I gets super slow (waiting minutes) when there are a few thousand files tracked. Thousands files have to be tracked, if you have e.g. a 10GB file per day and region and artifacts generated from it.
You are encouraged (it only can track artifacts) if you model your pipeline in DVC (think like make). However, it cannot run tasks it parallel. So it takes a lot of time to run a pipeline while you are on a beefy machine and only one core is used. Obviously, you cannot run other tools (e.g. snakemake) to distribute/parallelize on multiple machines. Running one (part of a) stage has also some overhead, because it does commit/checks after/before running the executable of the task.
Sometimes you get merge conflicts, if you run a (partial parmaretized) stage on one machine and the other part on the other machine manually. These are cumbersome to fix.
Currently, I think they are more focused on ML features like experiment tracking (I prefer other mature tools here) instead of performance and data safety.
There is an alternative implementation from a single developer (I cannot find it right now) that fixes some problems. However, I do not use this because it propably will not have the same development progress and testing as DVC.
This sounds negative but I think it is currently the one of the best tools in this space.