A procurement team needs a ceiling to model against. "We will send you reports" is not a ceiling. An IT leader competing for internal capital against departments with fixed-cost vendors needs predictability, not a calculator.
The AI Cost Governance Report quantifies the gap: only 15% of companies can forecast their AI costs within plus or minus 10%. Meanwhile, 84% report margin erosion from AI spending. The builder's ideal pricing model and the buyer's ideal pricing model are in direct conflict.
Someone has to blink first.
The Numbers
The forecastability gap is not anecdotal. Every major survey in 2025-2026 converges on the same finding.
| Finding | Data Point | Source |
|---|---|---|
| IT leaders reporting unexpected AI charges | 78% (up from 65% in 2025) | Zylo SaaS Management Index |
| Actual costs exceeding estimates | 30-50% overruns from token overages | Zylo Research |
| Companies forecasting AI costs within 10% | 15% | AI Cost Governance Report |
| Companies reporting margin erosion from AI costs | 84% | AI Cost Governance Report |
| AI leaders with major cost concerns (8x increase since 2023) | 83% of 600 surveyed | Menlo Ventures |
| IT leaders who cut projects due to unplanned cost increases | 61% | Zylo Research |
IBM Apptio's 2026 Technology Investment Management Report found that internal capital reallocation as the primary AI funding source jumped from 50% to 67% in a single year. CIOs are setting roughly 9% of IT spend aside just to cover vendor price increases.
That is the competitive environment. Your buyer's finance team is not comparing your product to a competitor's product. They are comparing your cost model to every other budget line item in the department. If your pricing cannot produce a forecastable number, you lose that comparison before the product evaluation begins.
Consolidation
Enterprise AI procurement in 2026 looks structurally different from 2024.
TechCrunch surveyed 24 venture capitalists and found an overwhelming majority predict enterprises will increase AI budgets while concentrating spend on fewer vendors. Andrew Ferguson of Databricks Ventures: "2026 will be the year that enterprises start consolidating their investments and picking winners." Rob Biederman of Asymmetric Capital Partners: "Budgets will increase for a narrow set of AI products that clearly deliver results and will decline sharply for everything else."
Constellation Research put it plainly: "Consumption models were unpredictable and CIOs, not to mention CFOs, wanted predictability."
| Phase | Buying Behavior | Pricing Tolerance |
|---|---|---|
| 2024-2025: Experimentation | Multiple pilots, proof-of-concept budgets | High. Variable costs accepted as learning expense. |
| 2026+: Consolidation | 1-2 strategic vendors, CFO sign-off, multi-year commitments | Low. Finance teams require modelable spend. |
The window to become one of the winning vendors is narrowing. The buyers you pitched as a pilot in 2024 are now running formal vendor selection processes. The selection criteria have shifted from "does this work?" to "can we budget for this?"
The Credit Backlash
Credit-based pricing is spreading fast. Growth Unhinged reports that 79 of the top 500 SaaS companies now offer credit-based pricing, up from 35 at end of 2024. A 126% year-over-year increase. The adoption brought backlash.
Cursor transitioned from 500 fast requests per month to $20 worth of API credits in June 2025. Heavy users ran out of credits within days. Some saw bills jump from $28 to $500 in three days. CEO Michael Truell issued a public apology and offered refunds.
Salesforce Agentforce went through what Monetizely described as "whiplash-inducing changes." The initial $2-per-conversation model faced pushback because customers did not understand what "conversation" meant. Salesforce introduced Flex Credits at $0.10 per action, then a $125+/month per-user seat license through the Agentic Enterprise License Agreement. By early 2026, three pricing models were running simultaneously: per-conversation, per-credit, and per-seat.
The pattern: credits are a unit of account, not a unit of value. One GTM lead quoted in Metronome's 2025 Field Report: "Our finance team likes it. Our customers don't know what a credit does." Finance teams cannot translate credits into budget line items. The opacity problem compounds with scale.
Agentic Workflows Break the Model
Traditional AI features have human-speed cost patterns. Request, response, review. Cost scales linearly with user actions. Agentic workflows break that linearity.
One goal triggers recursive fan-out: 1,000 sub-tasks, multiple model calls per sub-task, retries on failure. The cost per outcome varies by orders of magnitude depending on task complexity. When an enterprise buyer asks "what will this cost next quarter?" and the honest answer involves recursive model calls with variable depth, the deal stalls.
| Cost Pattern | Traditional AI Feature | Agentic Workflow |
|---|---|---|
| Trigger | Human clicks a button | Agent receives a goal |
| API calls per action | 1-3 | 10-1,000+ |
| Cost variance | Low (predictable per-request) | High (depends on task complexity) |
| Budget modeling | Usage x unit price | Requires simulation and historical baselines |
Gartner's June 2025 prediction identifies three drivers for the 40% cancellation rate: escalating costs, unclear business value, and inadequate risk controls. The cost items that most frequently surprise organizations are not headline technology spend. They are high-frequency API calls at scale, custom connectors to legacy systems, and ongoing operational costs for agent monitoring.
