Real Cost of Building a Test Automation Framework
Many teams opt to build their own test automation frameworks using open source tools like Selenium or Playwright. The primary reason being that there's no licensing cost associated with such tools.
But is that the only reason to choose open source tools or is that the only cost associated with using open source tools?
Not quite. In this blog, we’ll uncover the hidden costs of building and maintaining a custom test automation framework and explore why it might not always be the most sustainable long-term strategy.
The Illusion of "Free" with Open-Source Tools
There’s a common misconception that open-source tools are free. Of course, there is no licensing cost, but that does not mean the development is free. You still need engineers and a team to build and maintain the test automation framework using the open source tools. The first step involves setting up the right team - either repurposing skilled engineers or hiring new ones—typically SDETs or automation engineers.
You also need to consider how much time it takes to design the framework. Multiple types of automation frameworks can be designed based on your application’s needs. Beyond that, decisions need to be made on:
- The programming language your team is comfortable with (or hiring the right set of engineers)
- How test data will be seeded
- Whether you’ll execute tests on static environments or dynamic environments like Docker containers
All these decisions depend on your team’s structure, available bandwidth, and budget. Once infrastructure, data alignment, and dependencies (like libraries for visual validation, accessibility, database and file-based operations) are in place, the actual test case automation begins.
During test creation, you must:
- Choose a framework model
- Choosing the right design pattern
- Define reusable components and page objects
- Select the right locator strategies
If your app design changes frequently, are image-based locators more suitable? These decisions are not just one-time—they influence long-term test stability and maintenance. Every choice—from locator strategy to test runner setup—adds complexity and cost.
The Hidden Cost of Maintenance
Building your own test automation framework means you're also taking on the responsibility of maintaining it—and this is where costs begin to spiral.
The test infrastructure requires constant monitoring and proactive upgrades. Supporting new browsers, devices, or operating systems isn’t just about platform compatibility—it also means updating web drivers, adjusting environment configurations, and ensuring your tests continue to work seamlessly across all changes.
I’ve personally been in a situation where we needed to push a critical hotfix. But because we hadn’t updated the drivers in time, our entire automation suite—over 3,000 test cases—failed. The team ended up firefighting all night to fix the automation, while the actual bug fix sat idle. That kind of overhead is not only frustrating but costly.
As your test suite grows from 100 to 2,000+ tests, so does the complexity. Execution time increases, and test flakiness becomes inevitable. Debugging flaky tests often consumes more time than building new ones, leading to automation backlogs. Eventually, teams begin commenting out unstable tests just to get clean runs—which ironically increases manual effort and reduces trust in automation.
And then there are structural changes.
For example, if your app migrates from Angular to React, the design changes could affect hundreds of automated tests. Refactoring all those scripts is not a trivial task.
If the framework isn’t consistently maintained, test coverage quickly becomes outdated. Your team ends up in a perpetual firefighting loop—where the original promise of automation (speed and stability) is lost entirely.
Knowledge silos
Typically, when a custom test automation tool is built, it starts with one person—usually an engineer who initiates the framework.
That individual makes key decisions around which tools to use (like Selenium or Playwright), what programming language to adopt, and how the framework should be structured—often based on specific application requirements and informal discussions with a small group.
But what happens when that person leaves?
In my experience, when I’ve joined as the second or third SDET on a team, I’ve often found myself redesigning the entire framework from scratch. Why? Because I bring my own experience, preferences, and tooling knowledge—leading me to overlook or discard the existing setup. This cycle of reinvention is surprisingly common.
As teams grow, automation engineers and SDETs begin contributing independently across different features. Without shared standards or guidelines, everyone implements their own ideas and styles. This leads to fragmented test code, inconsistent practices, and an increasing need for maintenance and refactoring—becoming a significant overhead.
What makes this worse is the lack of documentation. Internal tools like test automation frameworks rarely have robust onboarding guides. So, when new engineers join, it becomes a challenge to get up to speed. They’re often left reverse-engineering existing codebases, which slows things down considerably.
And then there’s the issue of inclusivity. Non-technical testers are usually left out of the automation process entirely. Since custom frameworks demand coding knowledge and an understanding of engineering principles, the barrier to entry becomes too high. As a result, collaboration between manual QA and automation engineers decreases, and the overall velocity of test automation takes a hit.
Tooling vs. Testing Mindset
One pattern I’ve observed—even in my own journey—is that when you're deep into building a test automation framework, you gradually start thinking more like a developer than a tester. The focus shifts to designing the framework for scalability, reusability, modularity, stability, and reducing flakiness. While these are critical engineering aspects, the core intent of automation—providing fast and meaningful feedback on product quality—often takes a back seat.
When we over-focus on architecture, we risk under-investing in writing meaningful test cases. The automation suite becomes a beautifully designed structure—but one that may not test real-world user flows effectively. It's important to recognize early on that the goal of automation is not framework perfection; it's actionable feedback and test coverage that matters.
