Inference Cost Control Playbook (2026)

Use this checklist to cut AI inference spend while keeping quality and latency stable. It’s designed for founders, engineers, and operators running LLM features in production.

1) Define the unit of value
- Pick one “successful outcome” metric per workflow (e.g., ticket resolved, note generated and accepted, SQL query executed correctly).
- Track $ per successful outcome, not just $ per request.

2) Instrument the real cost drivers
Log these per request and aggregate weekly:
- Input tokens, output tokens, total tokens
- Model/route chosen, fallback count
- Tool calls (count + duration), retrieval top-k and context tokens
- Cache hit/miss (semantic + exact)
- End-to-end latency (p50/p95) and error rate

3) Set budgets and enforce them
- Max turns per session (e.g., 6)
- Max tool calls (e.g., 2)
- Max input tokens and output tokens (per workflow)
- Add automated tests that fail if prompts exceed budgets.

4) Build a conservative router
Start with three lanes:
- Small model lane: low-risk, routine tasks
- Frontier lane: high-stakes or complex tasks
- Escalation lane: human review or “judge” model
Gate on request type, user tier, risk signals, and predicted output length.

5) Implement caching in the highest-repeat flows
- Start with semantic caching for summaries/rewrites/templates.
- Target 15% hit rate in week one, 25%+ by month one.
- Add cache keys that normalize prompt templates and strip volatile fields.

6) Reduce retrieval cost before changing models
- Tighten chunking, filters, and top-k.
- Add reranking to keep quality while shrinking context.
- Track retrieval context tokens as a first-class metric.

7) Constrain output length by default
- “Concise by default” system instruction.
- Hard max_tokens caps.
- Only generate long-form on explicit user action (expand/click).

8) Stop runaway agents
- Cap retries and loops.
- Require a reason code for each retry (timeout, tool failure, low confidence).
- Escalate gracefully instead of iterating endlessly.

9) Capacity plan like a production service
- Separate base load from burst load.
- Use reserved/dedicated capacity only after demand is stable.
- Review utilization weekly; aim for a defined utilization band that preserves p95.

10) Run a weekly “cost + quality” review
Agenda (30 minutes):
- Top 5 cost outliers by customer/workflow
- Changes in $/successful outcome, tokens/session, tool calls/session
- Any p95 latency regressions or quality eval regressions
- Next week’s 2–3 optimization experiments with expected % impact

If you can’t explain where your last 20% cost increase came from in under 10 minutes, you don’t have an inference system—you have a demo in production.