DVC vs MLflow
A side-by-side comparison of DVC and MLflow, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | DVC | MLflow |
|---|---|---|
| Category (differs) | Data Ops | Observability |
| Pricing | FREE | FREE |
| License | Open source | Open source |
| Deployment (differs) | — | Self-host |
| Platforms (differs) | CLI | Web, CLI, API, Linux, macOS, Windows |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | lakeFS | Linux Foundation |
The honest brief
DVC
A lightweight Git extension that versions datasets and ML models next to code with no server to run — unlike data-lake platforms such as lakeFS.
- Free and open source
- Versions data and models with Git
- No server to operate
- Works with any storage backend
- Reproducible ML pipelines
- CLI-centric learning curve
- Large-scale lakes better served by lakeFS
- Roadmap now tied to lakeFS
MLflow
The most widely adopted open-source option: one platform spanning tracing, evals, prompt registry, and classic ML.
- Fully open source, no lock-in
- OpenTelemetry-based, framework-agnostic
- Built-in metrics and LLM judges
- Large community + Linux Foundation backing
- Self-host on your own infrastructure
- Self-hosting adds operational overhead
- Broad scope can feel heavy for simple needs
- Managed convenience needs Databricks or DIY
- UI less polished than some SaaS rivals