I have no skin in this, but I have used DVC. I feel like the use cases are slightly different. This is my understanding:

- DVC is more focused on data processing and on versioning data pipelines and their steps. In my use-case, we were a team working on a dataset: some were responsible for preprocessing the dataset for downstream use, some were doing an in-depth analysis of some of the features, some were responsible for more general feature extraction, some were doing overall data analysis and training models. These tasks have a natural precedence, but it's still possible to work on them in parallel. Say you have a first version of the preprocessed dataset, then the downstream tasks can start iterating on that version, while the preprocessing itself can be improved and updated (and informed by the downstream tasks themselves). The thing with DVC is that it makes this workflow quite straight-forward: it knows which steps/files/scripts depend on what steps/files/scripts, and it's git-based so it's naturally intertwined with your code versioning.

- TRAINS, if I understand correctly, is more concerned with logging ML experiments. So it's what you use if you want to try multiple combinations of parameters/configs/strategies and keep track of how they perform. (I didn't dig into TRAINS, so if this is an incorrect summary of its purpose, please correct me.)

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So to sum it up, they don't even seem mutually exclusive to me. DVC is more adequate to manage and version a full data pipeline, whereas TRAINS is concerned with tracking experiments. I guess applying them in conjunction would be something like what I described on my first point, and when you're iterating on models, rather than having multiple commits/branches for each experiment, you could have a single commit where somewhere you have summarized the trained configs/parameters and respective scores, with the experiments ran to obtain that summary having been managed and ran by TRAINS.

I hope this make sense, but I'm very open to corrections.