The Case Against Building Your Own Data Platform

February 28, 2026  ·  9 min read

I have a lot of respect for engineers who build data platforms from scratch. It's genuinely hard work, and the people who do it are usually technically excellent. But I've also watched the decision to build rather than buy derail data programs at companies that could not afford the distraction.

This is my honest assessment of why building your own data platform is usually the wrong call — and the narrow cases where it isn't.

The build cost is always underestimated

When an engineering team decides to build a data platform, they typically estimate the cost of the first version. That estimate is usually accurate. What's never accurate is the total cost of ownership over three years — which is the relevant number.

Version 1 gets built. Then the requirements change. The data volume grows by 50x. A new cloud provider needs to be supported. A security audit reveals gaps in the access control model. The original engineers leave and their institutional knowledge walks out with them. Every one of these is a multi-week engineering project that competes with product work for resources.

I've talked to engineering leaders at companies who built their own platforms and they consistently say the same thing: if they knew at the start what the three-year cost was going to be, they would have bought. Not all of them — but most.

Your platform doesn't improve unless you invest in it

A commercial data platform has a team of engineers whose full-time job is making it better. Query optimization, new connectors, improved observability, better security controls — these improvements happen continuously and you get them as updates. Your internal platform improves only when your engineers aren't doing something else.

In practice, internal platforms almost never have dedicated teams. They're owned by the data engineering team, which also owns all the pipelines, all the schemas, all the integrations, and the on-call rotation. Platform improvements are backlog items that get deprioritized when production issues come in. The platform slowly falls behind the state of the art.

Three years after the initial build, you often find yourself with a platform that was modern when it was designed but is now two generations behind where it would have been if you'd stayed on a maintained external product.

Talent is harder to retain

Strong data engineers want to work on interesting problems. Maintaining an internal platform that isn't advancing is not interesting after the first year. The engineers who built it will often leave once the interesting parts are done, replaced by engineers who have to learn an undocumented system without the original context.

This creates a knowledge preservation problem that compounds over time. Systems accumulate undocumented design decisions, workarounds for bugs that no longer exist, and dependencies that nobody can explain. Onboarding new engineers takes months. Any significant change to the system carries high risk because the blast radius is unknown.

The security and compliance overhead

SOC 2, GDPR, HIPAA, CCPA — compliance requirements for data platforms are real and getting more complex. A commercial platform maintains these certifications as part of their business model. Your internal platform needs someone to own this, keep certifications current, respond to audits, and implement required controls when regulations change.

This isn't glamorous work, and it often falls to whoever is available rather than whoever is best suited for it. When a compliance issue surfaces — and it will — the remediation is expensive, time-pressured, and disruptive to everyone else's work.

When building makes sense

There are cases where building is the right answer. If your data platform is your product — if the thing you sell is built on proprietary data infrastructure that is itself a competitive advantage — then build. The investment is core to your business model.

If you have genuinely unique requirements that no commercial platform can meet and you've done the evaluation honestly, build. If you're operating at a scale where the cost of commercial platforms exceeds the cost of engineering (and you're talking about billions of events per day, not millions), build.

For everyone else, the math rarely works out. The engineering team's time is better spent on the product, the customer, and the problem you're actually in business to solve. Data infrastructure is critical, but it shouldn't be where you differentiate.

The question to ask before starting any build effort: does building this platform make our product better, or does it just give us something to maintain?