ClearML vs Koordinator
ClearML
clear.mlKoordinator
koordinator.shSide by side
| Figure | ClearML | Koordinator |
|---|---|---|
| Price from | $15/mo | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 4 | 0 |
| Spec score · GPU Cluster Management Software | 8.7 (#2) | 7.5 (#10) |
Plans
ClearML
- CommunityFree
- Pro$15 a month
- EnterprisePriced on request
- ScalePriced 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.
Koordinator
Koordinator is a free, self-hosted QoS scheduling system for Kubernetes microservices, AI, big-data, audio, and video workloads. It adds priority and quality-of-service controls so different pods and workload types can share nodes. Application profiling supports resource overcommitment while preserving QoS guarantees, and fine-grained isolation targets latency-sensitive work alongside batch jobs. Customizable policies cover Web Service, Spark, Presto, TensorFlow, and PyTorch. QoSManager tunes pod isolation using profiling, interference detection, and SLO settings. Koord-Queue supports TFJob, PyTorchJob, Spark, Argo Workflow, Ray, and native Kubernetes Jobs, with Kueue AdmissionCheck compatibility. YARN Copilot connects Hadoop YARN jobs with Kubernetes pods. Koordinator works alongside kube-scheduler, using control-plane and node components without invasive native changes. Helm 3.5 or newer and Kubernetes 1.18 or newer are required.