--- title: "serve vs FastChat" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jina-ai-serve-vs-lm-sys-fastchat" tools: ["jina-ai-serve", "lm-sys-fastchat"] --- # serve vs FastChat *GraphCanon updated Aug 7, 2026* ## Verdict Pick serve if serve enables developers to create and deploy multimodal AI services in cloud-native environments with Python; pick FastChat if fastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB. [serve](https://jina.ai/serve) reports 22k GitHub stars, 2.2k forks, and 27 open issues, last pushed Mar 24, 2025. [FastChat](https://github.com/lm-sys/FastChat) has 40k stars, 4.8k forks, and 1.0k open issues, last pushed May 1, 2026. Figures are from public GitHub metadata via [serve's repository](https://github.com/jina-ai/serve) and [FastChat's repository](https://github.com/lm-sys/FastChat). | | [serve](/tools/jina-ai-serve.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Tagline | Build multimodal AI applications with cloud-native stack | An open platform for training, serving, and evaluating large language models | | Stars | 21,863 | 39,517 | | Forks | 2,243 | 4,788 | | Open issues | 27 | 1,038 | | Language | Python | Python | | Adopt for | Serve enables developers to create and deploy multimodal AI services in cloud-native environments with Python. | FastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving, Model Training | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [serve](/tools/jina-ai-serve.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 495d | 98d | | Open issues (now) | 27 | 1.0k | | Full report | [trust report](/tools/jina-ai-serve/trust.md) | [trust report](/tools/lm-sys-fastchat/trust.md) | ## Shared compatibility - **Python**: [serve](/tools/jina-ai-serve.md) - Python runtime; [FastChat](/tools/lm-sys-fastchat.md) - Python runtime ## Decision facts: serve - **Adopt for:** Serve enables developers to create and deploy multimodal AI services in cloud-native environments with Python. ## Decision facts: FastChat - **Adopt for:** FastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB ## Choose when ### Choose serve if… - Tags unique to serve: cloud-native, cncf, deep-learning, docker. - - If your project requires building cloud-native applications that integrate multiple types of data (visual, text, audio) with high scalability - Leaner open-issue backlog (27). ### Choose FastChat if… - Tags unique to FastChat: chatbots, distributed-serving, evaluation system, large language models. - Also covers Evaluation & Observability, LLM Frameworks. - - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ## When NOT to use serve - - If your project is limited to single-modal AI tasks or does not demand cloud-native deployment capabilities - - If the team lacks familiarity with Kubernetes or gRPC, since these technologies are integral to Serve's operational model for deploying and managing services ## When NOT to use FastChat - - You require a proprietary or closed-source framework; FastChat is open-source under Apache-2.0 license and its use might be unsuitable for environments requiring proprietary solutions. - - Your chatbot evaluation needs do not align with the types of data used in FastChat's datasets (e.g., human votes, MT-Bench evaluations). - - You prefer a more user-friendly setup without the need to clone a repository and manually install dependencies; FastChat requires installation from source with additional steps for Rust and CMake on - + Mac. ## Common questions ### What is the difference between serve and FastChat? serve: Build multimodal AI applications with cloud-native stack. FastChat: An open platform for training, serving, and evaluating large language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose serve over FastChat? Choose serve over FastChat when Tags unique to serve: cloud-native, cncf, deep-learning, docker; - If your project requires building cloud-native applications that integrate multiple types of data (visual, text, audio) with high scalability; Leaner open-issue backlog (27). ### When should I choose FastChat over serve? Choose FastChat over serve when Tags unique to FastChat: chatbots, distributed-serving, evaluation system, large language models; Also covers Evaluation & Observability, LLM Frameworks; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ### When should I avoid serve? - If your project is limited to single-modal AI tasks or does not demand cloud-native deployment capabilities - If the team lacks familiarity with Kubernetes or gRPC, since these technologies are integral to Serve's operational model for deploying and managing services ### When should I avoid FastChat? - You require a proprietary or closed-source framework; FastChat is open-source under Apache-2.0 license and its use might be unsuitable for environments requiring proprietary solutions. - Your chatbot evaluation needs do not align with the types of data used in FastChat's datasets (e.g., human votes, MT-Bench evaluations). - You prefer a more user-friendly setup without the need to clone a repository and manually install dependencies; FastChat requires installation from source with additional steps for Rust and CMake on + Mac. ### Is serve or FastChat more popular on GitHub? FastChat has more GitHub stars (39,517 vs 21,863). Stars measure visibility, not whether either tool fits your constraints. ### Are serve and FastChat open source? Yes - both are open-source projects on GitHub (serve: Apache-2.0, FastChat: Apache-2.0). ### Where can I find alternatives to serve or FastChat? GraphCanon lists graph-backed alternatives at [serve alternatives](/tools/jina-ai-serve/alternatives) and [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) ([serve markdown twin](/tools/jina-ai-serve/alternatives.md), [FastChat markdown twin](/tools/lm-sys-fastchat/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/jina-ai-serve-vs-lm-sys-fastchat.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, serve or FastChat? serve: Dormant. FastChat: Slowing. 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 serve and FastChat? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [serve trust report](/tools/jina-ai-serve/trust); [FastChat trust report](/tools/lm-sys-fastchat/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jina-ai-serve`](/api/graphcanon/graph?tool=jina-ai-serve) - 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/_