Machine Learning · head to head
ClearML vs DVC
ClearML
Machine Learning
Open-source MLOps platform for experiment tracking and orchestration
- From
- Free
- Rated
- -
DVC
Machine Learning
Data version control for machine learning projects
- From
- Free
- Rated
- -
The short version
- Each has a real cost: ClearML broad scope means more to learn and more to run than a focused tracking tool; DVC no pricing published for enterprise lakeFS option; requires booking a demo
- They diverge on capability: ClearML covers Remote execution, DVC covers Pipeline management.
Where they differ
Only the attributes on which ClearML and DVC actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in ClearML
- Remote execution
- Pipelines
Only in DVC
- Pipeline management
- Remote storage
- Git integration
- Git
- S3
- Azure Blob
- Google Cloud Storage
- SSH
Both cover
- Experiment tracking
- Data versioning
What people use each for
The jobs each tool is most often brought in to do.
ClearML
- Tracking experiments across a team so results are reproduciblenot DVC
- Moving training from laptops to shared GPU hardware without repackagingnot DVC
- Versioning datasets alongside the experiments that consumed themnot DVC
DVC
- Machine learningnot ClearML
- Data analysisnot ClearML
- Model trainingnot ClearML
- Predictive analyticsnot ClearML
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
ClearML
- Broad scope means more to learn and more to run than a focused tracking tool
- Self-hosting the server is real infrastructure — database, file storage and web server
- Documentation quality is uneven across the newer parts of the platform
- Smaller community than the most popular tracking tools, so fewer worked examples exist
DVC
- No pricing published for enterprise lakeFS option; requires booking a demo
- Free/open-source products may have limited features for production enterprises
Pricing, plan by plan
ClearML
Free- Open sourceFree
- Experiment tracking
- Pipelines
- Self-hosted server
DVC
Free- Open SourceFree
- Data versioning
- Pipeline management
- Experiment tracking
- DVC StudioFree
- Web UI
- Team collaboration
- Visualizations
Which should you pick?
Choose ClearML if
- You need remote execution.
- You want to start without paying.
- You work on Linux, macOS, Windows, Docker, Kubernetes.
- You also want pipelines.
Choose DVC if
- You need pipeline management.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want remote storage.
Questions people ask
- Is ClearML or DVC better?
- Neither clearly leads. ClearML starts at Free and DVC at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, ClearML or DVC?
- ClearML starts at Free and DVC at Free.
- Does ClearML or DVC run on more platforms?
- ClearML runs on Linux, macOS, Windows, Docker, Kubernetes. DVC runs on Linux, Mac, Windows.
- Can I use ClearML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is ClearML best used for?
- ClearML is most often used for tracking experiments across a team so results are reproducible, moving training from laptops to shared gpu hardware without repackaging, versioning datasets alongside the experiments that consumed them. Of those, tracking experiments across a team so results are reproducible and moving training from laptops to shared gpu hardware without repackaging are not what DVC is typically brought in for.
- What can ClearML do that DVC cannot?
- ClearML covers Remote execution, Pipelines. DVC covers Pipeline management, Remote storage, Git integration, Git. Both handle Experiment tracking, Data versioning.
Answered from the vendors’ own pages
ClearML: Is ClearML free?
The open-source version is free and self-hostable. Hosted and enterprise tiers are paid.
DVC: Is DVC free?
Yes, DVC is free and open source for individual data scientists. lakeFS is also free and open source, with an Enterprise version available for enterprise teams that requires contacting the vendor for pricing.
SourceClearML: How much code does tracking require?
Very little — adding a couple of lines to an existing training script captures parameters, metrics and environment automatically.
DVC: How much does DVC Enterprise cost?
DVC does not publish pricing for its enterprise offerings. Interested organizations must book a demo or contact the vendor directly to discuss pricing for enterprise lakeFS deployments.
SourceClearML: Does ClearML replace MLflow?
It covers MLflow’s tracking and adds orchestration, remote execution and data versioning. Whether that breadth is an advantage or extra weight depends on whether you need the rest.
Related pages
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