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.
- Number of Variations: The scope can expand from simple A/B to multi-variate tests with multiple variations.
- Split Percentages: Instead of fixed splits, you may require partial rollouts (10% one day, 50% the next) or dynamically adjusted traffic (Bandits).
- Randomization Seeds: Deterministic assignment so every data query lines up with the same user-variant grouping.
- Remote configurations. You may want ways to pass different values to the experiment from your experimentation platform.
- Targeting Rules: Fine-grained controls, such as showing a test only to premium subscribers in California, can quickly add complexity.
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.
- Stable Identifiers: Teachers logging in from shared computers, students on tablets, or parents switching between mobile and desktop.
- Hashing & Randomization: You want an algorithm that’s fast and produces an even distribution.
- Server vs. Client-Side: Server-side experimentation is great for removing some of the problems with flickering but may lack certain user attributes at assignment time. Client-side is more flexible but can cause quick visual shifts as JavaScript loads.
- Timing Issues: Caching layers or missing user identifiers can lead to partial or double exposures, invalidating experiment data.
- Cookie consent: Determining when you are allowed to assign them, what counts as an essential cookie vs a tracking one.
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:
- Defining Attributes: Collect and securely store user data (location, subscription status, device type).
- Overlaps & Exclusions: Prevent one user from landing in conflicting experiments.
- Evolving Segmentation: Plan for marketing and product teams to constantly discover new slices of your user base.
- Sticky Bucketing: Once a user sees a variant, they should continue to get this variant even if other settings change to not invalidate their data. This quickly gets tricky deciding on when to sticky a user, and when to reassign
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:
- Data Volume: Logging millions of events for high-traffic applications can overwhelm poorly designed systems.
- Pipeline Reliability: Data loss or delays can lead to inaccurate analyses.
- Separation of Concerns: The last thing you want is for your site’s main functionality to slow down because your experiment-logging system chokes under load.
Performance Issues
Your experimentation system must never degrade user experience. That means your platform must be built with the following in mind:
- Latency: Assignment and targeting logic should run in milliseconds to avoid flickering.
- Fault Tolerance: If the platform goes down, the product should revert to a default or safe state, not crash outright.
- Decoupling: Keep experiment code out of critical paths to prevent a single failure from taking out your entire product.
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.
- Customization: Each metric might require customization from the default. Does it need different conversion windows, minimum sample sizes, or specialized success criteria? (e.g., “logged in at least 5 times within 7 days”)
- Metadata Management: Who owns each metric? How is it defined? Are you duplicating metrics under different names?
- Flexibility: Hard-coding a handful of metrics quickly becomes a bottleneck when new use cases emerge.
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:
- Conversion Windows: Ensure that only events occurring after exposure are counted and while the experiment was running.
- Data Joins: Merging experiment exposure logs with event data often requires complex queries that can tax your data warehouse.
- Periodic Updates: Experiment results change over time, so you’ll want to have a way to update results periodically.
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:
- SRM (Sample Ratio Mismatch): A study from Microsoft found that ~10% of experiments failed due to assignment errors. Regularly test that actual split percentages match your intentions.
- Double Exposure: If a user is unintentionally counted in two variations, their data should be excluded. If the percentage of users getting multiple exposures is high, you probably have a bug in your implementation.
- Outlier Handling: A handful of power users can skew averages. Techniques like winsorization help maintain balanced metrics.
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
- Non-Technical Teams: A user-friendly dashboard lets product, marketing, and data teams set up and monitor experiments without engineering support.
- Collaboration & Documentation: Capture hypotheses, share outcomes, and maintain a history of past tests—so insights don’t disappear when someone leaves.
- Real-Time Visibility: Spot anomalies (like misallocated traffic splits) early and fix them before they skew results.
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.