DVC alternatives

2 tools to consider instead of DVC, shown against it.

DVC MLflow Metaflow
Vendor Treeverse (lakeFS); formerly stewarded by Iterative Linux Foundation (LF AI & Data); originated at Databricks Outerbounds; originated at Netflix
Pricing model Open source + paid options Open source + paid options Open source + paid options
Free tier Yes Yes Yes
Deployment Self-hosted Cloud, Self-hosted Cloud, Self-hosted
Open source Yes (Apache-2.0) Yes (Apache-2.0) Yes (Apache-2.0)
Best for Teams wanting Git-style version control and reproducibility for datasets, models, and pipelines. Teams wanting free, self-hostable experiment tracking without committing to a vendor's hosted SaaS. Data science teams wanting a lightweight Python framework to move pipelines from notebook to production.
Pricing

DVC is free, open-source software; any cost comes from the storage remote (cloud or on-prem) you point it at.

Pricing has not been verified yet — see the vendor's site.

MLflow itself is free and open source; any cost comes from the infrastructure or managed platform (e.g. Databricks) you run it on.

Pricing has not been verified yet — see the vendor's site.

Metaflow the framework is free and open source; scaling costs come from the cloud compute/storage it's configured to use, and Outerbounds' separate managed platform is priced independently.

Pricing has not been verified yet — see the vendor's site.

Features
  • Git-like versioning for datasets and models
  • Pipeline definition and dependency tracking (DAGs)
  • Reproducible experiment runs tied to code/data versions
  • Storage-agnostic remotes (S3, GCS, Azure, local)
  • Metric and parameter diffing across commits
  • Data registries for sharing datasets across projects
  • Experiment tracking (parameters, metrics, artifacts)
  • Model Registry with staged versioning
  • Reproducible project packaging
  • Model packaging across ML frameworks
  • REST API and CLI for automation
  • Built-in model serving for quick deployment
  • LLM tracing and evaluation
  • Autologging for popular ML frameworks
  • Pipeline-as-Python-code with step decorators
  • Automatic versioning of code, data, and run results
  • Seamless scaling from laptop to cloud compute
  • Built-in support for distributed training and GPUs
  • Resumable and inspectable past runs
  • Dependency and environment management per step

In the index now