concurrent-load-testing-pattern

Measuring p99 latency and throughput for concurrent LLM API requests involves running deterministic asyncio benchmarks with httpx to ramp concurrency and calculate precise p50/p95/p99 distributions alongside RPS.

Your API load testing shows misleading average latency because it hides tail latencies and often hits rate limits. Using p50/p95/p99 metrics with multi-key distribution avoids RPM throttling and reveals the true throughput ceiling.

Finding the RPS ceiling of an LLM gateway without rate limit distortion requires distributing requests across multiple API keys and ramping concurrency until detecting the throughput knee using p99 latency and success rate.

You can use locust to cross-validate asyncio httpx benchmarking results by running distributed verification to generate dashboard-friendly reports and confirm capacity metrics captured during deterministic async tests.

You need multiple API keys for reliable concurrency load testing because distributing requests via a KEY_LIST avoids RPM throttling distortion, ensuring accurate capacity assessment and sustainable concurrency decisions.

Setting SLOs for LLM serving using capacity testing metrics involves combining RPS/TPS, success rate, and p99 latency with basic GPU/memory signals during load ramping to decide maximum sustainable concurrency levels.