--- title: "ray vs sglang" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ray-project-ray-vs-sgl-project-sglang" tools: ["ray-project-ray", "sgl-project-sglang"] --- # ray vs sglang Neutral, constraint-first comparison with live GitHub stats. | | [ray](/tools/ray-project-ray.md) | [sglang](/tools/sgl-project-sglang.md) | | --- | --- | --- | | Tagline | Unified framework for scaling AI and Python applications | Serving framework for large language models and multimodal models | | Stars | 43,156 | 30,062 | | Forks | 7,775 | 7,016 | | Open issues | 3,455 | 4,053 | | Language | Python | Python | | Adopt for | Ray is a comprehensive distributed framework targeting both simplicity and scalability in various ML tasks like training, hyperparameter tuning, reinforcement learning, etc. It uses abstractions like Tasks, Actors, and S | SGLang is a high-performance serving framework designed specifically for deploying, optimizing inference tasks on large language models (LLMs) and multimodal models. It supports multiple backend architectures including n | | Persona | - | - | | Runtime | - | - | | License | Ray is open-source software released under the Apache License, Version 2.0. | SGLang is licensed under the Apache-2.0 license, offering permissive open-source terms that are flexible for commercial use with attribution requirements. | | Categories | Model Training, Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ray](/tools/ray-project-ray.md) | [sglang](/tools/sgl-project-sglang.md) | | --- | --- | --- | | Open issues (now) | 3.5k | 4.1k | | Full report | [trust report](/tools/ray-project-ray/trust.md) | [trust report](/tools/sgl-project-sglang/trust.md) | **Typed relationship:** ray _(integrates with)_ sglang SGLang is a serving framework that can integrate with Ray for efficient scaling and deployment of large language models and multimodal applications. ## Decision facts: ray - **Pricing:** freemium - Ray core can be used freely for both personal and commercial use due to its open-source nature. Additional advanced features or support may require a premium option via Anyscale. - **Adopt for:** Ray is a comprehensive distributed framework targeting both simplicity and scalability in various ML tasks like training, hyperparameter tuning, reinforcement learning, etc. It uses abstractions like Tasks, Actors, and S - **License detail:** Ray is open-source software released under the Apache License, Version 2.0. ## Decision facts: sglang - **Hosting:** self hosted - Deploy SGLang in a self-hosted environment tailored to your specific hardware, such as NVIDIA GPUs or TPUs. - **Adopt for:** SGLang is a high-performance serving framework designed specifically for deploying, optimizing inference tasks on large language models (LLMs) and multimodal models. It supports multiple backend architectures including n - **License detail:** SGLang is licensed under the Apache-2.0 license, offering permissive open-source terms that are flexible for commercial use with attribution requirements. ## Choose when ### Choose ray if… - Pricing: Ray core can be used freely for both personal and commercial use due to its open-source nature. Additional advanced features or support may require a premium option via Anyscale.. - SGLang is a serving framework that can integrate with Ray for efficient scaling and deployment of large language models and multimodal applications. - Tags unique to ray: reinforcement-learning, data-science, deep-learning, python. - Also covers Model Training. - - When you need to handle large-scale computations that can benefit from parallel processing across multiple machines or cores. ### Choose sglang if… - Deploy SGLang in a self-hosted environment tailored to your specific hardware, such as NVIDIA GPUs or TPUs. - SGLang is a serving framework that can integrate with Ray for efficient scaling and deployment of large language models and multimodal applications. - Tags unique to sglang: llama, deepseek, cuda, diffusion. - When you require support for the latest open-source model releases such as Nemotron 3 Ultra, Nemotron 3 Super, or Higgs Audio v3 TTS. ## When NOT to use ray - - When working on small-scale projects where the overhead of setting up a distributed system outweighs the benefits, simpler local solutions might be preferable. - - If your project specifically requires deep integration with specific frameworks (e.g., extremely tight control over TensorFlow or PyTorch operations) that isn't as well-supported by Ray's abstrac - - When the development team lacks expertise in managing distributed systems and would struggle to effectively leverage Ray’s complex features without significant learning curve investment. ## When NOT to use sglang - Avoid using SGLang if your project relies exclusively on CPU-based inference, as it specifically optimizes for GPU architectures like CUDA. - SGLang may not be suitable for scenarios where the primary model focus is reinforcement learning (RL), given its specific strengths in LLM and multimodal model serving. - If you need a broader range of features beyond solely inference speed and efficiency for large language models, SGLang's specialized capabilities might not address all your needs. ## Common questions ### What is the difference between ray and sglang? ray: Unified framework for scaling AI and Python applications. sglang: Serving framework for large language models and multimodal models. See the comparison table for live GitHub stats and shared categories. ### When should I choose ray over sglang? Choose ray over sglang when Pricing: Ray core can be used freely for both personal and commercial use due to its open-source nature. Additional advanced features or support may require a premium option via Anyscale.; SGLang is a serving framework that can integrate with Ray for efficient scaling and deployment of large language models and multimodal applications; Tags unique to ray: reinforcement-learning, data-science, deep-learning, python; Also covers Model Training; - When you need to handle large-scale computations that can benefit from parallel processing across multiple machines or cores. ### When should I choose sglang over ray? Choose sglang over ray when Deploy SGLang in a self-hosted environment tailored to your specific hardware, such as NVIDIA GPUs or TPUs; SGLang is a serving framework that can integrate with Ray for efficient scaling and deployment of large language models and multimodal applications; Tags unique to sglang: llama, deepseek, cuda, diffusion; When you require support for the latest open-source model releases such as Nemotron 3 Ultra, Nemotron 3 Super, or Higgs Audio v3 TTS. ### When should I avoid ray? - When working on small-scale projects where the overhead of setting up a distributed system outweighs the benefits, simpler local solutions might be preferable. - If your project specifically requires deep integration with specific frameworks (e.g., extremely tight control over TensorFlow or PyTorch operations) that isn't as well-supported by Ray's abstrac - When the development team lacks expertise in managing distributed systems and would struggle to effectively leverage Ray’s complex features without significant learning curve investment. ### When should I avoid sglang? Avoid using SGLang if your project relies exclusively on CPU-based inference, as it specifically optimizes for GPU architectures like CUDA. SGLang may not be suitable for scenarios where the primary model focus is reinforcement learning (RL), given its specific strengths in LLM and multimodal model serving. If you need a broader range of features beyond solely inference speed and efficiency for large language models, SGLang's specialized capabilities might not address all your needs. ### Is ray or sglang more popular on GitHub? ray has more GitHub stars (43,156 vs 30,062). Stars measure visibility, not whether either tool fits your constraints. ### Are ray and sglang open source? Yes - both are open-source projects on GitHub (ray: Apache-2.0, sglang: Apache-2.0). ### Where can I find alternatives to ray or sglang? GraphCanon lists graph-backed alternatives at /tools/ray-project-ray/alternatives and /tools/sgl-project-sglang/alternatives (/tools/ray-project-ray/alternatives.md, /tools/sgl-project-sglang/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 /compare/ray-project-ray-vs-sgl-project-sglang.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ray or sglang? ray: Very active. sglang: 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 ray and sglang? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ray: /tools/ray-project-ray/trust; sglang: /tools/sgl-project-sglang/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ray-project-ray`](/api/graphcanon/graph?tool=ray-project-ray) - 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/_