Tokenomics

Tokenomics — token economics — is the practice of treating the token as the unit of account for AI spend. Because model providers bill per input and output token, the token is where cost is actually generated; measuring, attributing, and forecasting at that level is what turns an unpredictable AI bill into a budget you can plan against.

In the AI context, tokenomics is narrower and more operational than the loose "crypto tokenomics" usage: it is about the economics of LLM usage — how many tokens a request consumes, what each token costs across models and providers, and how that cost rolls up to a user, project, session, or cost center. Getting this right is the prerequisite for chargebacks, budgets, and forecasts.

How Behest applies tokenomics

AI Token FinOps is Behest's approach to tokenomics: it applies FinOps for AI at the token and request level. Behest meters every model call on the request path — model, tokens, and cost — and attributes it to an owner in real time, then enforces hard token or dollar budgets before a request runs. That makes token-level economics not just observable after the fact, but controllable before the spend happens.

Where broad tokenomics tells you what AI usage costs, AI Token FinOps is the enforcement layer that lets you act on those costs — the difference between reconciling AI spend and controlling it.