Experimentation’s build-vs-buy debate is asking the wrong question

For years, product teams have framed experimentation as a simple build-or-buy decision: You either build an in-house experimentation platform and own the infrastructure, maintenance, and engineering effort, or you buy a dedicated experimentation tool and start running tests sooner.

But both approaches have their disadvantages, and as experimentation programs mature, the limitations of both become harder to ignore. The good news? Companies today have more than just these two options, in large part because modern analytics platforms now include experimentation and feature flagging alongside product analytics. 

That means you have a third option: Bring experimentation into the same platform you’re already using to understand customer behavior.

There are obvious costs to creating an in-house A/B testing platform. Your team has to build:

Engineers are responsible for maintaining every component long after launch, and someone has to stay on call when something breaks.

Those costs alone are significant, but the higher cost comes from everything your team doesn't build while it works on experimentation infrastructure.

Every month your engineers spend maintaining an internal platform delays work on customer-facing features. And while you’re absorbing this opportunity cost, dedicated experimentation vendors are continuing to improve their products by applying learnings from hundreds or thousands of customers.

It's the same tradeoff many companies face with analytics: whether to invest engineering resources in building an internal analytics platform or buy a purpose-built solution. In both cases, the biggest expense is not only the time it takes to build the platform, but also the ongoing cost of maintaining infrastructure that isn't core to your business.

Lichay Cohen, former VP of Product at BeachBum and CEO of Sugar Games, summarizes the challenge succinctly:

Not only that, but all of that investment still leaves you with a problem: Unless your experimentation platform shares the same foundation as your analytics platform, your experiment data and the behavioral context needed to understand those results are residing in separate places.

Many teams that don’t want to invest in building their own solution will choose to buy a dedicated experimentation platform. This approach helps save on time and internal resources while offering instant access to mature experimentation workflows, reliable statistics, rollout controls, and operational support—all without having to dedicate engineers to platform development.

But while purchasing a solution gives you those advantages, most are standalone platforms, which means they come with the same potential challenge as building from scratch: experiment results often live separately from the product data that teams use to understand user behavior.

Most build-vs-buy conversations focus on the obvious tradeoffs: engineering resources, implementation time, and total cost of ownership. Those considerations matter, but they don't address an overlooked (but vital) factor: where your experimentation data lives.

If your experimentation platform is separate from your analytics platform, every experiment introduces another layer of context switching. Teams review test results in one place, then have to open another application to understand why users did what they did. Even when the numbers align (which doesn’t always happen when you’re working in different systems), that workflow creates friction that slows decision-making.

Sourav Badami, Founder of Simple Viral Games, describes what happens when companies rely on disconnected infrastructure in an analytics context: 

A third option is beginning to reshape the build-vs-buy conversation: Use a platform that combines experimentation and analytics from the start. Instead of wasting time moving and comparing data between systems, teams can launch experiments and analyze outcomes using the same source of truth.

For example, Mixpanel Experiments connects experiment data with the cohorts, metrics, and reports teams already use for behavioral analysis. You can define who sees a change, measure the impact across primary and secondary metrics, and investigate unexpected outcomes without toggling between systems.

In other words, you can measure the broader downstream and long-term impact of a test, not just a single metric of interest. (A winning onboarding variant, for example, might increase initial conversion while also improving 60-day retention and/or driving engagement with other product features.)

Step, a mobile-first financial platform that gives teens and young adults better tools for managing their money, saw the value of this approach after bringing its experimentation workflows into the same platform it was already using for product analytics. Previously, the team relied on a combination of Looker dashboards and custom reports for experiment analysis, which forced them to stitch together data from multiple sources to understand experiment results.

By using Mixpanel's native experiments functionality to combine experiment exposure data, warehouse-connected metrics, and product metrics, Step built a more consistent process for evaluating experiment results. Consolidating those workflows also meant the team could set up experiments, review results, and make decisions in a single place.

“During our review meetings, we look at the results, make decisions, and record them directly in the dashboard,” says Giyom Lebleu, Chief Product Officer at Step. “The combination of reusing metrics, syncing backend data, and sending experiment events to Mixpanel made it all work for us.” 

Thanks to a redesigned UX (that the team tested and validated in Mixpanel), Step increased the conversion rate of customers making it their primary bank account by 14%.

If you’re evaluating experimentation platforms, the costs in terms of engineering resources, time, and financial investment are all factors to consider. But perhaps an equally important question is whether your experimentation setup helps your team make better product decisions over the long term.

Buying a standalone platform may give teams the means to run experiments quickly, but it typically creates another data silo. Building internally gives teams control, but requires ongoing engineering investment that could otherwise go toward customer-facing work. Both options often require piecing together insights across multiple systems.

An integrated analytics and experimentation platform offers a different path, equipping teams with behavioral data to identify opportunities, run experiments within the same workflow, and understand the impact of those decisions across both short- and long-term outcomes.

See what experimentation looks like when it lives inside your analytics. Explore Mixpanel Experiments and feature flagging to see how teams can safely roll out changes, measure their impact, and connect experiment results back to the customer behaviors behind them.