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SPECIFICTION
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ClearML vs DataChain

ClearML

clear.ml

DataChain

datachain.ai

Side by side

FigureClearMLDataChain
Price from$15/moFree
Free planYesYes
Free trial——
Open source——
WebYesYes
WindowsYes—
macOSYes—
LinuxYesYes
APIYesYes
Self-hostedYesYes
Plans published43
Spec score · Data Version Control Tools8.7 (#4)8.0 (#13)
SupportScale includes private Slack channel support and an SLA, while Enterprise includes white-glove support with a custom SLA and professional services.Customers may request security documents and compliance details by contacting [email protected].

Plans

ClearML

  • CommunityFree
  • Pro$15 a month
  • EnterprisePriced on request
  • ScalePriced on request

ClearML pricing →

DataChain

  • Open SourceFree
  • TeamsPriced on request
  • EnterprisePriced on request

DataChain pricing →

In their own words

ClearML

ClearML is an AI infrastructure platform organized into an Infrastructure Control Plane, AI Development Center, and GenAI App Engine. The Control Plane manages GPU resources across on-premises, cloud, and hybrid environments, including multi-tenant GPU-as-a-Service. The Development Center supports developing, training, testing, and deploying AI/ML models, with monitoring, pipelines, a model repository, and CI/CD integration. The GenAI App Engine deploys LLMs on compute clusters and provides networking, authentication, access control, and monitoring. Experiment tracking, comparisons, artifacts, metrics and plots, and Git version control integration appear across the listed tiers. Integrations include PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, LightGBM, Optuna, Hydra, TensorBoard, Matplotlib, and LangChain; storage integrations include AWS S3, Azure, Google Storage, MinIO, and Backblaze B2. ClearML can be hosted on its servers, self-hosted, or provided as a managed service, with VPC, on-premises including air-gapped, and hybrid options. Community is free; Pro costs $15 a month per user plus usage. Scale and Enterprise pricing is by quote.

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DataChain

DataChain is a Python library and context layer for organizing unstructured data for researchers and AI agents. It converts files from Amazon S3, Google Cloud Storage, Azure Blob Storage, local filesystems, and Hugging Face into typed, versioned datasets that can be queried. Its Dataset DB uses Pydantic schemas, versioning, and file references, while the Knowledge Base stores summaries, statistics, lineage, and code. Parallel Python pipelines can run locally or across distributed workers with asynchronous I/O and automatic checkpoints. An agent harness connects Claude Code, Cursor, and Codex through a local skill or Studio MCP. DataChain Studio adds job tracking, dataset management, experiment tracking, a model registry, team collaboration, a REST API, and GitHub, GitLab, and Bitbucket connections. Compute can run in a customer VPC, and on-premises deployment, role-based access, audit logs, and SSO/SAML are supported. The Open Source plan is free for one developer using local compute and millions of records. Teams costs what you agree with the maker and supports up to five users; Enterprise pricing is custom.

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