Why I tried to kill token billing (and why we kept it)
Source
stripe.com
Date
Key takeaways · AI-distilled
The post argues per-tokenThe chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.Full definition → invoices expose your model mix and markups, defining your value as the gap over someone else's model costs. As models get cheaper and interchangeable, customers can challenge that markup or route around you.
Its suggested middle step is unified credits: keep token-level metering internally for routing and margin tuning, while customers burn down one credit balance per product operation, so the invoice shows work done rather than models used.
It separates three pricing metrics: inputs (costs such as tokens), outcomes (customer results like revenue) and outputs (objective, countable work such as a generated image), and argues outputs align AI pricing with value best today.
Outcome pricing, the author argues, needs monopoly-like control of the telemetry or very large contracts; otherwise attribution is disputed, such as whether an AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → or a sales rep closed a deal. Fin's per-resolution price is framed as an output.
Adoption can flip abruptly: hybrid seat-plus-usage pricing went largely unused for about 18 months, then by August 2026 roughly one in six Stripe users past key revenue milestones were using or rolling it out.
Terms in this piece · Glossary
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
Why it matters
Explains why passing model-token costs through to customers weakens pricing, and why token metering should stay a backend tool for tracking usage and margins when you build AI products.
