MLflow vs 
DVC
MLflow vs DVC compared for 2026 — features, license, ease of use, performance and which one to choose. Track experiments and ship models without the spreadsheet vs Git for datasets and models.
Updated regularly · curated by OpenSourceAI.tech
MLflow vs DVC at a glance
| Spec | MLflow | DVC |
|---|---|---|
| Category | ML frameworks & MLOps | ML frameworks & MLOps |
| Type | Experiment tracking | Data versioning |
| License | Apache-2.0 | Apache-2.0 |
| Runs locally | Yes | Yes |
| Primary language | Python | Python |
| Ease of use | Beginner | Intermediate |
| Best for | any team that has lost track of which run produced the good model | reproducing a result six months later, exactly |
| GitHub stars | 27.1k | 15.8k |
How MLflow and DVC score
| Criterion | MLflow | DVC |
|---|---|---|
| Popularity | 3.5 | 3.5 |
| Maintenance | 5.0 | 5.0 |
| Ease of use | 5.0 | 3.5 |
| Privacy | 5.0 | 5.0 |
| License freedom | 5.0 | 5.0 |
Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.
What each one is
MLflow
MLflow records every run, its parameters and its metrics, then packages the winning model for deployment — the open answer to Weights & Biases.
- Self-hostable, no per-seat pricing
- Works with any framework
- Model registry and deployment included
DVC
DVC versions the data and the models that Git cannot hold, keeping the whole pipeline reproducible from a commit hash.
- Works alongside Git, not against it
- Storage-agnostic (S3, GCS, SSH, local)
- Makes pipelines reproducible by construction
Key differences
MLflow is experiment tracking, while DVC is data versioning. MLflow leans more beginner-friendly, whereas DVC is more suited to intermediate users. In short, MLflow fits any team that has lost track of which run produced the good model, and DVC fits reproducing a result six months later, exactly.
Which should you choose?
Choose MLflow for any team that has lost track of which run produced the good model. Choose DVC for reproducing a result six months later, exactly.
There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.
Frequently asked questions
Is MLflow or DVC easier to use?
MLflow is generally the easier of the two to get started with, while DVC rewards more setup with more control.
Are MLflow and DVC free?
MLflow is free and open source (Apache-2.0), and DVC is free and open source (Apache-2.0). Neither charges for the core software.
Can I run MLflow and DVC locally?
MLflow: yes · DVC: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
MLflow vs DVC — which should I pick in 2026?
Choose MLflow for any team that has lost track of which run produced the good model. Choose DVC for reproducing a result six months later, exactly.
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