AI Cost Governance: Proven Controls to Stop Costly Waste
An AI budget behaves nothing like a software budget: it moves the moment somebody writes a longer prompt, enables a more capable model or ships an agent that retries five times instead of once. This guide sets out the controls that keep inference spend predictable, covering token budgets at request, session and tenant level, a capability ladder and routing strategy that sends each task to the cheapest model that can do it, hard and soft usage limits that stop runaway agent loops, prompt caching and context discipline, tagging and unit-cost dashboards, the monthly operating cadence, and a fully worked support-copilot example.
