ClearML
7.4#1 in gpu cluster management softwareFrom $15 a monthFree planWebAPILinuxmacOS
VisitClearML vs dstack
What both publish
| Fact | ClearML | dstack |
|---|---|---|
| Integrations | The integrations documentation lists PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, LightGBM, Optuna, Hydra, TensorBoard, Matplotlib, and LangChain among supported integrations. | Listed backend types include AWS, Azure, GCP, Kubernetes, Slurm, Runpod, Nebius, Lambda, and other cloud providers. |
| Support | Scale includes private Slack channel support and an SLA, while Enterprise includes white-glove support with a custom SLA and professional services. | The documentation directs users to report issues on GitHub and ask questions in the dstack Discord server. |
ClearML plans
- CommunityFree
- Pro$15 a month
- EnterprisePriced on request
- ScalePriced on request
dstack plans
- dstack OSSFree
- dstack Sky GPU MarketplacePriced on request
Pick ClearML if…
ClearML suits AI and machine-learning teams that need experiment management and model development tools alongside GPU resource management. Its hosting choices may also suit teams with self-hosted, VPC, on-premises, or hybrid requirements.
Pick dstack if…
dstack suits teams orchestrating AI workloads across different accelerator and infrastructure environments. It is also suited to users who want to configure workloads with YAML and manage resources through a CLI or HTTP API.