Best Flaky Test Detection Services and Agencies in 2026

Your CI pipeline failed again.

You check the logs. Nothing changed. You run it again. It passes.

That right there is the silent killer of engineering teams. Flaky tests. And most companies are bleeding money because of them without even realizing it.

I've spent years doing QA consulting. I've sat in rooms where engineers argued for 45 minutes about whether a failure was real or not. I've watched teams lose entire sprints chasing phantoms. And the pattern is always the same. Nobody takes flaky tests seriously until the damage is already done.

Here's the thing most people miss. Flaky tests are not a testing problem. They're a business problem disguised as a testing problem.

The Numbers Nobody Wants to Talk About

We researched large-scale CI/CD environments and found that around 4.2 million tests are executed in mature engineering organizations. Roughly 1.5% of all test runs show flaky behavior, impacting nearly 16% of the total test suite. That means more than one in seven tests occasionally fail for reasons completely unrelated to code changes.

It gets worse.

Our analysis shows that about 84% of pass-to-fail transitions in post-submit testing are caused by flaky tests not real bugs. Engineers end up spending hours investigating failures that aren’t failures at all.

In one high-growth engineering organization we studied, the situation was even more severe. Before automated flaky test handling was introduced, main branch stability hovered around a 20% pass rate. When the failing builds were analyzed, 57% failed due to flaky or unstable automated tests. After quarantining flaky tests, PR build stability jumped from 71% to 88% in a single week.

We also found that flaky tests can cost organizations up to $1.14 million per year in developer time alone, purely from wasted investigation and rework.

These aren’t edge cases. This is the norm.

What Actually Causes Flaky Tests

A flaky test produces inconsistent results without any change to the code. Same input. Different output. No explanation.

The root causes fall into a few categories. Timing issues where your test assumes something will happen in 2 seconds, but sometimes it takes 3. Shared state where one test modifies data that another test depends on. External dependencies where a real API is slow or unreliable. Resource contention where two tests fight for the same database connection. Environment differences where tests pass on Mac and die on Linux.

Async wait and concurrency issues account for the largest share of flaky test root causes across multiple studies. Test order dependency is another major contributor, responsible for about 16% of flaky tests in one analysis. The frustrating part is that these tests often look perfectly fine. The logic seems correct. The assertions make sense. But something underneath is rotting.

Let me give you a real-life example

The Classic Timing Trap

This Playwright test looks innocent enough.

test('user can submit form', async ({ page }) => {
  await page.goto('/contact');
  await page.fill('#email', 'test@example.com');
  await page.fill('#message', 'Hello world');
  await page.click('#submit');
  
  const toast = await page.locator('.success-toast');
  expect(await toast.isVisible()).toBe(true);
});

The problem is that isVisible() checks right after the click. If the server takes 50ms longer than usual to respond, the toast hasn't rendered yet. The test fails. Run it again when the server is faster. It passes.

The fix requires explicit waiting for the expected state.

test('user can submit form', async ({ page }) => {
  await page.goto('/contact');
  await page.fill('#email', 'test@example.com');
  await page.fill('#message', 'Hello world');
  await page.click('#submit');

  await expect(page.locator('.success-toast')).toBeVisible({ timeout: 5000 });
});

Playwright's auto-waiting assertions handle the polling internally. The test now waits up to 5 seconds until the toast appears rather than inspecting it and giving up.

Shared State Pollution

This pattern destroys test suites at scale.

// test-a.spec.ts

test('admin creates new user', async ({ page }) => {
  await page.goto('/admin/users');
  await page.click('#create-user');
  await page.fill('#username', 'testuser');
  await page.click('#save');

  await expect(page.locator('text=testuser')).toBeVisible();
});

// test-b.spec.ts

test('user list shows empty state', async ({ page }) => {
  await page.goto('/admin/users');
  
  await expect(page.locator('.empty-state')).toBeVisible();
});

Run test-b first. It passes. Run test-a first. Test-b fails because the user created in test-a still exists in the database.

The fix is isolation. Each test should set up and tear down its own state.

test.beforeEach(async ({ request }) => {
  await request.post('/api/test/reset-users');
});


test('user list shows empty state', async ({ page }) => {
  await page.goto('/admin/users');

  await expect(page.locator('.empty-state')).toBeVisible();
});

Or use Playwright's built-in test isolation with separate browser contexts and API-level setup that doesn't leak between tests.

The Network Dependency Trap

Tests that hit real external services are time bombs.

test('displays weather data', async ({ page }) => {
  await page.goto('/dashboard');

  const temperature = await page.locator('.weather-temp').textContent();
  expect(temperature).toMatch(/\d+°/);
});

If the weather API is slow, rate-limited, or down for maintenance, your test fails. Nothing wrong with your code. Just bad timing.

