--- title: "jan vs vllm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/janhq-jan-vs-vllm-project-vllm" tools: ["janhq-jan", "vllm-project-vllm"] --- # jan vs vllm Neutral, constraint-first comparison with live GitHub stats. | | [jan](/tools/janhq-jan.md) | [vllm](/tools/vllm-project-vllm.md) | | --- | --- | --- | | Tagline | Jan is an open-source self-hosted ChatGPT alternative. | Easy, fast, and cheap LLM serving for everyone | | Stars | 43,447 | 85,665 | | Forks | 2,886 | 19,107 | | Open issues | 372 | 5,589 | | Language | TypeScript | Python | | Adopt for | Jan is an open-source and self-hosted chatbot solution that offers extensive control over AI models, privacy-focused operations, and compatibility with a variety of LLMs and APIs. | vLLM is a high-throughput, memory-efficient inference and serving engine for Large Language Models (LLMs). It supports a wide range of models via Hugging Face integration and implements advanced techniques like Paged-AR/ | | Persona | - | - | | Runtime | - | - | | License | Other | Apache-2.0 | | Categories | LLM Frameworks, Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [jan](/tools/janhq-jan.md) | [vllm](/tools/vllm-project-vllm.md) | | --- | --- | --- | | Open issues (now) | 372 | 5.6k | | Full report | [trust report](/tools/janhq-jan/trust.md) | [trust report](/tools/vllm-project-vllm/trust.md) | **Typed relationship:** jan _(alternative)_ vllm Both projects focus on serving LLMs locally but with optimizations for speed and cost-efficiency. ## Decision facts: jan - **Pricing:** freemium - The core product is available under an open-source license, which means it can be used at no cost. However, some features or services might require additional costs depending on third-party integrat - **Requirements:** Min 8 GB RAM; Local installation is required. - **Adopt for:** Jan is an open-source and self-hosted chatbot solution that offers extensive control over AI models, privacy-focused operations, and compatibility with a variety of LLMs and APIs. ## Decision facts: vllm - **Adopt for:** vLLM is a high-throughput, memory-efficient inference and serving engine for Large Language Models (LLMs). It supports a wide range of models via Hugging Face integration and implements advanced techniques like Paged-AR/ ## Choose when ### Choose jan if… - jan is primarily TypeScript; vllm is Python. - License: jan is Other, vllm is Apache-2.0. - Pricing: The core product is available under an open-source license, which means it can be used at no cost. However, some features or services might require additional costs depending on third-party integrat. - Requirements: Min 8 GB RAM; Local installation is required.. - Both projects focus on serving LLMs locally but with optimizations for speed and cost-efficiency. - Tags unique to jan: self-hosted, llm, chatgpt, localai. - Also covers LLM Frameworks. - Use Jan when you need full control and complete privacy for your AI interactions since it runs locally without internet dependence. ### Choose vllm if… - vllm is primarily Python; jan is TypeScript. - License: vllm is Apache-2.0, jan is Other. - Both projects focus on serving LLMs locally but with optimizations for speed and cost-efficiency. - Tags unique to vllm: amd, llama, deepseek, cuda. - - When you need state-of-the-art throughput with efficient attention management using **PagedAttention**. ## When NOT to use jan - Avoid using Jan if you require cloud-based services for AI model processing, as its full operation in a local environment might not leverage the benefits of cloud-scale computing resources. - Jan may not be suitable if seamless integration with platforms that rely on consistent online availability is essential to your workflow, given its offline nature. ## When NOT to use vllm - - For users who do not require or cannot support the hardware and software dependencies such as CUDA/HIP for optimal performance. - - If your project focuses on model training rather than inference since vLLM's primary strength lies in serving and high-throughput applications. - - When you need a tool that is highly portable to older or less common architectures, given its optimization for modern GPUs and specialized hardware might not be beneficial in those scenarios. ## Common questions ### What is the difference between jan and vllm? jan: Jan is an open-source self-hosted ChatGPT alternative.. vllm: Easy, fast, and cheap LLM serving for everyone. See the comparison table for live GitHub stats and shared categories. ### When should I choose jan over vllm? Choose jan over vllm when jan is primarily TypeScript; vllm is Python; License: jan is Other, vllm is Apache-2.0; Pricing: The core product is available under an open-source license, which means it can be used at no cost. However, some features or services might require additional costs depending on third-party integrat; Requirements: Min 8 GB RAM; Local installation is required.; Both projects focus on serving LLMs locally but with optimizations for speed and cost-efficiency; Tags unique to jan: self-hosted, llm, chatgpt, localai; Also covers LLM Frameworks; Use Jan when you need full control and complete privacy for your AI interactions since it runs locally without internet dependence. ### When should I choose vllm over jan? Choose vllm over jan when vllm is primarily Python; jan is TypeScript; License: vllm is Apache-2.0, jan is Other; Both projects focus on serving LLMs locally but with optimizations for speed and cost-efficiency; Tags unique to vllm: amd, llama, deepseek, cuda; - When you need state-of-the-art throughput with efficient attention management using **PagedAttention**. ### When should I avoid jan? Avoid using Jan if you require cloud-based services for AI model processing, as its full operation in a local environment might not leverage the benefits of cloud-scale computing resources. Jan may not be suitable if seamless integration with platforms that rely on consistent online availability is essential to your workflow, given its offline nature. ### When should I avoid vllm? - For users who do not require or cannot support the hardware and software dependencies such as CUDA/HIP for optimal performance. - If your project focuses on model training rather than inference since vLLM's primary strength lies in serving and high-throughput applications. - When you need a tool that is highly portable to older or less common architectures, given its optimization for modern GPUs and specialized hardware might not be beneficial in those scenarios. ### Is jan or vllm more popular on GitHub? vllm has more GitHub stars (85,665 vs 43,447). Stars measure visibility, not whether either tool fits your constraints. ### Are jan and vllm open source? Yes - both are open-source projects on GitHub (jan: Other, vllm: Apache-2.0). ### Where can I find alternatives to jan or vllm? GraphCanon lists graph-backed alternatives at /tools/janhq-jan/alternatives and /tools/vllm-project-vllm/alternatives (/tools/janhq-jan/alternatives.md, /tools/vllm-project-vllm/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/janhq-jan-vs-vllm-project-vllm.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, jan or vllm? jan: Very active. vllm: 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 jan and vllm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: jan: /tools/janhq-jan/trust; vllm: /tools/vllm-project-vllm/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=janhq-jan`](/api/graphcanon/graph?tool=janhq-jan) - 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/_