The Problem
As AI-powered products grew in popularity, engineering teams found themselves with a new challenge: understanding and controlling AI costs. Unlike traditional infrastructure costs, AI spending is distributed across multiple providers, priced by usage metrics like tokens and API calls, and often difficult to attribute to specific products or teams.
Finance teams were asking questions that engineering couldn't answer: "What did it cost to serve our enterprise customers last month?" "Which feature is driving our OpenAI bill?" "Are we on track to hit our AI budget?"