Stripe wants to turn your AI costs into a profit center

Stripe on Monday released a preview of a new feature that could help AI startups (and other companies) solve the problem of passing through the underlying costs of AI model usage to their customers. Stripe’s feature, however, goes even further than just passing through the costs of the tokens. It allows startups to charge a markup percentage on token usage. So a company can, for instance, charge an automatic 30% above the cost of the tokens that the startup will pay the model maker. As Stripe described it, “Say you’re building an AI app: you want a consistent 30% margin over raw LLM token costs across providers. Billing automates the process.” The billing feature lets the startup pick the AI models it uses. It tracks the API prices of those models. It then records the customers’ token usage and applies the profit-margin markup automatically. As we’ve previously reported , there are a variety of ways that AI startups are charging for their wares. Many of them charge tiered monthly subscriptions that have usage-rate caps; once those are hit, the subscriber may be charged more for exceeding the limit. For instance, Cursor last year changed the pricing on some of its tiers from unlimited use to rate-limited usage, with fees for extra consumption on top. Without a usage cap, users could run up big bills for a startup with the model makers, and force the startup to operate in the red. This is especially acute for agentic startups. The more their customers use their agents, the more tokens they consume from the underlying model provider, be that OpenAI, Google Gemini, Anthropic or others — making pricing and business model decisions especially critical. Techcrunch event Disrupt 2026: The tech ecosystem, all in one room Your next round. Your next hire. Your next breakout opportunity. Find it at TechCrunch Disrupt 2026, where 10,000+ founders, investors, and tech leaders gather for three days of 250+ tactical sessions, powerful introductions, and market-defining innovation. ...