| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | — | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Deep Learning Software | 8.0 (#12) | 7.4 (#25) |
| Intended users | The project describes its audience as deep learning researchers and practitioners working on large-scale training and inference. | Ray’s security documentation describes Ray developers running local single-node clusters or remote multi-node clusters on infrastructure provided by platform providers. |
| Monitoring | The DeepSpeed Monitor can log live training metrics to TensorBoard, WandB or CSV files. | Ray Train provides user guides for monitoring and logging metrics during training. |
| Purpose | DeepSpeed is a deep learning optimization library for distributed model training and inference. | Ray Train distributes model training compute to worker processes across a Ray cluster. |
| Security | The repository links to a SECURITY file and identifies the project as Apache-2.0 licensed. | Ray supports built-in token authentication starting in version 2.52.0, while its security guidance calls for controlled networks and trusted code. |
| Support | The GitHub repository says DeepSpeed holds public office hours on the last Tuesday of each month. | The Ray site offers a community Slack, forums, and documentation, and says Anyscale offers hands-on training and expert support. |