This is not a product failure. It is a forecasting failure. For the margin implications of variable-cost AI products, the math gets worse with every improvement to the product. Better agents complete more tasks. More tasks trigger more inference. More inference compresses the margin.
What Procurement Asks Before Signing
Enterprise procurement teams evaluate forecastability across six dimensions. The rows you cannot answer are the rows that stall the deal.
| Requirement | The Question |
|---|---|
| Spend caps | Can we set a maximum monthly spend that cannot be exceeded? |
| Threshold alerts | Will we receive warnings at 50%, 75%, 90% of budget? |
| Trajectory projections | Can the dashboard show projected spend based on current usage? |
| What-if modeling | Can we simulate "what happens if usage doubles next quarter"? |
| Overage behavior | When we hit the cap, does service stop or do overages apply? |
| Historical patterns | Can we see usage trends by team, feature, and time period? |
Gartner recommends that enterprise buyers embed "dynamic usage caps and outcome guardrails within every large contract."
Deloitte's survey of 3,235 leaders found that token consumption is expected to roughly double within two years. By 2028, 61% expect to consume more than 10 billion tokens per month. The stakes are rising. The absolute dollar amounts at risk make forecastability more valuable with every quarter, not less.
Kyndryl's 2025 Readiness Report found that 61% of 3,700 business leaders feel more pressure to demonstrate AI ROI compared to a year ago. The financial lens has shifted from "what can AI do?" to "what does AI cost, and can we predict it?"
Who Blinks First
The hybrid model is the blink.
Salesforce's AELA wraps per-seat licensing around Agentforce, bundling 1M Flex Credits per year at roughly $550/user. Seats become the budget-friendly wrapper. Credits handle the variable layer underneath. OpenAI partnered with Metronome for usage-based billing infrastructure that supports self-serve pay-as-you-go alongside bespoke enterprise contracts with commitment pricing.
| Provider | Base Component | Usage Component | Predictability Mechanism |
|---|---|---|---|
| Salesforce AELA | $550+/user/month | 1M Flex Credits/year included | Seat-based wrapper, "fair use" policy |
| OpenAI API | None | Token-based pricing | Soft and hard usage limits, spend alerts |
| Cursor | $20/month | $20 worth of API credits included | Hard cap, manual credit top-up |
Metronome's field research found: "Predictability, not price point, drives enterprise adoption. Companies that give buyers clear expectations via caps, rollovers, or flat rates unlock usage and expansion."
The consistent pattern: the vendor blinks first. They sacrifice pure usage alignment for a hybrid that gives the CFO a number. The CFO signs. Usage expands. The vendor wins the deal they would have lost by insisting on pure consumption pricing.
For a framework on choosing the right model, see How to Price AI Products: A Data-Driven Framework. For the infrastructure that makes hybrid pricing operational, see Billing Infrastructure vs. Cost Visibility.
The Practical Answer
If you are selling AI into enterprise, the question is simple: can your pricing produce a number a CFO can put into a quarterly budget?
Pure usage-based with no caps. Your buyer's finance team has no ceiling to model against. You will hear "we need more usage data" indefinitely.
Caps without projections. Better. But the buyer still cannot see where spend is headed. Add a usage dashboard with trajectory forecasts. That is the difference between a vendor and a partner.
Agentic products. Bound the cost of a single workflow execution. If a buyer asks "what is the maximum this could cost per run?" and the answer is "it depends," you need guardrails: retry limits, depth caps, circuit breakers. These are engineering decisions and pricing decisions at the same time.
A concrete starting point: "Up to X usage for $Y/month, overages at $Z per unit, hard cap at $W." That sentence is more signable than an elegant credit system that requires a calculator to forecast. For what the usage-based pricing trap looks like in practice, the pattern is the same: the model that feels fair to the builder feels unpredictable to the buyer.
The Vendors Who Win
Enterprise buyers have shifted from price sensitivity to forecast sensitivity. The evidence is consistent across Zylo, Deloitte, Gartner, and Menlo Ventures.
The vendors who provide cost predictability, through commitment pricing, spend caps, or hybrid models, win enterprise deals. The vendors who say "it depends on usage" lose to competitors who offer a number the CFO can sign.
The question is not whether your product is good enough. The question is whether your pricing is forecastable enough.
If you are building AI products for enterprise buyers, Bear Lumen provides the cost visibility and spend projection layer that turns variable costs into forecastable budgets. See how the pricing works.