Token Debt: Why FinOps for Agentic AI Is an Engineering Problem, Not a Model Choice
Why the next chapter of FinOps is not about finding a cheaper model. It is about engineering systems that do not waste the tokens they already have.
A finance leader opens the monthly invoice for the company's AI platform and finds a number that does not match any story anyone can tell. Usage grew modestly. The bill grew sharply. Nobody switched to a pricier model. Nobody approved a new integration that anyone remembers. The line item simply grew on its own, the way cloud bills used to grow before anyone built a discipline around watching them.
Ask the engineering team what happened and the answer is rarely a single cause. It is a hundred small decisions: a system prompt that grew every time someone patched in a new rule, a retrieval step that fetches ten documents when two would do, an agent that retries a failing tool call five times before giving up, a workflow that hands a conversation between three specialized agents and resends the full history at every handoff. None of these decisions looked expensive in isolation. Together, they are the bill.