API Performance Testing Best Practices
Good API performance testing is less about exotic tools and more about discipline: realistic requests, clear SLOs, controlled RPS, and comparable reports.
Use this checklist as your default playbook.
1. Define success before you generate traffic
Write pass criteria in one line:
At 200 RPS for 10 minutes with 60s ramp-up, successful p95 < 250 ms, errors < 0.5%, timeouts < 0.1%.
Without this, every run becomes a debate.
2. Prefer staging that mirrors production
Match instance sizes, connection limits, cache configuration, and auth paths as closely as you can. Document gaps so you do not over-claim.
3. Author realistic HTTP requests
Start from production traces or a known-good curl / Postman snippet. Include required headers and a representative JSON body. Avoid empty happy paths that skip validation or DB work.
4. Think in RPS, ramp-up, and duration
- RPS — arrival rate you care about
- Ramp-up — time to reach that rate
- Duration — hold long enough to exit warmup
Calculate RPS from traffic, not vibes — see How to Calculate RPS.
5. Climb a ladder
Smoke → baseline → target → optional stress. Stop when SLOs fail; fix bottlenecks; continue. Jumping straight to peak hides the first breaking layer.
6. Watch percentiles and errors together
Track p95/p99 on successful responses and error/timeout rates. Hitting target throughput with rising 5xx is not a pass. Background: p95 and p99.
7. Keep observability open during the run
Dashboards for CPU, memory, DB connections, lock waits, queue depth, and downstream latency turn “it got slow” into “the pool saturated at 2k RPS.”
8. Change one variable between comparisons
Same request, same RPS profile, one code or infra change. Export PDFs so reviews share the same artifact.
9. Verify ownership and respect safety
Only test hosts you control. Use domain verification. Prefer staging. Coordinate production game days. Traffic is real.
10. Re-run after meaningful deploys
A launch-week hero run goes stale. Keep a short regression profile in the team workspace and re-run when risk is high.
11. Separate Plan capacity from vanity peaks
Know your tool and plan limits (max RPS, duration, monthly request quota). In Loadcurl, a start holds roughly duration × RPS. Check Usage before large rehearsals.
12. Share results in the language of the product
Translate metrics for stakeholders: “We can sustain expected launch peak with p95 under budget” beats “p99 was 412.”
A minimal recurring profile
| Step | RPS | Duration | Purpose |
|---|---|---|---|
| Smoke | 10 | 2 min | Sanity |
| Baseline | 50% peak | 5 min | Trend |
| Peak | 100% peak | 10 min | SLO check |
Store the curl, the criteria, and the last PDF next to the service runbook.
How Loadcurl supports the practice
Loadcurl keeps the loop tight: verify domain → paste curl / compose request → set RPS profile → poll live run → read latency/throughput report → export PDF. Personal workspaces work for solo engineers; company workspaces share tests, domains, and quota.
Docs: getting started, report card.
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