What’s the biggest mistake teams make with load testing?

Optimizing for throughput (RPS) instead of **user-facing latency percentiles** and failure modes. Production incidents are usually p99 + retries + saturation, not “we didn’t hit enough RPS.”

Do I need a perfect production clone to get value?

No, but you need production-like **constraints**: same DB engine/version, realistic dataset cardinality, the same autoscaling/resource limits, and the same critical dependencies (or controlled simulators). Otherwise you’ll find the wrong bottleneck.

k6 vs Locust vs Gatling—does the tool matter?

Less than you want it to. Pick one your team will maintain. k6 is great for CI-friendly scripting and thresholds; Locust is great if your org is Python-heavy; Gatling shines in JVM shops. The strategy (workload model + SLIs + gates) matters more.

How do you connect performance improvements to business impact?

Measure funnel metrics alongside SLIs: checkout success rate, abandonment, conversion, revenue/hour. Then correlate changes in p95/p99 and error rate during peak tests (and real peaks) to those funnel shifts.

Performance-optimization · Dec 28, 2025 · 7 minute read

Your Load Test Passed. Production Still Melted. Here’s the Strategy That Actually Predicts Pain.

Load testing isn’t about bragging rights on RPS. It’s about protecting p95 latency, checkout success, and revenue when your marketing team “surprises” you with traffic.

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Your Load Test Passed. Production Still Melted. Here’s the Strategy That Actually Predicts Pain.

Key takeaways

Implementation checklist