Free tool

Flaky test cost calculator

Flaky tests don’t just waste time — they erode trust in the whole suite. Estimate what your flakiness costs in engineering hours and dollars every month.

1,000 tests
16%  (Google has reported ~16%)
20 min
Flaky tests in your suite160
Estimated time lost to flakiness231 hrs / mo
Estimated cost of flakiness$17,325 / mo$207,900 / year

Rough estimate: flaky tests × ~one investigation per week × your minutes-per-investigation, at a $75/hr loaded cost. It ignores the harder-to-cost damage — ignored red builds and real regressions slipping through the noise. Your results will vary.

What this means for your team

The number above is only the direct cost — the hours spent re-running and investigating failures that turn out to be noise. The bigger, harder-to-quantify cost is what flakiness does to trust: once a team learns that a red build is “probably just flaky,” they start ignoring failures, and that’s exactly when a real regression ships.

The fix isn’t heroic effort — it’s removing the cause. Self-healing tests re-resolve locators automatically so cosmetic UI changes stop breaking tests, and test impact analysis runs only the tests a change can affect — so there are far fewer failures to investigate in the first place.

Frequently asked questions

How much do flaky tests cost?

It depends on suite size and flaky rate, but the cost is rarely small: each flaky failure triggers an investigation, and at typical rates (Google has reported ~16% of its tests are flaky) a mid-size suite can burn dozens of engineering hours a month. Use the calculator above to estimate yours.

What counts as a flaky test?

A flaky test passes and fails intermittently without any code change — usually due to timing, network, test data, or unstable selectors. The real damage is trust: once teams assume red builds are "just flaky," genuine regressions slip through.

How do you reduce flaky-test cost?

Stabilise locators (self-healing re-resolution instead of brittle CSS/XPath), quarantine genuinely flaky tests rather than blind-retrying them, and run only the tests a change can affect so there are fewer failures to investigate.

Read: how to kill the maintenance tax

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