DagsHub

GitHub-style collaboration platform for ML datasets, experiments, and models with MLflow and DVC under the hood.

Freemium· Free Individual tier; Team $99-$119/user/mo; Enterprise customFine-tuning

Best for

Pick DagsHub if you want a GitHub-like home for ML projects that unifies dataset versioning, experiments, and model tracking without rolling your own MLOps stack.

Skip if

Skip it if your team is happy with a bespoke MLflow + S3 + Weights & Biases setup, or if you need a fully self-hostable open-source platform.

DagsHub is a hosted platform for managing the messy parts of machine learning: large datasets, experiment runs, models, and the code that ties them together. It bundles Git for code, DVC for data versioning, MLflow for experiment tracking, and Label Studio for annotation into one repo-shaped interface, so an ML team can browse a dataset diff, compare runs, and pull a model checkpoint without stitching five tools together.

It is aimed at ML engineers and data scientists who have outgrown notebooks-and-S3 but do not want to build a full MLOps stack in-house. The free Individual tier covers solo work and public repos with 20GB of storage and 100 tracked experiments; Team plans land around $99-$119 per user per month with private repos and 1TB of data, and Enterprise scales to petabyte workloads used by groups at Google, Pfizer, Intel, and academic labs.

The platform's edge is its openness to external storage (S3, GCS, Azure) and open formats: you keep your data in your bucket and DagsHub indexes it. Multimodal annotation, auto-labeling, notebook diffing, and model registry round it out. The product itself is commercial, though many of the underlying tools it leans on (DVC, MLflow) are open source.

Editor's take

DagsHub is the closest thing the ML world has to a GitHub for data, and it leans hard on open tooling rather than reinventing it. The free tier is genuinely useful for solo work, but the per-seat Team pricing means you should be sure the unified UI is worth it before you commit a whole team.

— The AI Tool Bible editorial team