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Charge for the unit of value your customer can understand and you can measure—not automatically for the tokens your system consumes. Token pricing fits when customers are buying model access itself. For an AI application that completes work, recognizable work units or credits may be easier to budget; outcome pricing can fit when results are observable and attributable. The practical rule is to price at the highest layer of value you can reliably measure, attribute, and defend.
What should an AI product’s pricing unit represent?
Start with what the customer is buying, not with the most convenient number in your infrastructure bill. A model provider selling inference may have a good reason to meter tokens. An application that completes a workflow can often describe its value in terms of that work, while a product with measurable business results may be able to charge for outcomes.
Andreessen Horowitz authors Tugce Erten and Sarah Wang frame the decision around a unit customers already understand and the company can measure consistently. They note that buyers who ask about tokens may be seeking comparability or control. Those concerns still need a clear answer, but they do not require every application to make tokens its billing unit.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesWhen does token pricing make sense?
For raw model access
Tokens are a natural meter when the product being sold is model inference itself. The customer is paying for access to a capability whose use is counted in input and output tokens, so the meter directly describes consumption.
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For applications, tokens can make budgets harder to predict
In an application, token use can vary with context length, retrieval, retries, reasoning time, and output size. Buyers may have to forecast those technical details before they can estimate the cost of a workflow or compare its return with alternatives. A token-based bill can still be transparent about consumption, but the customer may not know what useful work that consumption produced.
As a limited pass-through
A hybrid plan can pass through token costs for unusually expensive or volatile model use while charging for other work through a more legible unit. That makes the exceptional cost visible without making every routine application task a token-estimation exercise.
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When are credits or work units a better fit?
Credits can work for an application in the middle of the AI stack: the product does variable work, but customers can recognize the job better than its underlying infrastructure. The important distinction is that credits are a currency, not automatically a value metric. If a credit is just an opaque translation of token consumption, it may obscure rather than clarify the bill.
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- Use plain descriptions of the work a credit buys.
- Show intuitive effort bands when task complexity varies.
- Let a shared pool cover multiple workflows when customers can still understand how it is used.
- Explain completed work and where capacity went, rather than exposing only an unexplained balance change.
In a survey reported by Andreessen Horowitz in 2026, 27 of 50 technical AI buyers preferred credits tied to recognizable work, while 14 preferred tokens. The article does not provide the sampling method, geography, question wording, or enough methodology to establish how representative those counts are; they are a reported survey result, not a universal preference estimate. Read the authors’ article.
When can you charge for outcomes?
Outcome pricing is most plausible when the result is observable, attributable to the product, and valuable enough to support stable pricing. Examples include a resolved support conversation, a qualified lead, a booked meeting, a processed claim, or dollars recovered.
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Before choosing this meter, decide how success is defined and how attribution will work. If the customer and vendor cannot consistently agree whether the outcome occurred or what part the product caused, an outcome fee can produce disputes rather than clarity. In that case, a work unit may be a more defensible measure.
How should the pricing models be compared?
| Pricing unit | Customer legibility and budget control | Value measurement | Cost and margin exposure | Good fit |
|---|---|---|---|---|
| Tokens | Clear as a measure of model consumption; application spend can be harder to forecast when technical usage varies. | Measures inference use, not necessarily useful work or business value. | Can track a variable cost, but total delivery costs may also include retrieval, tools, data, retries, and failed runs. | Raw inference access, or explicit pass-through for costly or volatile usage. |
| Credits or work units | Can be understandable if units map to recognizable tasks and effort; opaque conversions weaken control. | Measures completed work when the unit is tied to a customer-recognizable task. | Requires a price that accounts for variable delivery costs and protects against unusually expensive tasks. | Applications completing useful but variable workflows. |
| Outcomes | Can connect charges to results, provided both sides agree on the definition and attribution. | Directly tied to a business result when that result is observable and attributable. | Requires stable enough results and pricing to make the fee defensible. | Work such as resolved conversations, qualified leads, booked meetings, claims processed, or dollars recovered. |
The comparison is qualitative: Erten and Wang do not publish a quantitative ranking of these models or independent evidence that any one of them improves margins in practice.
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How can a hybrid model work without confusing customers?
A hybrid plan can assign a separate meter to each layer of what the product sells. For example, seats can grant access, credits can cover variable application work, token pass-through can handle unusually expensive model use, and outcome fees can apply where attribution is clean. The goal is not to add meters for their own sake: each charge should have a clear unit and rationale.
The billing meter also does not have to be the cost meter. A customer can be shown what work was completed and where capacity went without being billed line by line for every underlying cost. Erten and Wang’s guidance is to separate what the work is worth from what it costs to deliver, while ensuring the company can measure and defend the price.
What costs should the price account for?
Inference is only one potential cost of delivering AI work. Retrieval, search, tool calls, data, retries, and failed runs can all affect unit economics. A fixed work-unit price can be easier for customers to budget, but the company still needs to account for these variable costs when setting rates and monitoring gross margins. If a particular workload is unusually volatile or expensive, a clearly explained pass-through component may be more defensible than hiding that exposure inside a general credit price.
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What does the Clay example illustrate?
The Andreessen Horowitz article reports that Clay’s 2026 pricing memo separates Data Credits for third-party data from Actions for orchestration work, uses fixed pricing for models with predictable costs, and passes through actual token cost without markup for more volatile, expensive reasoning models. This is an example as described by Erten and Wang; the memo was not independently checked for this article. It illustrates how different layers of work and cost can receive different meters rather than forcing one unit to represent everything.
Quick Recap
A practical way to choose your meter
- Name the thing being sold. Decide whether customers are buying inference access, application work, or a business result.
- Choose the highest defensible value layer. Use an outcome only if it can be observed and attributed; otherwise consider a recognizable work unit. Use tokens when inference itself is the product or when a specific volatile cost should be passed through.
- Check measurability and explainability. Confirm that usage can be counted consistently and that customers can understand what each unit buys.
- Map costs behind the meter. Include inference, retrieval, tools, data, retries, and failures where relevant, even if customers are billed for work rather than each cost component.
- Test for budget surprises. Consider whether a customer can estimate spend before starting a task and reconcile the bill afterward.
- Keep each hybrid component distinct. Give seats, credits, pass-through costs, and outcome fees separate purposes and clear descriptions.
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