How to Price an AI Product: Seats, Usage or Outcomes
Price an AI product by matching the metric to the value the buyer sees: seats for broad access, usage for variable cost, outcomes for work that is easy to define. Most solo founders should start with a capped usage plan and move toward outcomes only when delivery is measurable.
Key takeaways
- Seats are simple to sell but disconnect revenue from model cost, so a few heavy users can erase your margin.
- Usage ties revenue to consumption, but unpredictable bills scare buyers unless you publish caps and credits.
- Outcome pricing charges for completed work: Intercom Fin lists $0.99 per outcome, and ThoughtSpot AgentSpot charges only when an agent delivers a conversation, workflow run or app session.
- Token prices set the floor. OpenAI lists gpt-6.1-sol at $2 input and $10 output per million tokens, with batch and flex at half.
- Test one change at a time, and put the model bill inside your profitability maths.
Price an AI product by charging for the unit your buyer already values, not for the tokens you buy. Seats suit products where access is the product, usage suits products where consumption varies, and outcomes suit work that is easy to define and verify. For a solo founder, the safest starting point is a capped usage plan, with an outcome tier added once delivery is measurable.
TL;DR
- Seats are simple to sell but disconnect revenue from model cost, so a few heavy users can erase your margin.
- Usage ties revenue to consumption, but unpredictable bills scare buyers unless you publish caps and credits.
- Outcome pricing charges for completed work. Intercom Fin lists $0.99 per outcome, and ThoughtSpot AgentSpot charges only when an agent delivers a conversation, workflow run or app session (Intercom pricing, ThoughtSpot).
- Token prices set the floor. OpenAI lists gpt-6.1-sol at $2 input and $10 output per million tokens, with batch and flex at half (OpenAI API pricing).
Disclosure: ZeroRaised publishes this analysis and is a launch site for bootstrapped products. Facts come from the linked sources as checked on 26 Oct 2026; ZeroRaised has no commercial relationship with the companies named.
Outcome-based pricing is a pricing model where the customer pays when the product completes a defined unit of work, such as a resolved support conversation or a finished workflow run, rather than for seats or tokens.
Why AI products break seat pricing#
Seat pricing charges a fixed fee per user per month. It is easy to quote and forecast, but two seats can cost very different amounts to serve: one customer asks a few short questions, another runs long agent sessions that resend the whole conversation on every call.
Token prices make the spread concrete. OpenAI lists gpt-6-astra at $10 per million input tokens and $50 per million output tokens, while gpt-6-luna lists $0.10 and $0.50 (OpenAI API pricing). A flat seat price assumes those extremes average out, which fails badly in a small base.
Agentic features widen the gap. Anthropic's guidance says that "Agentic systems often trade latency and cost for better task performance" and that autonomous agents bring "higher costs, and the potential for compounding errors" (Anthropic, Building effective agents).
What are the three pricing models?#
Seats charge a fixed amount per user. Revenue is predictable, but cost is not linked to consumption.
Usage charges per unit consumed: tokens, credits, messages, runs or minutes. It protects margin and lets light users pay less, but it pushes the risk of a surprise bill onto the buyer.
Outcomes charges per completed piece of work. It is easiest to justify when the work has a clear start and end, and hardest to run when the definition is fuzzy.
| Model | What the buyer pays for | Best fit | Main risk |
|---|---|---|---|
| Seats | Access per user | Broad, low-variance use | Heavy users erode margin |
| Usage | Units consumed | Variable or long sessions | Unpredictable bills |
| Outcomes | Completed work | Clear, verifiable tasks | Disputes over the definition |
What do real AI companies charge?#
Intercom prices its Fin AI Agent at $0.99 per outcome. It counts an outcome when a customer confirms resolution, does not ask for more help after Fin responds, or Fin completes a workflow, including handoffs, and charges once per conversation (Intercom pricing). With an existing helpdesk there are no seat costs, but a minimum monthly commitment applies.
ThoughtSpot takes a broader version of the same idea. AgentSpot, announced on 22 Oct 2026, says: "AgentSpot doesn't charge for tokens or seats. Customers pay only when an agent delivers work: a conversation, a workflow run or an app session." Building and testing are free, every plan includes unlimited users and unlimited agents, and the free plan has no time limit, no card and a weekly allowance (ThoughtSpot).
