How to Test LLM Backend Performance with Service Mocking

LLM application testing should extend beyond model capability benchmarks and training concerns to emphasize production performance, including latency, throughput, error rates, saturation, rate limits, and cost. Using an example application with OpenAI chat completion and image-generation features, the discussion shows that external model calls can account for most request time, with chat responses around 1.5 seconds and image generation near 10 seconds, making endpoint context and asynchronous page design important. It recommends capturing real API traffic, then replaying it through service mocks to create repeatable tests that isolate application behavior from nondeterministic LLM responses, provider rate limits, outages, and per-request charges. Load tests can then increase virtual users, adjust infrastructure settings such as replicas and CPU or memory allocation, and inject failures to identify scaling limits and resilience weaknesses. Results should be evaluated through SRE-style golden signals and response-time percentiles, while teams balance model quality against speed and operational cost when selecting models for production.

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