We wrote up the general framework we use for build-vs-buy decisions here - how we think about the real costs, the six questions we ask, and why these decisions are harder to evaluate honestly than they look. This post is about what happens when you apply that thinking to crash reporting specifically.

Because crash reporting has a few things that make the build-vs-buy tradeoff especially deceptive. The first 20% of the problem is easy enough to make you think you can do the whole thing. You usually can't.

Capture is the easy part

Capturing a crash dump is straightforward. Most platforms have hooks for it. If you're on Windows you've got structured exception handling. macOS and Linux give you signal handlers. Game engines like Unreal and Unity have their own crash hooks. A competent engineer can get crash data flowing to a server in a day or two.

That's the part that tricks people. You get dumps landing in an S3 bucket and think you're most of the way there. You're not. You've done the easy 20%.

The other 80% is everything that turns raw crash data into something your team can actually use to fix bugs:

Grouping duplicate crashes so you're not looking at ten thousand individual reports when it's really three bugs. Symbolicating stack traces so you get function names and line numbers instead of memory addresses. Tracking crash rates across versions so you know if your fix actually fixed anything. Alerting the right people at the right time without flooding a Slack channel. Giving non-engineers on your team a way to see what's happening without learning to read a stack trace.

Each of those is its own project. And getting them to work reliably at scale, across platforms, across build configurations - that's years of iteration. It's the kind of thing that only gets good when it's someone's entire job. It's been ours for twenty years.

"But what about AI?"

Fair thought. This is 2026. AI-assisted development has genuinely changed the math on building internal tools. A competent engineer with Cursor or Claude Code could scaffold a crash collection endpoint in an afternoon.

But the hard part of crash reporting was never writing the code. It's the domain knowledge baked into the workflow. How do you group crashes that have slightly different stack traces but the same root cause? How do you handle symbolication when your build pipeline produces symbols in three different formats? What do you do when a customer reports a crash and you need to find their specific report among hundreds of thousands?

AI tools compress the time it takes to write code. They don't compress the time it takes to understand a problem domain deeply enough to build good tooling around it. We've spent twenty years learning the edge cases. An LLM can't shortcut that for you in a sprint.

The feature requests that eat you alive

This is the part I've watched play out hundreds of times from the other side of the counter.

You got the prototype working. Crashes come in, you can see a stack trace, maybe you even wrote a little grouping logic. Now what?

Your PM wants to know crash rates by version. Your QA lead wants email alerts when a new crash pattern appears. Someone asks if you can integrate it with Jira. A customer reports a crash and you need to find their specific report among thousands. The junior dev on your team asks why the stack traces are all mangled on the release build and now you're learning about symbol servers.

Every one of these is a reasonable request. Every one of these is also a project. And none of them are your product. At this point the implementation is getting expensive, and the fact that it's not baked into your team's workflow is doubling that expense.

The backlog for the thing that isn't your product starts competing with the backlog for the thing that is. And the thing that isn't your product always loses - which means it stays half-built, which means your team is getting less value from it than they would from a tool that already has all of this.

What about open-source crash capture?

Ok, so you're thinking: Joey, joey, joey. You fool! You think I'd build it from scratch? No way! I'm going to use an open-source tool like a pro.

Aren't you clever. Open-source libraries like Google Breakpad and Crashpad are genuinely useful. Our CTO Bobby wrote what's become the go-to guide for getting Crashpad set up, and we integrate with both at BugSplat. If you decide to build, start there.

But a crash capture library is not a crash reporting tool. Breakpad will get you a minidump. It won't group your crashes, track your crash rate over time, symbolicate your release builds automatically, alert you when something new shows up, or give your team a UI that doesn't require a debugger.

Starting with Breakpad or Crashpad saves you time compared to starting from zero. But you're still building the entire reporting layer on top - which is where all the real complexity lives.

Pick a tool and go ship your thing

Crash reporting is a solved problem. Crashes are crashes. Stack traces are stack traces. The workflow for finding, prioritizing, and fixing them is well-understood.

There are several good crash reporting tools on the market depending on your platform and stack. We're one of them, and we think we're pretty good at it, but it doesn't hurt to to try a few and see which one fits. The important thing is to stop spending your team's time on a problem someone else has already spent years solving.

Every hour your team spends on crash reporting infrastructure is an hour they're not spending on the product your customers actually care about.

Fix the crashes. Ship the thing.