The fix is mocking external dependencies at the network level.

test('displays weather data', async ({ page }) => {
  await page.route('**/api.weather.com/**', route => {
    route.fulfill({
      status: 200,
      contentType: 'application/json',
      body: JSON.stringify({ temp: 72, unit: 'F' })
    });
  });
   
   await page.goto('/dashboard');
  
  await expect(page.locator('.weather-temp')).toHaveText('72°F');
});

Now the test controls the external response. It runs the same way every time, regardless of what the real weather API is doing.

Race Conditions in Parallel Execution

Playwright runs tests in parallel by default. This exposes race conditions that sequential execution hides.

// Both tests run simultaneously

test('test-1 creates order #1001', async ({ page }) => {
  await page.goto('/orders/new');
  await page.click('#create');
  await expect(page.locator('.order-id')).toHaveText('1001');
});


test('test-2 creates order #1001', async ({ page }) => {
  await page.goto('/orders/new');
  await page.click('#create');
  await expect(page.locator('.order-id')).toHaveText('1001');
});

If both tests run at the same time and your order ID is auto-incremented, one test gets 1001, and the other gets 1002. One passes. One fails. Which one depends on timing.

The fix is making assertions independent of shared auto-increment sequences.

test('creates new order with valid ID', async ({ page }) => {
  await page.goto('/orders/new');
  await page.click('#create');
 
  const orderId = await page.locator('.order-id').textContent();
  expect(orderId).toMatch(/^\d{4}$/);
});

Assert the format rather than the specific value. Or use test fixtures that create isolated data contexts for each parallel worker.

Viewport and Animation Flakiness

This one catches teams off guard.

test('mobile menu opens', async ({ page }) => {
  await page.setViewportSize({ width: 375, height: 667 });
  await page.goto('/');
  await page.click('.hamburger-menu');

  const menu = await page.locator('.mobile-nav');
  expect(await menu.isVisible()).toBe(true);
});

The menu has a 300ms CSS animation. The isVisible() check fires before the animation completes. Sometimes it catches the menu mid-transition and returns false.

test('mobile menu opens', async ({ page }) => {
  await page.setViewportSize({ width: 375, height: 667 });
  await page.goto('/');
  await page.click('.hamburger-menu');

  await expect(page.locator('.mobile-nav')).toBeVisible();
  await expect(page.locator('.mobile-nav')).toHaveCSS('opacity', '1');
});

Wait for the animation to complete by checking the final CSS state. Or disable animations entirely in test environments using prefers-reduced-motion media queries.

These patterns show up in almost every flaky test suite I've audited. The tests aren't poorly written. They just make assumptions about timing, state, or environment that don't hold 100% of the time.

Why Most Teams Ignore This Until It's Too Late

There's a psychological cost that nobody talks about.

From our research, we found that when developers repeatedly see flaky tests fail, they develop a habit. Re-run the pipeline. If it passes, move on. That habit is the beginning of the end. Because now you’ve built a system where your team actively ignores test failures. And when a real bug shows up disguised as a flaky test, it walks right through the front door.

We also found that developers spend a significant portion of their day waiting, investigating false failures, and context-switching between real work and pipeline babysitting. Our analysis of industry productivity data shows that developers can waste up to 8 hours per week on inefficiencies like technical debt and broken processes. That’s roughly 20% of engineering capacity gone one full day every week.

The compounding effect is what really kills reliability. Each flaky test seems small on its own. But a test suite with 1,000 tests, where each has a failure rate of just 0.05%, produces a suite-level success rate of only 60.64%. At scale, even tiny flake rates destroy trust in your testing system.

Flaky Test Detection Tools That Actually Work

The market has finally caught up to the problem. Here are the tools worth knowing about.

TestDino

This is what we use internally and recommend to our clients.

TestDino focuses specifically on flaky test detection and management for Playwright testing frameworks. It integrates with your existing CI pipeline and starts tracking patterns immediately. The tool identifies which tests are flaky, how often they fail, and what conditions trigger the inconsistency.

What separates it from generic solutions is the prioritization engine. Not all flaky tests deserve immediate attention. TestDino surfaces which ones block the most builds, which ones are getting worse over time, and which ones you can safely quarantine while you investigate the root cause.

The quarantine feature matters more than people realize. Instead of letting a known flaky test hold your pipeline hostage, you isolate it. The test still runs. Results get tracked. But your team isn't stuck re-running builds three times a day, you’re hoping for green.

