--- title: "FATE vs FastChat" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/federatedai-fate-vs-lm-sys-fastchat" tools: ["federatedai-fate", "lm-sys-fastchat"] --- # FATE vs FastChat *GraphCanon updated Jul 11, 2026* ## Verdict Pick FATE when tags unique to FATE: fate, algorithm, machine-learning, python; pick FastChat when tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving. [FATE](https://github.com/FederatedAI/FATE) reports 6.1k GitHub stars, 1.6k forks, and 21 open issues, last pushed Nov 19, 2024. [FastChat](https://github.com/lm-sys/FastChat) has 39k stars, 4.8k forks, and 1.0k open issues, last pushed May 1, 2026. Figures are from public GitHub metadata via [FATE's repository](https://github.com/FederatedAI/FATE) and [FastChat's repository](https://github.com/lm-sys/FastChat). | | [FATE](/tools/federatedai-fate.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Tagline | An Industrial Grade Federated Learning Framework | An open platform for training, serving, and evaluating large language models | | Stars | 6,084 | 39,490 | | Forks | 1,569 | 4,788 | | Open issues | 21 | 1,027 | | Language | Python | Python | | 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 | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Model Training, Inference & Serving, Computer Vision | LLM Frameworks, Model Training, Inference & Serving, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FATE](/tools/federatedai-fate.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 599d | 71d | | Open issues (now) | 21 | 1.0k | | Full report | [trust report](/tools/federatedai-fate/trust.md) | [trust report](/tools/lm-sys-fastchat/trust.md) | ## 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 FATE if… - Tags unique to FATE: fate, algorithm, machine-learning, python. - Also covers Computer Vision. - Leaner open-issue backlog (21). ### Choose FastChat if… - Tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving. - Also covers LLM Frameworks, Evaluation & Observability. - - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ## When NOT to use FATE - Last GitHub push was 600 days ago (dormant maintenance, Nov 19, 2024). Validate activity before betting a new project on FATE. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## 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 FATE and FastChat? FATE: An Industrial Grade Federated Learning Framework. 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 FATE over FastChat? Choose FATE over FastChat when Tags unique to FATE: fate, algorithm, machine-learning, python; Also covers Computer Vision; Leaner open-issue backlog (21). ### When should I choose FastChat over FATE? Choose FastChat over FATE when Tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving; Also covers LLM Frameworks, Evaluation & Observability; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ### When should I avoid FATE? Last GitHub push was 600 days ago (dormant maintenance, Nov 19, 2024). Validate activity before betting a new project on FATE. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### 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 FATE or FastChat more popular on GitHub? FastChat has more GitHub stars (39,490 vs 6,084). Stars measure visibility, not whether either tool fits your constraints. ### Are FATE and FastChat open source? Yes - both are open-source projects on GitHub (FATE: Apache-2.0, FastChat: Apache-2.0). ### Where can I find alternatives to FATE or FastChat? GraphCanon lists graph-backed alternatives at [FATE alternatives](/tools/federatedai-fate/alternatives) and [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) ([FATE markdown twin](/tools/federatedai-fate/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/federatedai-fate-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, FATE or FastChat? FATE: Dormant. FastChat: Steady. 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 FATE and FastChat? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FATE trust report](/tools/federatedai-fate/trust); [FastChat trust report](/tools/lm-sys-fastchat/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=federatedai-fate`](/api/graphcanon/graph?tool=federatedai-fate) - 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/_