How would you load-test a Python API and turn the result into a capacity decision?

Uses representative traffic and tail latency to find the real bottleneck and a safe operating range.

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I model the critical user flows with realistic payloads, data distribution, authentication, request mix, and think time. The test ramps through expected and peak concurrency while measuring throughput, errors, and p50, p95, and p99 latency. At the same time I capture per-worker CPU and memory, event-loop delay, connection-pool pressure, database time, and downstream limits. The goal is to find where latency or errors bend upward, not advertise one maximum requests-per-second number.

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Uses representative traffic and tail latency to find the real bottleneck and a safe operating range.

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Uses representative traffic and tail latency to find the real bottleneck and a safe operating range.
I model the critical user flows with realistic payloads, data distribution, authentication, request mix, and think time. The test ramps through expected and peak concurrency while measuring throughput, errors, and p50, p95, and p99 latency. At the same time I capture per-worker CPU and memory, event-loop delay, connection-pool pressure, database time, and downstream limits. The goal is to find where latency or errors bend upward, not advertise one maximum requests-per-second number.
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I separate load, spike, stress, and soak questions because they expose different failures. Cold starts and cache state are controlled, and the generator is proven capable of producing the requested load without becoming the bottleneck. I test the real server worker model and resource limits. If a dependency is stubbed, the result describes only the isolated service; if it is real, I protect it with stop conditions and coordination.

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Reporting only average latency
Testing a different worker model from production
Treating the load generator as infinitely capable

How would you load-test a Python API and turn the result into a capacity decision?

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