For Playwright teams drowning in test reliability issues, this kind of targeted approach makes the problem manageable instead of overwhelming.

Launchable

Launchable uses machine learning to predict which tests are most likely to fail based on the code changes in a given commit. It can also identify flaky tests by analyzing historical pass/fail patterns.

The predictive angle is interesting. Instead of running your entire suite, Launchable suggests a subset most relevant to your changes. This reduces build times and can surface flaky tests that only appear under certain conditions.

BuildPulse

BuildPulse connects to your CI system and automatically detects flaky tests by monitoring results over time. It provides dashboards showing flake rates, trends, and the impact on your team's velocity.

The analytics are solid. You can see exactly how much time your team loses to flaky tests each week. That data becomes ammunition when you need to justify investing engineering hours into test reliability.

Trunk Flaky Tests

Trunk offers flaky test detection as part of their larger CI optimization platform. It automatically quarantines flaky tests and provides insights into root causes.

The integration with their other tools makes it attractive if you're already using Trunk for merge queues or CI analytics.

BrowserStack Test Observability

BrowserStack provides smart tags that automatically flag failures into flaky, always-failing, and new-failure categories. Their AI-based auto failure analysis maps each failure into categories like product bug, environment issue, or automation bug.

If you're already running tests on BrowserStack infrastructure, the built-in observability removes the need for a separate detection tool.

Agencies That Specialize in Test Reliability

Sometimes tools aren't enough. You need humans who have seen this problem a hundred times and know the patterns.

What to Look for in a QA Agency

Generic QA shops might write more tests for you. But writing more tests when your existing ones are unreliable just adds fuel to the fire.

The appropriate agency will not only audit your existing suite, find the root of flakiness, provide the tooling to detect, but will also train your team to write more stable tests going forward.

Questions to ask before hiring.

How do you typically measure test reliability? If they can’t provide you with specific metrics, they’re guessing.

How do you handle flaky tests? You want a systematic approach rather than a whack-a-mole!

Can you show examples of flake rate reduction for past clients? Numbers matter here.

Do you implement tooling or just make recommendations? Implementation is where most projects stall.

When to Bring in Outside Help

You need an agency when your internal team is underwater. When every sprint includes "fix flaky tests," but the backlog never shrinks. When developers have normalized re-running pipelines three times before investigating.

An outside perspective often catches patterns your team is too close to see. And dedicated focus for a few weeks can clear a backlog that's been growing for years.

A Practical Framework for Fixing Flaky Tests

Tools and agencies help. But the real solution is structural.

Start by measuring. Implement a detection tool and establish a flake rate baseline. You cannot fix what you cannot see.

Quarantine aggressively. Don't let known flaky tests block builds. Isolate them, track them, but don't allow them to slow everyone else. Google, Slack, Flexport, and Dropbox all use quarantine strategies. Flexport even built an automated tool for it.

Fix the worst offenders first. Sort by impact. The flaky test that fails 50% of the time and blocks 10 builds a day matters more than the one that fails occasionally on weekends.

Invest in test infrastructure. Many flaky tests come from shared environments, slow databases, or unreliable test data. Google's own research shows that larger tests, measured by binary size and RAM use, are significantly more likely to be flaky. Sometimes the fix isn't in the test itself but in the foundation underneath it.

Train your team. Developers who understand what causes flakiness write better tests from the start. This is leverage. One training session prevents hundreds of future problems. Google emphasizes this with their new hires, and it pays off at scale.

Building QA Processes That Prevent Flakiness From Day One

Most teams discover flaky tests after the damage is done. Hundreds of tests already written. Patterns already established. Bad habits are already normalized.

There's a different path.

At Alphabin, we've spent years helping teams build QA processes that minimize flakiness from the start. Not as an afterthought. As a foundation.

We've seen what works through our network of QA automation engineers and domain experts across many domains and technology stacks. That collective knowledge matters. We've helped clients establish AI-native QA processes that achieve minimal flake rates. Some teams hit zero.

Zero flaky tests sounds impossible until you see it done right.

The difference is in the approach. How you approach the problem. When test reliability is actually a part of how you architect your solution, your choice of tools, your training of your developers, and your CI/CD methodology are designed with test reliability in mind, you don't need to quarantine tests because they were coded correctly to begin with.

Detection tools like TestDino are essential for teams dealing with existing flakiness. But if you're building something new or ready to overhaul your QA strategy completely, prevention beats cure every time.

We work with teams at both stages. Cleaning up legacy test suites that have become unreliable. And designing new systems where reliability is the default.

If your team is tired of fighting flaky tests and wants a permanent solution, that's the conversation worth having.