--- title: "determined vs ray" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/determined-ai-determined-vs-ray-project-ray" tools: ["determined-ai-determined", "ray-project-ray"] --- # determined vs ray *GraphCanon updated Sep 30, 2026* ## Verdict Pick determined if determined is an open-source machine learning platform that simplifies distributed training and hyperparameter tuning for PyTorch and TensorFlow applications; pick ray if ray is a unified framework for scaling AI and Python applications, offering a core distributed runtime and AI libraries for accelerating ML workloads. [determined](https://determined.ai) reports 3.2k GitHub stars, 375 forks, and 108 open issues, last pushed Mar 20, 2025. [ray](https://ray.io) has 44k stars, 8.1k forks, and 3.6k open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [determined's repository](https://github.com/determined-ai/determined) and [ray's repository](https://github.com/ray-project/ray). | | [determined](/tools/determined-ai-determined.md) | [ray](/tools/ray-project-ray.md) | | --- | --- | --- | | Tagline | An open-source machine learning platform for distributed training and resource management. | Unified framework for scaling AI and Python applications | | Stars | 3,241 | 43,863 | | Forks | 375 | 8,052 | | Open issues | 108 | 3,590 | | Language | Go | Python | | Adopt for | Determined is an open-source machine learning platform that simplifies distributed training and hyperparameter tuning for PyTorch and TensorFlow applications. | Ray is a unified framework for scaling AI and Python applications, offering a core distributed runtime and AI libraries for accelerating ML workloads. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [determined](/tools/determined-ai-determined.md) | [ray](/tools/ray-project-ray.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 558d | 0d | | Open issues (now) | 108 | 3.6k | | Stars delta | +14 (30d) | +337 (30d) | | Open issues delta | 0 (30d) | +107 (30d) | | Full report | [trust report](/tools/determined-ai-determined/trust.md) | [trust report](/tools/ray-project-ray/trust.md) | ## Shared compatibility - **Python**: [determined](/tools/determined-ai-determined.md) - Python runtime; [ray](/tools/ray-project-ray.md) - Python runtime ## Decision facts: determined - **Pricing:** freemium - The open-source version under Apache License is free to use. Additional enterprise features may incur costs, though specific pricing details are not provided here. - **Requirements:** Installation involves using 'pip' for the CLI and subsequent cluster deployment steps via `det deploy`. - **Adopt for:** Determined is an open-source machine learning platform that simplifies distributed training and hyperparameter tuning for PyTorch and TensorFlow applications. - **License detail:** Apache-2.0 ## Decision facts: ray - **Adopt for:** Ray is a unified framework for scaling AI and Python applications, offering a core distributed runtime and AI libraries for accelerating ML workloads. ## Choose when ### Choose determined if… - determined is primarily Go; ray is Python. - Pricing: The open-source version under Apache License is free to use. Additional enterprise features may incur costs, though specific pricing details are not provided here.. - Requirements: Installation involves using 'pip' for the CLI and subsequent cluster deployment steps via `det deploy`.. - Tags unique to determined: distributed training, ml-infrastructure, pytorch, tensorflow. - You require a streamlined solution for distributed model training, particularly if you are working with PyTorch or TensorFlow frameworks. ### Choose ray if… - ray is primarily Python; determined is Go. - Tags unique to ray: deployment, distributed, large-language-models, machine-learning. - Also covers Developer Tools, Inference & Serving. - When you need to perform distributed training, as Ray's Train library simplifies the setup and management of distributed training jobs. ## When NOT to use determined - Seeking a platform that supports more machine learning frameworks beyond PyTorch and TensorFlow. - Your current stack does not include any of the supported infrastructures like Kubernetes or cloud services where Determined can be deployed. ## When NOT to use ray - If your project is limited to single-node development and does not require scaling across multiple machines or clusters. - When the specific requirements of your project are better met by specialized tools that offer more tailored functionality for a particular task, such as TensorFlow for deep learning or PyTorch for GPU - If you are working in a non-Python environment, as Ray is primarily designed for Python applications. ## Common questions ### What is the difference between determined and ray? determined: An open-source machine learning platform for distributed training and resource management.. ray: Unified framework for scaling AI and Python applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose determined over ray? Choose determined over ray when determined is primarily Go; ray is Python; Pricing: The open-source version under Apache License is free to use. Additional enterprise features may incur costs, though specific pricing details are not provided here.; Requirements: Installation involves using 'pip' for the CLI and subsequent cluster deployment steps via `det deploy`.; Tags unique to determined: distributed training, ml-infrastructure, pytorch, tensorflow; You require a streamlined solution for distributed model training, particularly if you are working with PyTorch or TensorFlow frameworks. ### When should I choose ray over determined? Choose ray over determined when ray is primarily Python; determined is Go; Tags unique to ray: deployment, distributed, large-language-models, machine-learning; Also covers Developer Tools, Inference & Serving; When you need to perform distributed training, as Ray's Train library simplifies the setup and management of distributed training jobs. ### When should I avoid determined? Seeking a platform that supports more machine learning frameworks beyond PyTorch and TensorFlow. Your current stack does not include any of the supported infrastructures like Kubernetes or cloud services where Determined can be deployed. ### When should I avoid ray? If your project is limited to single-node development and does not require scaling across multiple machines or clusters. When the specific requirements of your project are better met by specialized tools that offer more tailored functionality for a particular task, such as TensorFlow for deep learning or PyTorch for GPU If you are working in a non-Python environment, as Ray is primarily designed for Python applications. ### Is determined or ray more popular on GitHub? ray has more GitHub stars (43,863 vs 3,241). Stars measure visibility, not whether either tool fits your constraints. ### Are determined and ray open source? Yes - both are open-source projects on GitHub (determined: Apache-2.0, ray: Apache-2.0). ### Where can I find alternatives to determined or ray? GraphCanon lists graph-backed alternatives at [determined alternatives](/tools/determined-ai-determined/alternatives) and [ray alternatives](/tools/ray-project-ray/alternatives) ([determined markdown twin](/tools/determined-ai-determined/alternatives.md), [ray markdown twin](/tools/ray-project-ray/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/determined-ai-determined-vs-ray-project-ray.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, determined or ray? determined: Dormant. ray: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for determined and ray? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [determined trust report](/tools/determined-ai-determined/trust); [ray trust report](/tools/ray-project-ray/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=determined-ai-determined`](/api/graphcanon/graph?tool=determined-ai-determined) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_