How to Price AI Products: Tokens, Credits and Outcome-Based Models

Why AI pricing is different

AI products have real marginal cost per request. A flat seat price can turn a heavy user unprofitable. Pricing has to protect margin while staying simple enough for buyers to understand.

Four models for AI pricing

• Per-token: transparent, maps to cost, but hard for non-technical buyers.
• Per-request or per-task: simpler, but varies in underlying cost.
• Credits: one currency that maps to many actions and models.
• Outcome-based: charge per resolved ticket or generated asset; strongest value alignment.

Protecting gross margin

Track cost of goods per event alongside the price. Set credit burn rates per model so expensive models consume more credits. Review margin by customer monthly.

A step-by-step approach

• Measure inference cost per action.
• Pick the unit customers already talk about.
• Package as credits with a platform fee.
• Add auto-reload and usage alerts.
• Revisit rates each quarter as model costs fall.