Machine Learning & Data Science · head to head
Comet ML vs DVC

Comet ML
Machine Learning & Data Science
Platform for tracking, comparing, and optimizing ML experiments
- From
- Free
- Rated
- -

DVC
Machine Learning & Data Science
Data version control for machine learning projects
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Comet ML the free cloud tier caps data at 25,000 spans a month with 60 day retention; 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.
- They diverge on capability: Comet ML covers Code versioning, DVC covers Data versioning.
Where they differ
Only the attributes on which Comet ML and DVC 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 Comet ML
- Code versioning
- Model registry
- Hyperparameter optimization
- Production monitoring
- PyTorch
- TensorFlow
- Keras
- scikit-learn
Only in DVC
- Data versioning
- Pipeline management
- Remote storage
- Git integration
- Git
- S3
- Azure Blob
- Google Cloud Storage
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.
Comet ML
- Tracking machine learning experiments, metrics and model versionsnot DVC
- Monitoring and evaluating LLM applications with tracingnot DVC
DVC
- Machine learningnot Comet ML
- Data analysisnot Comet ML
- Model trainingnot Comet ML
- Predictive analyticsnot Comet ML
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
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.
Pricing, plan by plan
Comet ML
Free- FreeFree
- 100 experiments
- Basic features
- Community support
- Team$179/month
- Unlimited experiments
- Team collaboration
- Priority support
DVC
Free- Open SourceFree
- Data versioning
- Pipeline management
- Experiment tracking
- DVC StudioFree
- Web UI
- Team collaboration
- Visualizations
Which should you pick?
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.
Choose DVC if
- You need data versioning.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want pipeline management.
Questions people ask
- Is Comet ML or DVC better?
- Neither clearly leads. Comet ML 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, Comet ML or DVC?
- Comet ML starts at Free and DVC at Free.
- Does Comet ML or DVC run on more platforms?
- Comet ML runs on Web, Linux, Mac, Windows. DVC runs on Linux, Mac, Windows.
- Can I use Comet ML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Comet ML best used for?
- Comet ML is most often used for tracking machine learning experiments, metrics and model versions, monitoring and evaluating llm applications with tracing. Of those, tracking machine learning experiments, metrics and model versions and monitoring and evaluating llm applications with tracing are not what DVC is typically brought in for.
- What can Comet ML do that DVC cannot?
- Comet ML covers Code versioning, Model registry, Hyperparameter optimization, Production monitoring. DVC covers Data versioning, Pipeline management, Remote storage, Git integration. Both handle Experiment tracking, Linux support, Mac support, Windows support.
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- DVC vs AWS SageMaker
- DVC vs Google Vertex AI
- DVC vs Azure Machine Learning
- DVC vs DataRobot
- DVC vs Snowflake
- DVC vs TensorFlow
- DVC vs Keras
- DVC vs MLflow
- DVC vs Jupyter
- DVC vs PyTorch
- DVC vs scikit-learn
- DVC vs Apache Spark MLlib
- DVC vs Weights & Biases
- DVC vs Alteryx
- DVC vs Anaconda
- DVC vs Databricks
- DVC vs Dataiku
