Cost & Latency Optimization

Cost and latency optimization reduces the price and wall-clock time of serving an LLM system while keeping quality above a defined threshold. The core levers: route simpler requests to smaller/cheaper models and reserve the strong model for hard cases; add prompt caching to avoid re-paying for stable context; tighten prompts and cap output tokens; and stream responses to cut perceived latency.

For non-interactive, high-volume work — bulk grading, dataset labeling, offline scoring — the Batch API is the big win: about 50% cheaper with results returned within 24 hours, since you trade immediacy for throughput. This is ideal for running large llm-as-judge eval jobs where a day of turnaround is fine.

Every optimization is a tradeoff against quality, so it must be validated with your eval suite, never assumed. Track cost-per-request and latency percentiles (p50/p95/p99) alongside quality scores, and only ship a cheaper configuration when regression-testing confirms scores held. Distinguish latency (per-request time) from throughput (requests per unit time) — batching improves throughput and cost while increasing per-request latency.