Standard API load tests give a false sense of security because they don't account for the unique behavior of AI workloads.
AI requests are fundamentally different from web requests due to nondeterminism, variable latency, bounded concurrency, and per-token costs.
AI systems can fail in unique ways, including latency tail explosions, response truncation, retry storms, and unexpected cost overruns.
Create effective tests by using a corpus of real prompts, modeling user think time with an open workload, and simulating client retry behavior.
Evaluate AI performance across four dimensions: latency percentiles, throughput in tokens per second, cost per request, and resilience through graceful degradation.
A standard load test with a fixed prompt and aggressive latency SLA fails for a useless reason because it misinterprets normal AI behavior.
Using a varied prompt corpus and an open workload model reveals the true latency distribution and performance characteristics of the AI service.
A stress test shows how a well-designed system sheds excess load with 503 errors and trips a cost circuit breaker instead of crashing.
Turn test results into reliability by setting AI-native SLOs, building safety mechanisms like "cancel on disconnect," and monitoring cost as a signal.
The core message is to model reality with real prompts and user behavior, measure across latency, cost, and resilience, and test continuously.
Jobs that call for the skills explored in this talk.