AI FinOps
AI FinOps brings cloud FinOps discipline — cost attribution, budgeting, and forecasting — to AI and LLM spend.
AI FinOps applies the FinOps operating model — the collaborative practice that finance, engineering, and product teams use to manage cloud costs — to spending on AI and large language models. It covers the full lifecycle: making AI spend visible, attributing it to owners, budgeting for it, and forecasting where it is headed.
In practice, "AI FinOps" is often used loosely to mean looking at the monthly AI bill and asking why it grew. That is a starting point, but it is reactive. The operational version of the discipline works at the unit level — the token — where you can attribute and enforce spend before it happens rather than explaining it afterward.
AI FinOps vs. AI Token FinOps
AI Token FinOps is the specific, proactive form of AI FinOps that operates at the token level and enforces budgets on the request path. Where broad AI FinOps might tell you that you spent $50,000 on a provider last month, AI Token FinOps tells you exactly which user, project, and session spent it — and can stop an overrun before it reaches the invoice.
Frequently asked questions
What is the difference between AI FinOps and AI Token FinOps?
AI FinOps is the broad discipline of managing what an organization spends on AI, mostly from the monthly provider bill. AI Token FinOps manages AI cost at the unit level, the token, in real time. AI FinOps says you spent $50,000 on OpenAI last month; AI Token FinOps shows which user, project, and session spent it, and enforces hard budgets before the spend happens.