AI pricing is more confusing than it needs to be. You'll see the same capability sold three different ways by three different vendors, and the cheapest sticker price often turns out to be the most expensive in practice. Before you sign anything, it helps to understand the three dominant pricing models, what each one is actually charging you for, and which one fits your situation. Here's a clean read.
Per-seat: predictable, easy to budget, often overpaid
Per-seat pricing charges a flat monthly fee for each person who has access — typically somewhere between $20 and $60 per user per month for business-grade AI assistants. This is how Microsoft 365 Copilot, Google Workspace AI, and most chat-based AI assistants are sold. The advantage is predictability. You know exactly what you're spending each month and you don't have to monitor anything. The disadvantage is that most companies dramatically overpay because heavy users get great value and light users get almost none — but you're charged the same for both. If less than 60% of your seats are actively used each week, you're subsidizing the unused ones.
Per-token: scales with use, hard to forecast
Per-token pricing charges based on how much text the AI processes — both what you send in and what it sends back. This is how API access from the major AI labs works, and how most AI features inside custom-built software are priced underneath the hood. The advantage is that you pay only for what you use, which can be very efficient for occasional or burst use cases. The disadvantage is unpredictability. A team can run up an unexpected bill if a workflow loops, if someone runs a large batch job, or if the model gets handed a much bigger document than expected. For per-token pricing, set spending alerts before you set anything else.
Per-workflow: outcomes-based, increasingly common
Per-workflow or per-action pricing charges for completed outcomes — a meeting transcribed, a contract reviewed, a lead enriched, a support ticket resolved. This is how a growing number of specialized AI tools price themselves, especially in legal, sales, and customer support. The advantage is that the cost is tied directly to value delivered, which makes ROI easy to measure. The disadvantage is that if you run a high volume of low-value workflows, per-action pricing adds up faster than the other models. It's most efficient when each completed workflow is genuinely worth the per-action fee.
How to actually choose
Match the pricing model to the usage pattern. For broad team-wide assistants that everyone touches occasionally, per-seat is usually fine if utilization is healthy. For specific workflows with bursty or large-document use, per-token is more efficient if you watch the meter. For high-value, well-defined tasks where each output is genuinely worth real money, per-workflow makes ROI obvious. The mistake most SMBs make is buying per-seat for everything because it's simplest, then never measuring whether the seats are actually getting used. Audit utilization at the six-month mark and re-shop the tools where the math no longer works.