Per-seat
Per-token
Per-task & Per-outcome
—per-seat / month
—per-token / month
—per-task / month
—per-outcome / month
Cost per interaction, side by side
| Model | Effective $ / interaction | Scales with volume? |
|---|
Which model is cheapest, by volume
Same per-unit prices held constant — only monthly interaction volume changes. The cheapest cell in each row is highlighted.
| Interactions / month | Per-seat | Per-token | Per-task | Per-outcome |
|---|
The cheapest pricing model is a function of volume, not a global answer
Flat per-seat pricing has no marginal cost, so it is overkill at low volume and a bargain at high volume; every usage-based model does the opposite, cheap when you barely use it and increasingly expensive as you scale. The crossover between them is a specific, computable number — seat price divided by your cheapest usage-based cost per interaction — not a rule of thumb. Price the raw token spend behind any of these on the LLM token cost calculator, check flat-vs-usage math for a single tier on the seat-based vs usage-based pricing calculator, model deflection-rate economics specifically for support automation on the cost per resolved ticket calculator, and pressure-test your own markup on the AI wrapper margin calculator.
How this calculator works
The AI Agent Pricing Model Calculator prices the same monthly interaction volume under four pricing shapes at once. Per-seat is flat: seats deployed times price per seat, independent of volume — a deliberate simplification that illustrates the real trade-off, since it is exactly the property that makes seat pricing win at high volume and lose at low volume. Per-token multiplies your input and output tokens per interaction by their respective prices to get a cost per interaction, then by volume. Per-task multiplies a flat price per action by volume directly. Per-outcome multiplies a price per resolved result by volume times your success rate, since outcome pricing only bills for the share of interactions that actually succeed.
The volume-sweep table holds every per-unit price constant and only changes monthly volume, which is what reveals the crossover: at low volume the usage-based models are all cheaper than the idle flat seat, and above a specific volume — seat cost divided by the cheapest usage-based cost per interaction — the seat becomes the cheapest option and stays cheapest as volume keeps growing. Per-outcome is usually the most expensive per raw interaction of the four because its headline price bakes in the cost of every unsuccessful attempt the vendor still had to run; that is the price of risk transfer, not of compute, and it is worth paying only when budget certainty on results matters more than the lowest possible unit cost. All prices here are adjustable assumptions representative of published 2026 vendor structures, not live quotes — confirm current pricing before committing to a contract.
Frequently asked questions
What is the difference between per-seat, per-token, per-task and per-outcome AI agent pricing?
Per-seat charges a flat monthly fee per human user or deployed agent regardless of how much work it does — predictable, but wasted if usage is low and undercharged if usage is high. Per-token bills the raw model cost of every interaction, the most granular and closest to true compute cost. Per-task charges a flat fee for each discrete action the agent completes — a lookup, a booking, a ticket touched — independent of how many tokens that action happened to use. Per-outcome, the newest model popularized by vendors like Fin.ai and Zendesk, charges only when the agent achieves a defined result such as a resolved ticket, so a failed or escalated attempt costs the buyer nothing even though the vendor still paid to run it.
Which AI agent pricing model is cheapest?
It depends entirely on volume, which is the point of the volume-sweep table above. Flat per-seat pricing has zero marginal cost, so at low usage it is overkill — a few usage-based dollars would have covered the same work — but at high usage it becomes the cheapest option because the usage-based models keep scaling linearly while the seat price stays flat. There is a specific crossover volume, seat price divided by the cheapest usage-based cost per interaction, below which usage-based wins and above which the seat wins. Per-outcome pricing is usually the most expensive per raw interaction of the four, because you are paying a premium for the vendor absorbing the risk of failed attempts, not for the compute itself.
Why would anyone pay more for per-outcome pricing than per-token?
Because the price is buying risk transfer, not compute. Under per-token or per-task pricing you pay for every attempt whether it worked or not, so a low success rate quietly inflates your effective cost per result — the token bill for ten failed attempts still lands on your invoice. Under per-outcome pricing the vendor eats the cost of every unsuccessful attempt and only bills you for a confirmed result, which is why the headline rate looks high per raw interaction: it has the cost of the misses baked into the price of the hits. It tends to make the most sense when success rates are uncertain, hard to audit independently, or when the buyer specifically wants budget certainty tied to results rather than effort.
How do I find the crossover volume where a flat seat price becomes cheaper?
Divide your flat monthly seat cost by the cost per interaction of whichever usage-based model is cheapest for you — usually per-token. Below that volume, the seat is sitting partly idle and a usage-based model would have cost less; above it, the seat's flat price is being spread over enough work that it undercuts the usage-based total. This calculator computes that crossover directly from your inputs and shows it as a specific number of interactions per month, alongside a full sweep so you can see exactly how the ranking of all four models shifts as volume grows from a pilot to full production.