LLM budget governance
LLM budget governance should prevent invoice surprises without creating a permission bottleneck for every AI feature. The goal is guardrails and ownership, not central approval for every prompt change.
A good budget model separates experimentation, production traffic, evals, and background jobs. Each category needs a different tolerance. Experimentation can be capped tightly. Production needs alerts and graceful degradation. Evals and enrichment can often move to batch lanes.
Governance primitives
- Monthly budgets by team and product surface.
- Daily burn-rate alerts for fast-moving workloads.
- Hard caps for experiments and test environments.
- Router policies that degrade to cheaper models where safe.
- Review cadence tied to invoice reconciliation.
Policy design
Policies should be specific enough to act on. “Reduce LLM spend” is not a policy. “Support summarization must use batch for nightly reprocessing,” “RAG context is capped at N chunks unless approved,” and “frontier model use requires a route reason” are policies teams can implement.
Budget governance works when it is visible in the engineering workflow. Owners should see spend impact near deploys, prompt changes, and route-policy edits rather than waiting for finance to send a month-end report.