CI/CD is a cornerstone of modern DevOps practices. It’s widely adopted by organizations that want rapid software delivery, frequent updates, and, of course, automation.
If you look at it from a theoretical perspective, the pipeline should fade into the background, quietly running tests, packaging code, and deploying updates while engineers focus on building the product. Right?
However, in practice, many organizations end up with the opposite. The pipeline becomes one of the most complex systems in the stack, where builds stretch into hours, developers wait in queues for tests to finish, and every upgrade or minor change risks breaking the flow.
Look, the problem here isn’t with the idea of CI/CD. It’s how easily your pipeline turns into an over-engineered machine that consumes more time and energy than it saves. Let’s explore how CI/CD pipelines get bloated, why that hurts your team, and what can be done to fix it.
Where CI/CD Pipelines Go Wrong: Roadblocks to Know
1. Tool sprawl
A typical setup includes Jenkins or GitLab, an Appium farm or a Selenium grid, scripts written years ago by engineers who have since left the organization, and half a dozen plug-ins or dashboards to fill in the gap in code coverage, test reporting, and security scans.
Each tool may have made sense at the time. But eventually, they end up creating a system that’s messy and complicated and takes hours for engineers to manage it.
2. Slow feedback
The most important purpose of CI/CD is fast feedback. Yet many teams find that a simple change, such as updating the color of a button, rewriting a single line of code in a configuration file, or fixing a typo on a page that appears in the app, can take hours to validate.
These tiny updates shouldn’t take long to test or release. But in a bloated pipeline, the wait time adds friction to every sprint and delays progress.
3. Fragile systems
Even when pipelines are working, they often feel brittle. For example, a minor version bump in a library, a configuration drift between environments, or a flaky test suite can bring the entire pipeline to a halt.
Now, each interrupts the flow and forces engineers to spend time firefighting instead of improving the product.
4. Hidden costs
A bloated CI/CD pipeline comes with a price. Every extra minute a workflow runs increases compute bills. Device labs and Selenium grids also require ongoing hardware, licenses, and engineers to keep them running.
A CircleCI’s 2025 report shows that simply trimming the workflow duration from 20 to 10 minutes could reclaim 750,000 minutes of engineering time in a year, which is worth more than $1 million annually in productivity.
Action Plan: How to Minimize the CI/CD Pipeline Bloat
Map your pipeline end-to-end
Start by drawing out every stage of your current pipeline: build, unit tests, integration tests, end-to-end tests, security scans, deployment, and reporting. For each stage, list:
- Which CI/CD tools and services are in play
- Average runtime per stage
- Failure rate per stage
- Who maintains it Once you have it all in front of you, you can see which steps create real value and which ones remain simply because “we’ve always used them.”
Pro Tip: Conduct a 60-90-minute value stream mapping workshop with your stakeholders, like QA leads, product owners, SDETs, and engineering managers. This helps give everyone the same view of where the pipeline slows down.
Measure the test stage
Timebox each stage and decide what to eliminate, automate, or standardize. To do this properly, analyze actual figures for each test suite. Some examples include:
- Cycle time: From commit to deploy
- Flakiness rate: Percentage of test runs that fail without a real defect
- Infra cost per run: For cloud compute, storage, and device lab usage
- Mean time to feedback: How long it takes for a developer to get a pass/fail signal Example: If your cycle time from commit to deploy is 45 minutes, your flakiness rate is 6%, each run costs $2.10, and your mean time to feedback is 12 minutes, you now have a baseline. From here, you can decide what to eliminate (like redundant tests), what to automate (such as environment cleanup), and what to standardize (like faster, parallelized runs) so your pipeline becomes leaner and more reliable.
Reduce redundant tools
In most modern CI/CD pipelines, testing stages account for the largest share of runtime, flakiness, and cost. Especially if you take UI, mobile, and integration suites, you’ll agree that they’re slower, run on heavier infra, and often require retries.
That’s why bloat shows up most visibly here. Every tool here should, therefore, deliver clear value: faster feedback, higher reliability, and lower cost.
Enforce fast feedback loops
Set a target for how quickly developers should get a signal after committing code. For instance, you can aim for unit and integration tests to finish within 10 minutes and functional suites within thirty. If a test suite takes longer than that and doesn’t deliver enough value, move it to a later stage or run it asynchronously.
How TestGrid Minimizes the CI/CD Bloat While Accelerating Release Velocity
If testing is the heaviest part of your software pipeline, then optimizing it will give you the biggest efficiency gains overall. That’s where TestGrid, the AI-powered end-to-end testing platform, can come in handy.
Instead of maintaining multiple disconnected tools, device labs, and reporting add-ons, you can manage your entire test infrastructure in one place.
For starters, rather than maintaining a local device lab, which is both expensive and hard to scale, TestGrid gives you on-demand access to real device testing online for 100+ iOS and Android devices in the cloud. You can validate web and mobile apps in true user conditions without hardware upkeep.
Secondly, TestGrid supports parallel execution across browsers and devices, compressing multi-hour test runs into minutes. In addition, the platform collects test result data, including pass/fail statuses, performance metrics, and logs from multiple test runs, making it easier to debug across releases.
TestGrid can also be integrated with CI/CD tools like Jenkins and Azure DevOps. It supports various frameworks and processes to test results from them, such as JUnit, TestNG, or custom formats. In the end, the result of using TestGrid is a more resilient pipeline.
This blog is originally published at Testgrid
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