Tracking Tokens Per Feature, Not Per Model, Is the Key to Cutting LLM Costs

A developer discovered that rising LLM API bills could not be explained by provider dashboards, which only group usage by model, API key, or time period. The real insight came from attributing every LLM call to a specific product feature — such as chat, document summarization, or a reporting agent — rather than looking at total token consumption. By logging structured usage records with fields like feature name, operation type, token counts, and latency, it became possible to pinpoint exactly which part of an application was driving cost increases. This approach revealed actionable problems, such as a classifier receiving an oversized system prompt or an agent repeatedly retrying failed tool calls. The method requires no changes to the model provider and can be implemented with a lightweight logging layer in the application itself.