MLflow vs DVC
Both are alternatives to Weights & Biases. Here's how they stack up — verified facts, no spin.
Also searched as DVC vs MLflow — same comparison, one verdict.
MLflow
TOP PICKThe open standard. Tracking, registry, projects and deployment in one.
MLflow is the most widely adopted open ML platform, and its scope is why: experiment tracking, a model registry with stage transitions, reproducible project packaging and deployment tooling, all Apache-2.0. It runs as a local file store for one person or as a server with a database and object store for a team, integrates with essentially every framework, and is supported natively by every major cloud — so it is the option least likely to become a dead end.
DVC
Git for data and pipelines. Reproducibility rather than dashboards.
DVC treats datasets, models and pipelines the way Git treats code: version them, branch them, and reproduce any past state exactly. It stores large files in your own object storage and keeps lightweight pointers in Git, so `git checkout` of an old commit brings the matching data and model with it. Its experiment tracking is a consequence of that design rather than the headline. Apache-2.0, and it addresses the failure most trackers do not — not knowing which data produced a result.
Side by side
| MLflow | DVC | |
|---|---|---|
| Sovereignty Score | 93 | 94 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | Apache-2.0 | Apache-2.0 |
| Pricing | Free, Apache-2.0. Managed versions are sold by the clouds if you want one. | Free, Apache-2.0. You supply the object storage. |
MLflow is Macrostack's recommended Weights & Biases alternative, so it's our pick here.
MLflow
Strengths
- +Covers tracking, registry, packaging and deployment — not just tracking
- +The de facto standard; integrated with every major framework and cloud
- +Scales from a local directory to a full server deployment
- +Apache-2.0 with unlimited seats
Trade-offs
- −UI is functional rather than pleasant, and slows on very large run counts
- −Team deployment means running a server, a database and object storage
- −No built-in sweep orchestration comparable to W&B's
DVC
Strengths
- +Data and models versioned alongside code in Git
- +True reproducibility — check out a commit, get the matching data
- +Storage-agnostic: S3, GCS, Azure, SSH or a local disk
- +Apache-2.0, no server to run
Trade-offs
- −Not a metrics dashboard — different tool for a different problem
- −Git-centric workflow takes adjusting to
- −Large binary handling needs care in the repository
Related alternative guides
Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.