--- title: "awesome-mlops vs FastChat" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kelvins-awesome-mlops-vs-lm-sys-fastchat" tools: ["kelvins-awesome-mlops", "lm-sys-fastchat"] --- # awesome-mlops vs FastChat *GraphCanon updated Jul 11, 2026* ## Verdict Pick awesome-mlops when tags unique to awesome-mlops: ai, awesome, data-science, machine-learning; pick FastChat when tags unique to FastChat: chatbots, distributed serving, evaluation system, large-language-models. [awesome-mlops](https://github.com/kelvins/awesome-mlops) reports 5.2k GitHub stars, 757 forks, and 67 open issues, last pushed Apr 29, 2026. [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 [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops) and [FastChat's repository](https://github.com/lm-sys/FastChat). | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Tagline | :sunglasses: A curated list of awesome MLOps tools | An open platform for training, serving, and evaluating large language models | | Stars | 5,208 | 39,490 | | Forks | 757 | 4,788 | | Open issues | 67 | 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 | | Categories | Computer Vision, Inference & Serving, Model Training | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Days since push | 73d | 71d | | Open issues (now) | 67 | 1.0k | | Owner type | User | Organization | | Full report | [trust report](/tools/kelvins-awesome-mlops/trust.md) | [trust report](/tools/lm-sys-fastchat/trust.md) | ## Shared compatibility - **Python**: [awesome-mlops](/tools/kelvins-awesome-mlops.md) - Python runtime; [FastChat](/tools/lm-sys-fastchat.md) - Python runtime ## 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 awesome-mlops if… - Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning. - Also covers Computer Vision. - Leaner open-issue backlog (67). ### 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 awesome-mlops - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## 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 awesome-mlops and FastChat? awesome-mlops: :sunglasses: A curated list of awesome MLOps tools. 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 awesome-mlops over FastChat? Choose awesome-mlops over FastChat when Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning; Also covers Computer Vision; Leaner open-issue backlog (67). ### When should I choose FastChat over awesome-mlops? Choose FastChat over awesome-mlops 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 awesome-mlops? Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### 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 awesome-mlops or FastChat more popular on GitHub? FastChat has more GitHub stars (39,490 vs 5,208). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-mlops and FastChat open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-mlops or FastChat? GraphCanon lists graph-backed alternatives at [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) and [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) ([awesome-mlops markdown twin](/tools/kelvins-awesome-mlops/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/kelvins-awesome-mlops-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, awesome-mlops or FastChat? awesome-mlops: Steady. 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 awesome-mlops and FastChat? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-mlops trust report](/tools/kelvins-awesome-mlops/trust); [FastChat trust report](/tools/lm-sys-fastchat/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kelvins-awesome-mlops`](/api/graphcanon/graph?tool=kelvins-awesome-mlops) - 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/_