OpenML vs Polyaxon
OpenML
openml.org
Polyaxon
polyaxon.comSide by side
| Figure | OpenML | Polyaxon |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 1 | 1 |
| Spec score · Experiment Tracking | 7.4 (#10) | 7.3 (#13) |
| Spec score · ML Experiment Tracking Software | 7.3 (#22) | 7.3 (#23) |
| Experiment tracking | OpenML supports automatic tracking and sharing of pipelines, called flows, and experiment runs. | It captures run parameters, metrics, logs, code versions, artifacts, lineage, and runtime state. |
| Integrations | The documentation lists integrations for Python/scikit-learn, PyTorch, TensorFlow, R, Julia, Rust, and .NET. | The site lists integrations including PyTorch, TensorFlow, Hugging Face, LangChain, Ray, Dask, Spark, AWS, GCP, Azure, GitHub, GitLab, Slack, and Datadog. |
| Purpose | OpenML is an open platform for sharing datasets, algorithms, and experiments for machine learning. | Polyaxon is an AI engineering control plane for running machine learning, training, inference, and agent workloads on Kubernetes. |
| Support | OpenML lists its community discussions and the contact address [email protected]. | For self-hosting issues, the setup guide directs users to troubleshooting, GitHub Discussions, or GitHub bug reports and feature requests. |
Plans
In their own words
Polyaxon
Polyaxon is an AI engineering control plane for machine-learning training, inference and agent workloads on Kubernetes. It records run parameters, metrics, logs, code versions, artifacts, lineage and runtime state. Orchestration covers jobs, services, DAGs, schedules, sweeps, distributed workloads and reusable components. Isolated execution environments can be launched with controlled filesystems, network access, tools, SSH and runtime policies. Evaluation and monitoring include offline and online evaluations, logs, traces, metrics, health checks, alerts and dashboards. Listed integrations include PyTorch, TensorFlow, Hugging Face, LangChain, Ray, Dask, Spark, AWS, GCP, Azure, GitHub, GitLab, Slack and Datadog. Deployment is available through managed cloud or self-hosted Community and Enterprise editions. The core is Apache 2.0 licensed. A free Community Edition covers one cluster and namespace; paid access starts at $555/mo.