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Common API Performance Testing Mistakes

4 min read
Loadcurl
Loadcurl
Loadcurl Team

API performance tests fail quietly when the methodology is wrong. The dashboard turns green, the PDF looks busy, and nobody notices the results are not trustworthy.

Here are the mistakes we see most often — and how to avoid them.

1. Testing the wrong environment​

Hitting production “just for a minute” without coordination risks real users. Hitting a toy staging box that is 1/20th of production teaches you nothing about capacity.

Do this instead: Use staging sized like production for capacity claims. If you must touch production, treat it as a game day with ramp-up, limits, and on-call.

2. Skipping ramp-up​

Slamming from 0 to peak RPS instantly mixes cold-start effects with true capacity limits. Connection pools, JIT, caches, and autoscalers need time.

Do this instead: Set ramp-up (often 30–120 seconds) and ignore or annotate the first portion of the run when comparing percentiles.

3. Trusting average latency​

Averages hide the tail. A 50 ms average with a 3 second p99 still times out clients and triggers retries — which create more load.

Do this instead: Track p95 and p99, preferably on successful responses. Read What Is p95 and p99 Latency?.

4. Unrealistic payloads​

Empty bodies, missing auth, or hardcoded IDs that always hit a warm cache can make an API look infinitely scalable.

Do this instead: Mirror production headers, auth, and body shapes. Vary IDs carefully if that matches real traffic — without accidentally becoming a write amplification stress test you did not plan.

5. Changing too many variables​

You upgraded the DB, changed the query, and doubled RPS in one run. Now you cannot explain the delta.

Do this instead: One meaningful change per comparison. Keep RPS, duration, and payload constant when validating a fix.

6. Ignoring HTTP status mix​

“We hit target RPS” is meaningless if half the responses are 500s. Throughput without correctness is just noise generation.

Do this instead: Gate success on error rate and timeouts alongside throughput and latency.

7. Too short a duration​

A 20-second run may never leave warmup or catch saturation that appears at minute five.

Do this instead: Hold target RPS long enough for queues and GC to show up — often several minutes for load tests, longer for soak.

8. Forgetting client-side limits​

Your generators, laptop, or office network may be the bottleneck — not the API. Local tools especially hit file descriptor and bandwidth ceilings.

Do this instead: Prefer cloud load generators for serious profiles, and verify achieved RPS before blaming the server. See Cloud-Based vs Local Load Testing.

9. No pass/fail criteria​

Without thresholds, every report becomes a subjective debate.

Do this instead: Write criteria first: e.g. “150 RPS for 10 minutes, p95 < 300 ms on successful calls, errors < 0.5%.”

10. Not verifying ownership / permission​

Sending load at hosts you do not control is unethical and often illegal. Tools that require domain verification exist for a reason.

Do this instead: Verify domains, use staging hostnames you own, and keep credentials scoped. Loadcurl only targets verified hosts for this reason.

11. Treating one endpoint as the whole product​

The hottest GET might be fine while a dependent auth or search service melts.

Do this instead: Identify fan-out. Load test the critical path and the shared dependencies that amplify under traffic.

12. Never re-running a baseline​

A single heroic run before launch goes stale after the next deploy.

Do this instead: Keep a small regression profile and re-run after meaningful changes. Compare PDFs side by side.

Quick checklist before Start​

  • Staging (or approved production game day)
  • Realistic request
  • Target RPS calculated, not guessed wildly
  • Ramp-up and sufficient duration
  • Pass criteria written down
  • Observability open
  • Quota available (duration × RPS)

Go deeper​

Browse all

  • Best practices · checklist

    Pre-Launch Load Testing Checklist

    A practical pre-launch API load testing checklist: domain verify, RPS targets, SLOs, staging safety, quota, reports, and abort criteria.

  • Best practices · API performance

    API Performance Testing Best Practices

    Battle-tested API performance testing best practices: environments, RPS ladders, SLOs, realistic requests, observability, and repeatable reports.

  • Fundamentals · load testing

    Load Testing vs Stress Testing vs Performance Testing

    Clear definitions of load, stress, and performance testing for APIs — when to use each, what metrics matter, and how to design safe test profiles.