The Hidden Complexities of Building Your Own A/B Testing Platform

· 6 min read
The Hidden Complexities of Building Your Own A/B Testing Platform
Photo by Martijn Baudoin / Unsplash

Years ago, at Education.com, we decided to build our own A/B testing platform. We had a large amount of traffic, a data warehouse already tracking events, and enough talented engineers to try something “simple.” After all, how hard could it be? But as with most engineering projects, it quickly became evident that what seemed straightforward morphed into a complex, high-stakes system, one where one bug will invalidate critical business decisions.

In this post, we’ll break down the hidden complexities, costs, and risks of building your own A/B testing platform that you may not be thinking of when you first start out on this endeavor.


Experiment Description

On the technical side of running an experiment, you need a way to tell your systems how you will assign users to a particular variant or treatment group in an experiment. Most teams begin with the basics, like deciding how many variations to run (A/B, A/B/C, or more) and how to split traffic (e.g., 50/50 vs. 90/10). Initially, it might look like you only need to encode a handful of properties. But as your platform use grows, you’ll discover you need more parameters.

Hard-coding these elements can be tempting, but it rarely scales. As your needs evolve, a rigid approach may lock you into time-intensive updates—especially when product managers want new ways to target or measure experiments.

User Assignment

Ensuring that each user sees the same variant across multiple visits or devices may sound easy. But in practice, deterministic assignment can trip you up if you don’t handle user IDs, session IDs, and hashing logic carefully.

Mistakes here—such as users seeing multiple variations—can invalidate your results and lead to user frustration.

Targeting Rules

Precise targeting often starts simply—“Show the new treatment to first-time users”—but quickly grows. Soon, you’re juggling rules like “Display Variation A only to mobile users in the U.S., except iOS < 14.0, and exclude anyone already in a payment test.”

To avoid chaos, focus on these key areas:

Without a thoughtful targeting system, the tangle of conditions becomes unmanageable, undermining both performance and trust in your experimentation platform.

Data Collection

Every time a user is exposed to a test variant, you must log it accurately. If you already have a reliable event tracker or data warehouse, you’re in good shape—but it doesn’t eliminate problems:

Performance Issues

Your experimentation system must never degrade user experience. That means your platform must be built with the following in mind:

Metrics Definition

Metrics are the backbone of A/B testing. However, there are aspects of metrics used for experimentation that are not obvious at first.

We discovered product managers, data scientists, and marketing teams each had unique definitions of “success.” We needed a system to capture these definitions and keep them consistent across the organization.

Experiment Results

Analyzing results might be the most critical step:

Any mismatch between exposure events and downstream metrics can lead to spurious conclusions—sometimes reversing what you thought was a clear “win.”

Data Quality Checks

There are innumerable ways for your data to be messed up and cause unreliable results and a lack of trust in your platform. Here are some of the most common ones:

At Education.com, we regularly scanned for sample ratio mismatches—often uncovering assignment bugs we didn’t even know existed.

Statistical Analysis

Interpreting results is where the real magic—and risk—happens. When you're making a ship/no-ship decision, you need to be sure your analysis is as accurate and trustworthy as possible. A wrong decision caused by an issue with your statistics can be very costly, but finding a systemic issue with your statistics after months or years can be catastrophic. Relying on a single T-test can be misleading in real-world testing, especially if you’re “peeking” mid-experiment. Frequentist, Bayesian, and sequential methods each have trade-offs: how you handle multiple comparisons, ratio metrics, or quantile analyses can drastically impact conclusions. Underestimating these nuances may lead to false positives, costly reversals, or overlooked wins. If you don’t have deep in-house expertise, consider leveraging open-source statistical packages—like GrowthBook’s—to maintain rigor and reduce the chance of bad data driving bad decisions.

User Interface

Even the most advanced experimentation platform falls flat if only a handful of engineers can operate it. A well-designed UI empowers

Neglecting the UI may save development time initially, but it can stifle adoption and limit the overall effectiveness of your experimentation program.

Conclusion: The Realities of Building In-House

Building your own A/B testing platform is much more than a quick project—it’s effectively a second product that ties together data pipelines, statistical models, front-end performance, and organizational workflows. Even small errors can invalidate entire experiments and erode trust in data-driven decisions. Ongoing maintenance, ever-changing requirements, and new feature requests often overwhelm the initial appeal of a DIY approach.

Unless it’s truly core to your value proposition, consider a proven, open-source solution (like GrowthBook). You’ll gain robust targeting, advanced metrics, and deterministic assignment—without shouldering the full cost and complexity. This way, your team can focus on what really matters: shipping features that users love.

Want to give GrowthBook a try?

In under two minutes, GrowthBook can be set up and ready for feature flagging and A/B testing, whether you use our cloud or self-host.