yep some of these ML platform tools only make sense once you reach a certain scale/set of problems. For flyte, it was created because Lyft had a very heterogeneous set of tools one could build a ML pipeline with. So they made it really easy to integrate and importantly serialize, data between systems. E.g. sql -> python -> spark -> python. But if all your data fits in memory, or you only one system, you might not be a great fit for Flyte.

What I try to do, is understand who created the platform, and understand the environment of that company. Then that will give you a better idea of whether it makes sense for you or not.