Per-Seat vs Per-Token vs Per-Output: Financial Tradeoffs of AI Pricing Models
2025-12-10
You can have a strong AI product and still end up with weak economics if your pricing model fights your cost structure.
Most AI startups lean toward usage based pricing because that is how model providers bill them. Buyers, meanwhile, still like the simplicity of per seat. This post breaks down the tradeoffs so you can pick a model you can defend in a board meeting or data room.
Last updated: December 2025
The three AI pricing models
A quick map of the common structures:
- Per seat
Charge a flat fee per user per month. - Per token
Charge for raw usage, usually based on token counts from models like OpenAI, Anthropic, or Gemini. - Per output
Charge per finished outcome, such as a generated email, document, call summary, or ticket resolution.
Many teams end up with a hybrid model. One base fee plus a usage or output component keeps things predictable while still scaling with activity.
Per-seat pricing
Per seat is the classic SaaS model. It is familiar and easy to explain.
You might charge 50 dollars per user per month for access to your AI features inside a workspace.
Benefits
- Easy for customers to budget
- Predictable revenue for you
- Smooth for annual contracts and expansions
- Simple billing mechanics
Per seat revenue moves with headcount, not usage. That gives you more stable monthly numbers and a clean ARR story.
Tradeoffs
AI workload does not scale with seats. It scales with usage.
If one customer barely touches the AI features and another is generating content all day, they pay the same per seat. Your gross margins will not match.
Other issues:
- Customers argue about inactive seats
- Teams delay adding users
- Power users can drive large AI bills inside a fixed per seat rate
If you choose per seat, you still need internal usage tracking or you risk selling seats at a loss without knowing it.
When per seat works
Per seat works when:
- Usage per user is consistent
- AI cost per unit is low relative to your price
- The value of the product is tied to individuals, not bulk workloads
If power users dominate your workloads, per seat gets shaky fast.
Per-token pricing
Per token pricing ties revenue directly to AI usage. It mirrors how your upstream providers bill you.
You might charge 3 dollars per one million tokens across your supported models.
Benefits
- Transparent unit economics
- Easy to model margins: revenue per token minus cost per token
- Low friction for new customers who want to start small
- Revenue grows naturally with usage
As long as your cost per token stays stable, per token keeps your margins clean.
Tradeoffs
Buyers do not think in tokens.
Common pain points:
- Hard for customers to predict monthly spend
- Bills fluctuate with usage spikes
- Non technical decision makers have trouble understanding what a “million tokens” means
On your end, revenue becomes more volatile. Seasonality, experiments, and traffic bursts all pass straight through.
Discounting is also more visible with per token pricing. A small adjustment to the per-unit rate compounds quickly.
When per token works
Per token tends to work when:
- Your buyers are technical
- Your product is API-first
- Your upstream cost structure maps cleanly to tokens
- You offer good visibility and spend controls
If you do not provide alerts, caps, or budgets, you will be dragged into constant billing disputes.
Per-output pricing
Per output pricing is a middle ground. Instead of charging for raw tokens, you charge for completed outcomes.
Examples:
- Per generated article or draft
- Per sales email created
- Per helpdesk ticket resolved with AI assistance
- Per summarized call or meeting
The customer sees a clear value unit. Internally, you still track tokens and AI provider costs.
Benefits
- Easy for buyers to understand
- Lets you align price with value, not infrastructure
- Clean internal economics when cost per output is stable
If it costs you 5 cents of AI and infra to produce an output and you charge 40 cents, the math is simple for both sides.
Tradeoffs
Cost per output is not always stable.
Long inputs, retries, or multi-model workflows can push up the true cost. Averages do not tell the whole story.
You need:
- Cost per output
- Distribution of costs, not just the mean
- Awareness of expensive edge cases
If too many customers sit in the “expensive edge,” your margins collapse even if your averages look decent.
When per output fits
Per output works well when:
- You can define a clear unit of value
- Buyers care more about outcomes than low-level usage
- You have solid logging to map outputs back to true cost
It’s a strong option for AI products where value is tied to what the model produces, not how many tokens it consumed.
Revenue predictability across models
From most predictable to least:
- Per seat
- Per output
- Per token
Per seat moves with user count and contract changes. Per output moves with customer activity. Per token moves with raw workload, which can change quickly.
A hybrid model can balance predictability and fairness.
Common pattern:
- Platform fee (base)
- Usage or output fee (variable)
The platform fee stabilizes revenue. The variable fee captures upside when customers grow.
Unit economics and AI gross margin
No matter the pricing model, define a clear internal unit and understand its economics.
Good units:
- Per active user
- Per one million tokens
- Per one thousand outputs
Track:
- Revenue per unit
- Direct AI and infra cost per unit
- Gross margin per unit
Then break it down by:
- Customer
- Plan
- Feature
Per seat needs internal usage tracking or you will not know which seats are profitable. Per token is clean but can be noisy when providers change pricing. Per output is only as accurate as your cost attribution.
If your upstream costs drop over time and you keep customer pricing the same, your margins naturally rise. That story is easy to show with per token or per output models.
Revenue recognition considerations
ASC 606 drives all three models. You recognize revenue when you deliver service.
Typical patterns:
- Per seat
Recognize revenue straight-line over the contract period if you provide stand-ready access. - Per token
Recognize revenue as usage occurs. - Per output
Recognize revenue when the output is delivered or made available.
If you sell prepaid AI credits, you normally treat the cash as deferred revenue, then recognize revenue as customers consume credits or when credits expire.
Usage-based models require accurate logs. Auditors will ask whether your usage numbers match your billing numbers.
How investors read AI pricing models
Investors focus on:
- Predictability
- Gross margin
- Upside potential
- Customer control and spend stability
Per seat:
- Strong predictability
- Can hide poor unit economics if usage is high
Per token:
- Transparent economics
- Can feel like a commodity unless paired with clear product value
Per output:
- Strong narrative if outputs tie to business outcomes
- Requires solid internal costing
If you can explain your AI unit economics cleanly and show your margins by cohort, investors care less about the exact pricing model.
Putting it into practice
If you are tuning or choosing a pricing model, try this:
- Pick the story you want buyers to understand
- Map AI costs to that story for a few months
- Start with a hybrid so you get both stability and usage scaling
- Give buyers spend controls, caps, and clear overage rules
- Watch your unit margins and refund patterns
You can always make pricing more granular as your AI cost tracking matures. It is harder to undo a complicated model that nobody fully understands.
The goal is clarity: a model you can explain in one slide and defend under due diligence.
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