performance-audit

To profile API latency, you need a baseline measurement under production-like workloads to identify slow paths. A phased profiling workflow guides you through measuring, identifying bottlenecks, fixing, and verifying improvements against clear success criteria.

Measuring performance baselines for microservices requires running profiling under production-like workloads to collect initial metrics. This baseline establishes a reference point to compare and verify latency and memory improvements after optimizations.

Yes, profiling diagnostics apply to batch jobs under production-like workloads to locate slow paths and memory inefficiencies. By following a phased workflow, you can measure baselines, identify bottlenecks, and verify optimizations to reduce p95 latency.

Clear success criteria are required for performance optimization to define measurable targets, such as reducing p95 API latency from 350ms to under 200ms. Without them, you cannot effectively verify whether optimizations actually improve the baseline.

To find memory growth bottlenecks, use diagnostic profiling on a live service to collect baseline metrics. The phased workflow guides you through identifying memory inefficiencies and verifying fixes against the initial measurement.