The lesson is not to copy the number: both companies found a unit a buyer can recognise, and neither asks the buyer to reason about tokens.
How do token costs set your floor?#
You cannot price below cost for long. The provider's price list is your floor (OpenAI API pricing).
| Model (per million tokens, standard, short context) | Input | Output |
|---|---|---|
| gpt-6-astra | $10.00 | $50.00 |
| gpt-6.1-sol | $2.00 | $10.00 |
| gpt-6-luna | $0.10 | $0.50 |
| gpt-5.6-sol | $4.00 | $20.00 |
Batch and flex tiers are half the standard price, so gpt-6.1-sol falls to $1 input and $5 output. Above 272K input tokens, gpt-6.1-sol is listed at $4 input and $15 output. GPT-5.6 Sol's promotional pricing is available at least through 21 Nov 2026 (OpenAI API pricing).
A worked example: 2,000 output tokens on gpt-6.1-sol cost about $0.02 at $10 per million. Add input, retries and tool calls and it rises. At $0.99 per outcome the model bill stays small; unlimited use at $5 a month lets one customer take most of it.
Cheaper models change the floor, not the logic. gpt-6-luna at $0.10 and $0.50 makes high volume work viable, but only if it passes your quality checks. How to reduce LLM costs covers caching, batch tiers and model choice.
A practical framework for a solo founder#
- Measure cost per task first. Log input, output, cache and retries against the completed job. A price without a cost number is a guess.
- Choose the unit the buyer already uses. A support team thinks in resolved conversations, an ops team in workflow runs, a research team in reports.
- Set a floor from cost, then add margin. Put your model bill into a profitability model such as the ramen profitability calculator and check that each customer covers it after payment fees.
- Cap the downside. Publish usage limits, credit packs or a minimum outcome commitment. An uncapped flat plan transfers all cost risk to you.
- Review on a schedule. Provider prices and promotional periods move, so recheck your floor every quarter and note any end dates next to the price you used.
- Keep a second model tested. A cheaper or more portable option protects you when a price changes or a provider has an outage.
Common mistakes#
- Charging per token in the customer-facing interface. Buyers do not want to audit your prompt.
- Defining an outcome so loosely that every support ticket becomes a dispute.
- Forgetting payment fees, hosting and support time when checking margin.
Where ZeroRaised fits#
ZeroRaised is a launch site for products built without institutional venture capital or accelerator equity. If you have an AI product with a price you can defend, you can submit your product. The pricing page explains the current listing options, and the newsletter is where we share new posts and launches. A listing does not promise traffic or sales.
Pricing is one part of the launch. How to launch an AI app covers testing, and how to price a SaaS product covers plan design. For model choice, see open-weight vs closed AI models.
Sources#
Frequently asked questions
What is the best pricing model for an AI product?
There is no universal best model. Seats work when access is the product and usage is fairly even. Usage works when consumption varies and buyers accept caps. Outcomes work when the completed job is easy to define and verify, as with a resolved support conversation. Many companies combine seats with usage or outcomes.
How much should I charge for an AI feature?
Start from cost per task, then add margin. Measure the tokens, retries and tool calls that a typical job uses, convert that to a cost at your provider's current prices, and check that the price covers it after payment fees. Test the result against real usage before you publish a flat plan.
Is outcome-based pricing better than seats?
It can be, when the outcome is measurable. Intercom counts an outcome when a customer confirms resolution, does not ask for more help after Fin responds, or Fin completes a workflow, and charges once per conversation. If you cannot define and verify an outcome that cleanly, usage or seats will be easier to operate.
How do I cover token costs without scaring buyers?
Set a clear allowance and a published overage rule. A capped usage plan or a credit pack gives the buyer a predictable ceiling while keeping your downside limited. Avoid unlimited flat plans until you know your cost per task and have a way to stop runaway usage.
When should I change my AI pricing?
Review it when model prices move, when a large share of usage shifts to a new feature, or when a promotional rate ends. Note the end date next to any price you budget on, and recheck your floor every quarter. A price built on a temporary discount is a margin problem waiting to happen.