Budget operations
Monthly burn forecast for LLM spend: simple guardrails that work
A small burn-rate routine can prevent month-end surprises and improve plan decisions before spend overruns.
Full guide: LLM budget alert policy: thresholds and escalation
Forecast cadence
- Daily projected month-end spend
- 2x and 3x baseline burn-rate checks
- Top cost drivers by endpoint and tenant
- Escalation owner when projected overrun exceeds threshold
Burn-rate beats guesses (how to avoid false confidence)
Forecasting is only useful if it triggers action early. Burn-rate checks detect drift even when the absolute spend number is still small.
Track both spend/day and cost/request. A stable spend/day with rising cost/request usually means a deploy or prompt regression.
- spend/day vs baseline spend/day
- cost/request vs baseline cost/request
- endpointTag concentration changes
- tenant concentration changes
What improves forecast quality
- Stable endpoint tagging discipline.
- Prompt-version tracking on every deploy.
- Separate demo/test traffic from real usage.
- Consistent reconciliation with provider usage exports.
Weekly review template (15 minutes)
- Top 5 endpointTag drivers (total + delta vs last week).
- Top 5 tenants/users by spend and concentration percentage.
- PromptVersion changes shipped this week + cost/request deltas.
- Retry ratio and latency trend (multiplier detection).
- One owner action: cap, throttle, route, rollback, or reprice.
A simple forecast formula you can operationalize
- Projected month-end spend = (Spend so far / days elapsed) * days in month.
- Add a traffic adjustment when volume is trending up or down.
- Compute burn-rate by endpointTag and tenant, not just totals.
- Escalate when projection crosses warning threshold, not when it is too late.
- Log every forecast change with the driver (deploy, abuse, volume shift).
What to alert on
- burn-rate acceleration vs baseline
- endpointTag concentration changes in short windows
- unexpected tenant concentration in Top Users
- budget warning, spend-alert, and exceeded state transitions
Execution checklist
- Confirm alert is real: dataMode, environment, and time window.
- Identify dominant endpointTag and tenant/user contributors.
- Contain: cap output, lower max tokens, or throttle non-critical paths.
- Assign one incident owner and one communication channel.
- Update policy thresholds or ownership to prevent repeat incidents.
FAQ
Is userId required?
No. userId is optional, but recommended for tenant-level attribution. If needed, send a hashed identifier.
Where should token usage values come from?
Prefer provider usage fields first. If unavailable, use tokenizer estimates and mark uncertainty in your workflow.
How should retries be handled?
Keep the same externalRequestId for the same logical request so idempotency remains stable across retries.
Can telemetry break production flow?
It should not. Use short timeouts, catch errors, and keep telemetry asynchronous so provider calls keep running.
Related guides
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