Machine Learning & Data Science · head to head
DVC vs Comet ML
DVC
Machine Learning & Data Science
Data version control for machine learning projects
- From
- Free
- Rated
- -
Comet ML
Machine Learning & Data Science
Platform for tracking, comparing, and optimizing ML experiments
- From
- Free
- Rated
- -
The short version
- Each has a real cost: DVC dVC is Apache 2.0 licensed open source with no enterprise tier or paid support offering documented in the project itself; teams needing SLA-backed support get nothing from the DVC project directly.; Comet ML the free cloud tier caps data at 25,000 spans a month with 60 day retention
- They diverge on capability: DVC covers Data versioning, Comet ML covers Code versioning.
Where they differ
Only the attributes on which DVC and Comet ML actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).
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 DVC
- Data versioning
- Pipeline management
- Remote storage
- Git integration
- Git
- S3
- Azure Blob
- Google Cloud Storage
Only in Comet ML
- Code versioning
- Model registry
- Hyperparameter optimization
- Production monitoring
- PyTorch
- TensorFlow
- Keras
- scikit-learn
Both cover
- Experiment tracking
- Linux support
- Mac support
- Windows support
What people use each for
The jobs each tool is most often brought in to do.
DVC
- Machine learningnot Comet ML
- Data analysisnot Comet ML
- Model trainingnot Comet ML
- Predictive analyticsnot Comet ML
Comet ML
- Tracking machine learning experiments, metrics and model versionsnot DVC
- Monitoring and evaluating LLM applications with tracingnot DVC
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
DVC
- DVC is Apache 2.0 licensed open source with no enterprise tier or paid support offering documented in the project itself; teams needing SLA-backed support get nothing from the DVC project directly.
Comet ML
- The free cloud tier caps data at 25,000 spans a month with 60 day retention
- Retention stays at 60 days even on the paid Pro plan, and extending it is a $29 per 100k spans add on
- Overage on Pro is $5 per additional 100,000 spans
- The free MLOps tier is a single user with 100 GB of storage and training hours governed by a fair usage policy
- Pro MLOps is $19 per user per month and caps the team at 10 users
Pricing, plan by plan
DVC
Free- Open SourceFree
- Data versioning
- Pipeline management
- Experiment tracking
- DVC StudioFree
- Web UI
- Team collaboration
- Visualizations
Comet ML
Free- FreeFree
- 100 experiments
- Basic features
- Community support
- Team$179/month
- Unlimited experiments
- Team collaboration
- Priority support
Which should you pick?
Choose DVC if
- You need data versioning.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want pipeline management.
Choose Comet ML if
- You need code versioning.
- You want to start without paying.
- You work on Web, Linux, Mac, Windows.
- You also want model registry.
Questions people ask
- Is DVC or Comet ML better?
- Neither clearly leads. DVC starts at Free and Comet ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, DVC or Comet ML?
- DVC starts at Free and Comet ML at Free.
- Does DVC or Comet ML run on more platforms?
- DVC runs on Linux, Mac, Windows. Comet ML runs on Web, Linux, Mac, Windows.
- Can I use DVC for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is DVC best used for?
- DVC is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Comet ML is typically brought in for.
- What can DVC do that Comet ML cannot?
- DVC covers Data versioning, Pipeline management, Remote storage, Git integration. Comet ML covers Code versioning, Model registry, Hyperparameter optimization, Production monitoring. Both handle Experiment tracking, Linux support, Mac support, Windows support.