Machine Learning · head to head
ClearML vs Deepnote

ClearML
Machine Learning
Open-source MLOps platform for experiment tracking and orchestration
- From
- Free
- Rated
- -

Deepnote
Business Intelligence
Collaborative cloud workspace for data analytics and machine learning
- 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; Deepnote free plan limited to 3 editors, restricting team usage
- They diverge on capability: ClearML covers Experiment tracking, Deepnote covers Collaborative notebooks.
- Prices and features above were last checked on 29 August 2026.
Where they differ
Only the attributes on which ClearML and Deepnote actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
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
- Experiment tracking
- Remote execution
- Data versioning
- Pipelines
Only in Deepnote
- Collaborative notebooks
- Interactive dashboards
- Data agent building
- Scheduled pipelines
- Model management
- 100+ integrations
- GPU support
- API deployment
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 Deepnote
- Moving training from laptops to shared GPU hardware without repackagingnot Deepnote
- Versioning datasets alongside the experiments that consumed themnot Deepnote
Deepnote
- Data exploration and analysis workflowsnot ClearML
- Building interactive business intelligence dashboardsnot ClearML
- Collaborative machine learning model developmentnot ClearML
- Automating ETL and data pipeline orchestrationnot ClearML
- Creating shareable reports without exportsnot 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
Deepnote
- Free plan limited to 3 editors, restricting team usage
- Limited revision history on free plan compared to competitors
- Requires Team plan or higher for automated scheduling
- GPU support incurs additional charges beyond base subscription
- No mentioned offline capability
Pricing, plan by plan
ClearML
Free- Open sourceFree
- Experiment tracking
- Pipelines
- Self-hosted server
Deepnote
Free- FreeFree
- Up to 3 editors
- Up to 5 projects
- Limited Deepnote AI
- Team$39/month
- Unlimited viewers and notebooks
- Full Deepnote AI access
- Premium integrations
- Enterprise$null/custom
- Everything in Team plan
- Custom contracts
- Priority support
Which should you pick?
Choose ClearML if
- You need experiment tracking.
- You want to start without paying.
- You work on Linux, macOS, Windows, Docker, Kubernetes.
- You also want remote execution.
Choose Deepnote if
- You need collaborative notebooks.
- You want to start without paying.
- You work on Web, API.
- You also want interactive dashboards.
Questions people ask
- Is ClearML or Deepnote better?
- Neither clearly leads. ClearML starts at Free and Deepnote at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, ClearML or Deepnote?
- ClearML starts at Free and Deepnote at Free.
- Does ClearML or Deepnote run on more platforms?
- ClearML runs on Linux, macOS, Windows, Docker, Kubernetes. Deepnote runs on Web, API.
- 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 Deepnote is typically brought in for.
- What can ClearML do that Deepnote cannot?
- ClearML covers Experiment tracking, Remote execution, Data versioning, Pipelines. Deepnote covers Collaborative notebooks, Interactive dashboards, Data agent building, Scheduled pipelines.
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.
Deepnote: What is included in the free Deepnote plan?
The free plan includes up to 3 editors, up to 5 projects, limited Deepnote AI, basic machines with 5 GB RAM, and 7-day revision history.
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.
Deepnote: What data sources can Deepnote integrate with?
Deepnote integrates with 100+ data sources including major data warehouses like Snowflake, BigQuery, and Redshift, as well as BI platforms like Looker, Tableau, and Power BI.
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.
Deepnote: Does Deepnote support collaboration?
Yes, Deepnote provides real-time collaborative notebooks where multiple team members can work simultaneously. The Team plan allows unlimited viewers and notebooks.
SourceDeepnote: What compliance certifications does Deepnote have?
Deepnote is SOC 2, HIPAA, GDPR, and CCPA compliant and offers role-based access control, single sign-on, and directory synchronization.
SourceRelated pages
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- Deepnote vs Neptune.ai
- Deepnote vs Dataiku
- Deepnote vs Pachyderm
- Deepnote vs Azure Machine Learning
- Deepnote vs Domino Data Lab
- Deepnote vs DVC
- Deepnote vs AWS SageMaker
- Deepnote vs Google Vertex AI
- Deepnote vs DataRobot
- Deepnote vs Pinecone
- Deepnote vs Python
- Deepnote vs PyTorch
- Deepnote vs scikit-learn
- Deepnote vs Apache Spark MLlib
- Deepnote vs Weaviate
- Deepnote vs Fabi
- Deepnote vs Hex
- Deepnote vs Mode
- Deepnote vs TIBCO Spotfire
- Deepnote vs Yellowfin
- Deepnote vs Klipfolio
- Deepnote vs Domo
- Deepnote vs Periscope Data
- Deepnote vs Cyfe
- Deepnote vs DashThis
- Deepnote vs Grow
- Deepnote vs Luzmo
- Deepnote vs ThoughtSpot
- Deepnote vs Cube
- Deepnote vs Pigment
- Deepnote vs Evidence
- Deepnote vs Google Data Studio
- Deepnote vs Lightdash
