Should you build or buy an experimentation platform? (Real trade-offs)

It always starts this way. We can build this ourselves. We've got a small team, one year to do it. We'll make it work. And that's a pitch that launches a thousand internal experimentation platforms and kills most of them. We spoke with more than 15 large experimenting companies that has all either built or seriously considered building their own experimentation platforms. And almost all of them ran into the exact same problem. Their builds didn't fail because the code was bad. They failed because the organization couldn't keep them standing. And when internal experimentation platforms start to struggle, the problems show up everywhere. Experiments take longer to launch. Checking results becomes a project. Simple analysis suddenly takes hours and the whole program slows down. And the reason that happens is structural. In-house experimentation platforms tend to become a house of cards because a successful experimentation program depends on a handful of very specific roles. Think of each one like a card holding up the structure. Pull one out and the whole thing starts to wobble. Let's take a look at the cards. First card is a person championing the experimentation internally. They fight for budget, for headcount, for executive attention. And then when they leave, suddenly no one is defending that program. and experimentation quietly becomes optional. The next card is a person actually running the experimentation program. Without someone tracking what was tested or what was learned, the program loses its memory and eventually its credibility. And then there's engineering. The same developers building the experimentation platform are usually shipping product features too. And when priorities collide, experimentation loses. Experimentation only works if people trust the results. Once debates over methodology start slowing decisions down, momentum disappears. Every experiment introduces risk. Without proper QA, broken tests slip through and trust in the program erodess quickly. And finally, there's design. No designer availability means no experiment variations. And suddenly test velocity drops to zero. That is a house of cards. lose one card and the whole thing starts to collapse. Which raises a few obvious questions like why do teams still try to build if internal experimentation platforms are this fragile and why do smart companies keep trying to build them? Usually it comes down to a few reasons. One of the biggest reasons is data control. Teams want full ownership of their customer data and that's totally valid. But solving for data privacy doesn't mean you need to build an entire experimentation platform from scratch. Another reason is a cost of a vendor. Once a platform crosses a certain price point, teams assume building internally will be a lot cheaper. But these decisions rarely include the full cost modeling. When you factor in engineers, maintenance, infrastructure, and opportunity cost, internal platforms almost always cost more and deliver less. And sometimes it's just confidence. Teams will say we've built harder things than this and that might be true, but those estimates usually cover the code, not the organization required to actually sustain a mature experimentation program. And sometimes the inspiration comes from companies like Amazon or Netflix. Well, they built their own experimentation platforms, so we should be able to too. But those companies operate at an entirely different scale. Thousands of engineers, deep experimentation culture. that playbook doesn't always transfer easily to a team of five. Now, here's a tricky part. Even if all the cards stay standing, the program could still grind to a halt because internal experimentation platforms tend to struggle in the same four areas. First, usually there's no real user interface. Engineers run experiments directly through code, which means nontechnical teammates can't really participate. Every small change requires engineering time. Then there's monitoring. In mature experimentation platforms, checking results is straightforward. But with internal tools, checking whether a test hit significance can become a whole project. Teams are either checking obsessively or stop checking entirely. Analysis is another bottleneck. Simple questions require stitching together data across multiple systems. What should take 5 minutes can take hours in homegrown systems. And then finally, trust. Bucking and experimentation allocation are surprisingly hard to get right. Some teams run dozens of AA tests just to confirm that their system works and still don't get clean results. Once stakeholders stop trusting the numbers, experimentation stops driving decisions. Look, sometimes building is the right call. You're a $250 billion company with experimentation baked in from day one. or you've had a data breach and need to guarantee no third party touches

customer data or you want hybrid on some pieces by others. That can work but for most companies the principle is simpler. Build what's core to your product and buy the infrastructure that supports it. Experimentation feels core because it touches the product experience. But the platform behind it, that's infrastructure. And when every role in that system becomes a single point of failure, eventually one of those cards gets pulled and the whole thing collapses. If you're weighing whether to build or buy an experimentation platform, I've included a link to our free experimentation RFP template in the description below to help you evaluate your options. And if you found this useful, don't forget to subscribe for more content like this.