I would let the trains team answer this in details, but as a user moving from MLflow to trains, I can share the following insights:

MLflow and trains overlap when it comes to having a system with nice web UI to compare/log experiments/models/metrics. But MFlow lacks a crutial feature IMO which is ML/DevOps: Using MLFlow, you will have to take care of the whole maintenance of your machines, design interactions between them, etc. This is where trains shines, it provides these features out-of-the-box.

MLflow has been released a couple of months before trains, at times where the whole community of AI researchers are looking for such a tool, which might explain why they got such a big attraction. Since then their development slowed down and it is still missing features like auto devops.

Trains arrived later on and I agree that its name is not search-engine friendly, which might explain to some extend why the user base is not growing as fast as one would expect for such a nice library. But that will change, all trains needs is a bit more attention/communication