What’s the simplest way to start if we’ve never done real load testing?

Pick one critical flow (e.g., search → PDP → checkout), define a user-facing SLO (p95 < 400ms, <0.5% errors), build a k6 script with realistic think time, and run it against staging with production-like data. Add thresholds, wire to Prometheus, and set a CI gate so regressions fail PRs.

Do we need production traffic mirroring?

It’s great but not required. If you can’t mirror, ensure staging has realistic data, warmed caches, and identical autoscaling policies. For third parties, stub responses with realistic latency and error rates. Use synthetic spikes to emulate promos.

How do we tie performance to business impact?

Track conversion elasticity versus p95/p99 for your critical paths (historical experiments help). Publish $/req and revenue per ms dashboards. When p95 improves, check conversion, churn, and NPS for correlated movement.

Which tool should we pick: k6, Locust, Gatling, or Artillery?

Use what your team will maintain. k6 has great CI ergonomics; Locust (Python) is nice for stateful user flows; Gatling shines at high concurrency with Scala; Artillery is lightweight JS and good for API + websockets. The tool is less important than realistic scenarios and automated enforcement.

Performance-optimization · Oct 24, 2025 · 10 minute read

Load Testing That Actually Predicts Production: Validating Behavior Under Real Stress

Q: Do we need production traffic mirroring?

It’s great but not required. If you can’t mirror, ensure staging has realistic data, warmed caches, and identical autoscaling policies. For third parties, stub responses with realistic latency and error rates. Use synthetic spikes to emulate promos.

Q: How do we tie performance to business impact?

Track conversion elasticity versus p95/p99 for your critical paths (historical experiments help). Publish $/req and revenue per ms dashboards. When p95 improves, check conversion, churn, and NPS for correlated movement.

Q: Which tool should we pick: k6, Locust, Gatling, or Artillery?

Use what your team will maintain. k6 has great CI ergonomics; Locust (Python) is nice for stateful user flows; Gatling shines at high concurrency with Scala; Artillery is lightweight JS and good for API + websockets. The tool is less important than realistic scenarios and automated enforcement.

Stop microwaving toy benchmarks. Model real users, wire tests to SLOs, and optimize for the metrics your CFO cares about.

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Load Testing That Actually Predicts Production: Validating Behavior Under Real Stress

Key takeaways

Implementation checklist