AI cost optimization checklist
AI cost optimization is not a single project. It is a sequence of repeatable steps. This checklist covers the most reliable levers for 2026, ordered by impact and risk.
Before you optimize
- Reconcile to the invoice. Gateway logs under-count. Use the provider invoice as ground truth.
- Find the top five endpoints. ~80% of cost usually lives on 3–5 endpoints. Ignore the long tail at first.
- Define quality baselines. Cost savings that hurt quality are not savings. Agree on the metrics that matter before testing.
The optimization levers
- Prompt caching — 50–90% off cached tokens, lowest implementation effort.
- Model routing — send easy queries to cheaper models, hard queries to frontier models. 30–50% reduction.
- Semantic caching — reuse similar responses for RAG and support flows. 20–40% reduction on cache-friendly endpoints.
- Prompt compression — strip redundancy and move static context to system prompts. 10–25% reduction.
- Batch routing — move non-urgent work to batch APIs. Flat 50% off eligible volume.
- Provider arbitrage — use equivalent models priced lower on other providers. 30–60% reduction when A/Bs pass.
Governance checks
- Set per-request and per-user cost caps.
- Add alerts for budget variance and anomalous spend.
- Re-run A/B tests monthly; models and prices change.
- Document which optimizations are live and their measured savings.
For a deeper explanation of each lever, see AI cost optimization.
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