Dolt is for tabular data. It's like SQLite but with branching, versioning of the DB level. DVC is file-based. It saves large files, directories, etc to one of the supported storages - S3, GCP, Azure, etc. It's more like Git-lfs in that sense.
Another difference is that for DVC (surprisingly) data versioning itself is just one of the main fundamental layers that is needed to provide holistic ML experiments tracking and versioning. So, DVC has a layer to describe an ML project, run it, capture and version inputs/outputs. In that sense DVC becomes a more opinionated / high level tool if that makes sense.
Another difference is that for DVC (surprisingly) data versioning itself is just one of the main fundamental layers that is needed to provide holistic ML experiments tracking and versioning. So, DVC has a layer to describe an ML project, run it, capture and version inputs/outputs. In that sense DVC becomes a more opinionated / high level tool if that makes sense.