Product teams ship AI features faster than anyone tracks what they cost. Six months in, you have a dozen AI-powered features live and a single AI bill that does not tell you which of them is worth keeping. Feature-level spend tracking is how you find out.
Why feature attribution is worth the effort
Features are where product decisions get made. Knowing that the whole product spent a certain amount on AI is a finance fact; knowing that the summarization feature costs four times what the autocomplete feature costs, for a fraction of the usage, is a product decision. Feature-level tracking connects AI cost to the roadmap.
The mechanism
Attribute each AI call to the feature that triggered it, using a feature tag in the request metadata or a per-feature wrapper. Because a single feature may make several model calls and several tool calls per interaction, group them by a feature identifier and a session identifier so the cost you see is the cost of the feature doing its whole job, not one call in isolation.
What it reveals
Feature tracking surfaces the expensive features hiding behind cheap-looking calls, the features whose cost per use is rising as they get more capable, and the features that consume real budget while driving little engagement. It also reveals the opposite: low-cost features delivering outsized value that deserve more investment.
Cost per feature outcome
The most useful view is cost against what the feature produces. A feature that costs a few cents per successful outcome and drives retention is cheap at any volume. A feature that costs the same but produces output nobody uses is pure waste. Tracking by feature, then dividing by the outcome, is what tells the two apart.
Every AI feature is a small business with its own unit economics. Tracking spend by feature is how you run each one on evidence instead of enthusiasm.