The pricing economics of AI agents
How to price and bill an AI product so every customer stays profitable, even when each one costs you something different to serve.
The pricing economics of AI agents
What's inside
Why agent economics are different
Why cost moves with usage, model and task shape, and why falling token prices do not bring cost per task down at the same rate.
Where margin leaks
Seven predictable gaps between what you charge and what you pay, and how to find the heavy users who carry most of your provider cost.
Choosing what to charge for
How to compare pricing models, choose a unit you can cost, and keep outcome and credit pricing profitable.
Plan structures that protect margin
Hybrid plans, guardrails and cost floors, with three worked plans and the arithmetic behind them.
Running pricing as a system
Attributing provider cost to customers, the margin metrics to review each week, accounting for prepaid credits, and repricing without losing trust.
Who it's for
Founders of AI agent companies, and the product, finance and engineering leads who work with them on pricing.
Written by
Manish Balaji