We migrated from a custom ML serving solution to a standard one.

It took 3 months and was one of the best decisions we made.

The custom solution had been built by a talented engineer who left. It worked but nobody fully understood it. Bugs took days to fix because debugging required archaeology. Scaling required manual intervention. New team members took weeks to become productive.

We migrated to FastAPI + Docker + Kubernetes with standard monitoring. The new system wasn't more technically impressive. But it was maintainable by anyone on the team.

This is the hidden cost of custom solutions: they encode one person's knowledge and style. When that person leaves, the system becomes a black box.

My rule now: use standard tools and frameworks unless you have a very specific reason not to. FastAPI, not a custom serving framework. Docker, not a custom deployment script. MLflow, not a custom experiment tracker. Prometheus, not a custom monitoring system.

"But the standard tool doesn't handle our specific edge case!" — Handle the edge case with a thin wrapper around the standard tool. Not by building everything custom.

Your future team members know standard tools. They don't know your custom framework. And your "custom" solution is probably reimplementing 90% of what the standard tool does, just with more bugs.

Build custom where it matters. Use standard everywhere else.