ClearML vs DagsHub
ClearML
clear.mlDagsHub
dagshub.comSide by side
| Figure | ClearML | DagsHub |
|---|---|---|
| Price from | $15/mo | $8.25/mo |
| Free plan | Yes | Yes |
| Free trial | — | Yes |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 4 | 5 |
| Spec score · ML Experiment Tracking Software | 8.7 (#3) | 9.4 (#2) |
| Spec score · Model Registry Software | 8.7 (#3) | 9.3 (#2) |
| Spec score · Data Version Control Tools | 8.7 (#4) | 9.3 (#1) |
| Spec score · Model registries | 8.7 (#8) | 9.4 (#4) |
| Company headquarters | ClearML lists its global headquarters as 2288 Fulton St, Berkeley, California 94704, USA. | LinkedIn lists San Francisco as headquarters, while Crunchbase lists Tel Aviv, Israel. |
| Deployment options | ClearML says its platform can be hosted on its servers, self-hosted, or provided as a managed service, with VPC, on-premises including air-gapped, and hybrid options. | Enterprise installations can run on-premises, in a VPC, in air-gapped environments, and in OpenShift environments. |
| Experiment tracking | The pricing page lists experiment tracking, comparisons, artifacts, metrics and plots, and Git version control integration across Open Source, Free/Pro, and Scale/Enterprise tiers. | Experiments can be tracked with Git, DVC experiments or MLflow, including metrics, parameters and artifacts. |
| Integrations | The integrations documentation lists PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, LightGBM, Optuna, Hydra, TensorBoard, Matplotlib, and LangChain among supported integrations. | Documented integrations include GitHub, GitLab, Bitbucket, DVC, MLflow, Label Studio, Jenkins, Slack, Discord, New Relic and Amazon S3. |
Plans
ClearML
- CommunityFree
- Pro$15 a month
- EnterprisePriced on request
- ScalePriced on request
DagsHub
- IndividualFree
- IndividualFree
- Team$99 a year
- Team$119 a month
- EnterprisePriced on request
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.
DagsHub
DagsHub helps developers and teams manage machine-learning projects, from data collection and dataset curation through annotation, experimentation, and model management. A project workspace can hold code, data, experiments, models, pipelines, and annotations together. The platform is designed for text, images, audio, video, documents, medical imaging, and binary files. Experiment tracking supports run comparison, metric and parameter visualization, real-time progress monitoring, and reproducibility. Data Engine provides curation, visualization, quality review, lineage, hard-example mining, labeling, auto-labeling, and data streaming. Official integrations include tools such as GitHub, Google Colab, DVC, MLflow, Jenkins, Label Studio, Google Drive, Weights & Biases, and Hugging Face; hosted projects also have support for the MLflow and Label Studio APIs. DagsHub hosts data but does not provide compute, so projects rely on local, cloud, or edge infrastructure. Enterprise installations can run on-premises, in a VPC, in air-gapped environments, and in OpenShift environments. The Individual plan is free, with stated private-repository limits; paid pricing starts at $99/user/mo billed annually, and a 14-day trial is available.