How do we avoid flaky performance checks in CI?

Stabilize the synthetic environment: fixed network emulation, warmed caches between runs, and 3–5 Lighthouse runs with median selection. For load tests, run on dedicated runners with pinned regions and cap background noise. Use relative budgets (no more than +10% over main) to absorb natural variance.

What’s a good starter budget for a typical e-comm SPA?

Field (RUM): LCP < 2.5s p75, INP < 200ms p75, CLS < 0.1 p75. API: p95 < 800ms for cart/search, error rate < 1%. Synthetic: Lighthouse perf ≥ 0.9 on critical pages. Adjust after 2–4 weeks of baselining.

How do we tie performance to revenue credibly?

Instrument conversion and funnel analytics with performance buckets (e.g., LCP 0–1s, 1–2s, 2–4s). Compare conversion across buckets, controlling for channel/device. We’ve seen consistent 5–10% conversion gaps between fast and slow cohorts; share those graphs with Finance.

Do we need a separate performance environment?

If prod data is sensitive or your infra can’t take load, yes—stand up a perf env with prod-like topology and data volume. Otherwise, safe windows in staging + controlled canaries in prod give the best signal. Never blast prod without rate limits and kill switches.

What if our architecture is the bottleneck (e.g., chatty microservices)?

Prove it with traces (OpenTelemetry) and p95 hop counts under load. If calls per request > 12 and p95 blows up at peak, introduce edge aggregation, async pipelines, and cache hot paths. Automate a gate: reject merges that add new cross-service hops on critical flows without a compensating cache or batch plan.

Performance-optimization · Oct 10, 2025 · 9 minute read

Stop Guessing: Automate Performance Tests That Prove Your Speedups (or Kill Them Fast)

Q: What’s a good starter budget for a typical e-comm SPA?

Field (RUM): LCP < 2.5s p75, INP < 200ms p75, CLS < 0.1 p75. API: p95 < 800ms for cart/search, error rate < 1%. Synthetic: Lighthouse perf ≥ 0.9 on critical pages. Adjust after 2–4 weeks of baselining.

Q: How do we tie performance to revenue credibly?

Instrument conversion and funnel analytics with performance buckets (e.g., LCP 0–1s, 1–2s, 2–4s). Compare conversion across buckets, controlling for channel/device. We’ve seen consistent 5–10% conversion gaps between fast and slow cohorts; share those graphs with Finance.

Q: Do we need a separate performance environment?

If prod data is sensitive or your infra can’t take load, yes—stand up a perf env with prod-like topology and data volume. Otherwise, safe windows in staging + controlled canaries in prod give the best signal. Never blast prod without rate limits and kill switches.

Q: What if our architecture is the bottleneck (e.g., chatty microservices)?

Prove it with traces (OpenTelemetry) and p95 hop counts under load. If calls per request > 12 and p95 blows up at peak, introduce edge aggregation, async pipelines, and cache hot paths. Automate a gate: reject merges that add new cross-service hops on critical flows without a compensating cache or batch plan.

If you can’t prove it with LCP, INP, and p95, it’s not an optimization—it’s a hunch. Here’s how to wire automated performance tests that validate improvements and translate to business impact.

Alex R. Mercer

Partner, Performance & Reliability, GitPlumbers

20+ years shipping and fixing distributed systems at scale. Led SRE and platform teams through three replatformings, two cloud bills, and more 3 a.m. incidents than he cares to admit.

If you can’t prove it with LCP, INP, and p95, it’s not an optimization—it’s a hunch.

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Stop Guessing: Automate Performance Tests That Prove Your Speedups (or Kill Them Fast)

Key takeaways

Implementation checklist