Another challenge I've seen is with teams where QA engineers with coding skills transition into full-time SDETs. Over time, their mindset shifts—they're no longer thinking deeply about edge cases, exploratory testing, or holistic product validation. This often leads to a need for hiring manual QA engineers to fill the gap—people who can focus on reviewing the product, understanding business use cases, and writing robust test cases.
As a result, a silo forms. SDETs focus on maintaining and evolving the automation codebase, while manual testers focus on identifying test scenarios. But since they don't actively collaborate, critical insights may be lost in translation. The ones writing tests stop thinking like testers, and the ones testing lose visibility into how automation is evolving.
To avoid this disconnect, it’s essential to foster cross-functional collaboration, encourage shared ownership of quality, and regularly realign the team around the purpose of automation: enabling faster, more reliable product validation.
The Disconnect: When Automation Becomes a Black Box
As automation frameworks grow in complexity, manual testers—who are often closest to the business workflows and user expectations—start to lose visibility into what the automation is actually doing. The scripts become a black box: they run in CI pipelines, they report pass/fail results, but the logic behind them, the coverage they offer, and the edge cases they miss are not always transparent to those who aren't involved in the code.
This lack of visibility gradually erodes trust. Manual testers begin to wonder:
- “Is this test case even automated?”
- “What happens when this specific condition is hit?”
- “Why did this test pass when we know the feature is broken?”
Over time, this leads to a subtle but serious issue: manual testers stop relying on automation results. They begin to rerun manual test cases “just to be sure,” or they defer to their own validation cycles even when automation says things are green. Essentially, the automation becomes an isolated system—used more for reporting than for decision-making.
When that happens, the entire purpose of test automation—speed, confidence, and coverage—gets diluted. It’s no longer accelerating the feedback loop; it's just running in parallel.
When It Might Be Worth It
While building your own test automation framework can be time-consuming and resource-intensive, there are situations where it absolutely makes sense—provided certain criteria are met. It’s not a decision to take lightly, but when done with the right intent and support, it can deliver immense long-term value.
Here are three key scenarios where building your own framework may be the right choice:
Your App Has Unique Requirements That Off-the-Shelf Tools Can’t Handle
- Some applications are simply too complex for existing automation tools:
- Highly customized UIs where user workflows change dynamically.
- Domain-specific interactions (e.g., media platforms that require video transcoding validation).
- Embedded systems, real-time data streams, or extensive third-party integrations.
- Highly asynchronous operations that demand granular control.
- Regulated environments (e.g., fintech, healthcare, government) where cloud-based tools aren’t viable and robust on-prem solutions are limited.
In these cases, tailoring a framework from the ground up is often the only way to ensure proper test coverage and reliability.
You Have the Budget and the Team for a Long-Term Investment
Custom frameworks are not one-time efforts—they're long-term products:
- You need a strong in-house engineering team, dedicated not just to building, but to maintaining and evolving the framework.
- This isn’t a side project—it requires leadership buy-in, proper planning, and resource allocation.
- Over time, your test framework will grow to include performance testing, visual validation, accessibility checks, dynamic data generation, and more.
Done right, a well-built internal framework becomes a strategic asset, not just a set of scripts.
You Treat It Like a Product, Not a Disposable Tool
One of the most common mistakes is treating a custom test framework as a stopgap or utility that can be discarded when priorities shift. That mindset leads to wasted effort, fragmented teams, and poor continuity.
Instead:
- Think of your framework as a product—with its own roadmap, user base (internal QA/dev teams), documentation, and KPIs.
- Assign ownership and create a dedicated vertical if needed.
- Recognize that automation is not just UI testing. It spans performance, security, accessibility, data flows, and more.
Decide once—and commit. Switching between custom and ready-made solutions every year only leads to churn, fragmentation, and lost time. The decision to build should be deliberate, well-funded, and aligned with the company’s long-term testing vision.
Conclusion
Building a custom test automation framework is like choosing to build your own car instead of buying one—or even using Uber. On the surface, it may seem more cost-effective and flexible. But in reality, it often turns out to be more expensive, time-consuming, and complex than initially expected.
Modern platforms like DevAssure offer faster time-to-value, built-in stability, and accessibility for non-technical contributors—all of which drastically reduce the time, effort, and risk involved in rolling out effective test automation.
While a hybrid approach—using custom solutions for complex workflows and no-code platforms for simpler ones—might seem ideal, it introduces its own set of challenges: duplicate investments, fragmented maintenance, and scalability issues. I'll explore these trade-offs further in a future post.
At the core of it all is this:
You must evaluate the true cost before you commit to building.
Because this isn’t just about writing scripts or standing up a framework—it’s about driving real impact:
- ✅ Faster release cycles
- ✅ Higher product quality
- ✅ More productive teams
- ✅ And ultimately, happier users
Test automation isn’t just a technical project—it’s a strategic decision that influences your team’s velocity and your product’s success. So, make the decision consciously. And once you do, treat it like the long-term investment it truly is.
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