Why your API tests are flaky — and how to actually fix it

A flaky test is worse than no test. A missing test is an honest gap. A flaky one lies to you — it goes red when nothing's wrong, and eventually your team learns to ignore red entirely. The day a real regression turns the suite red, everyone shrugs and merges. That's how flakiness quietly destroys the whole point of testing.

API test suites are especially prone to it, and after chasing this across a few projects, almost every flaky API test I've seen traces back to one of four causes. Here they are, worst first, with what actually fixes each.

1. Live dependencies

This is the big one. Your API calls a database, a queue, a third-party service. Your test calls your API. So your test now depends on all of those being up, fast, and returning the same thing every time — and they aren't. A slow downstream, a rate limit, a sandbox that resets nightly, and your test goes red for reasons that have nothing to do with your code.

Fix: mock the dependencies. Not the API under test — its downstream calls. Once the database and external services are replaced with deterministic mocks, the test only fails when your behaviour changes. This single change removed most of my flakiness. Tools that auto-generate mocks from real traffic (rather than making you hand-write a mock server per dependency) make this cheap enough to actually do everywhere.

2. Non-deterministic data

created_at timestamps. UUIDs. Auto-increment IDs. Anything generated fresh on each run. If your assertion compares the whole response and the response contains a timestamp, it will never match twice.

Fix: normalize the noisy fields — freeze time, ignore or template out random IDs — so the comparison is against the stable, meaningful parts of the response. Good tools do this for you; if yours doesn't, you're hand-maintaining a list of "fields to ignore," which rots.

3. Test order and shared state

Test A creates a record, Test B assumes it's there, Test C deletes it. Run them in a different order — or in parallel — and they collide. These pass locally and fail in CI (or vice versa), which is the classic flaky signature.

Fix: each test owns its state. It sets up what it needs and doesn't depend on another test having run first. Recorded/replayed suites help here because each recorded interaction is self-contained, but the principle holds for any suite: no shared mutable state between tests.

4. Real timing and network

Fixed sleep(2) waits that are sometimes too short. Assertions that race against async processing. Anything that assumes the network took a specific amount of time.

Fix: replace fixed waits with explicit conditions, and — again — take the real network out of the hot path by replaying against mocks, so timing is deterministic instead of dependent on how loaded the machine is that day.

The pattern underneath all four

Notice the common thread: flakiness is non-determinism. Every fix above is really "remove a source of variance so the same input always produces the same result." Live dependencies, random data, shared state, real timing — all four are just variance leaking into your test. Kill the variance and the flakiness goes with it.

This is also why, when I evaluate an API testing tool now, reliability is the first thing I check, not the last. A tool that generates thousands of tests but can't run them deterministically has handed you thousands of future flaky failures. If you're weighing options, it's worth reading past the feature lists to how each one handles determinism — this comparison of API testing tools breaks them down by approach, and the mocking/normalization behaviour is the part that decides whether your suite stays trustworthy at scale. (For what it's worth, I work on Keploy, which leans on auto-generated mocks and field normalization specifically to keep replays deterministic — but the principle applies whatever you pick.)

The honest caveat

Some flakiness is real signal — a genuine race condition in your API that a test happened to expose. Don't reflexively "fix" every flaky test by suppressing it; sometimes the test is right and the code has the bug. The goal is to remove test-induced variance so that when a test does flake, you can trust it's pointing at something real.

What's your worst flaky-test story? Mine was a suite that only failed on Tuesdays — turned out to be a downstream service's weekly maintenance window bleeding into our CI.