Every AI feature has a marginal cost that scales with usage, unlike every other software feature you have shipped. Token costs are real, and a popular AI feature can quietly turn a profitable product unprofitable. The trap is measuring success in usage instead of in resolved tasks per dollar.
The right unit of measurement
Usage is a vanity metric for AI features. The honest metric is cost per resolved task: the total inference cost divided by the number of user tasks the AI actually completed. A feature with high usage and low resolution is a money pit. A feature with modest usage and high resolution is a moat.
What changes when you measure it
You stop adding tokens and start adding judgement. You route easy queries to cheap models and reserve the expensive model for the queries where it makes a measurable difference. You add a "did this resolve your task" feedback loop, because without it you have no denominator. You renegotiate provider contracts based on real usage instead of forecasted usage.
The pricing implication
Once you know cost per resolved task, you can price the feature properly. Outcome-based or task-based pricing aligns with the unit economics. Per-seat pricing for a feature with variable marginal cost is a slow leak.