--- title: "amazon-sagemaker-examples vs FastChat" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aws-amazon-sagemaker-examples-vs-lm-sys-fastchat" tools: ["aws-amazon-sagemaker-examples", "lm-sys-fastchat"] --- # amazon-sagemaker-examples vs FastChat *GraphCanon updated Jul 15, 2026* ## Verdict Pick amazon-sagemaker-examples when amazon-sagemaker-examples is primarily Jupyter Notebook; FastChat is Python; pick FastChat when fastChat is primarily Python; amazon-sagemaker-examples is Jupyter Notebook. [amazon-sagemaker-examples](https://sagemaker-examples.readthedocs.io) reports 11k GitHub stars, 7.0k forks, and 849 open issues, last pushed Jul 7, 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 [amazon-sagemaker-examples's repository](https://github.com/aws/amazon-sagemaker-examples) and [FastChat's repository](https://github.com/lm-sys/FastChat). | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Tagline | Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker. | An open platform for training, serving, and evaluating large language models | | Stars | 10,971 | 39,494 | | Forks | 6,969 | 4,786 | | Open issues | 849 | 1,027 | | Language | Jupyter Notebook | 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 | Inference & Serving, Model Training | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Maintenance | Active (82%) | Steady (60%) | | Days since push | 7d | 74d | | Open issues (now) | 849 | 1.0k | | Full report | [trust report](/tools/aws-amazon-sagemaker-examples/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 amazon-sagemaker-examples if… - amazon-sagemaker-examples is primarily Jupyter Notebook; FastChat is Python. - Tags unique to amazon-sagemaker-examples: aws, data-science, deep-learning, examples. - More recently updated (last pushed Jul 7, 2026). ### Choose FastChat if… - FastChat is primarily Python; amazon-sagemaker-examples is Jupyter Notebook. - 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 amazon-sagemaker-examples - 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 amazon-sagemaker-examples and FastChat? amazon-sagemaker-examples: Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.. 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 amazon-sagemaker-examples over FastChat? Choose amazon-sagemaker-examples over FastChat when amazon-sagemaker-examples is primarily Jupyter Notebook; FastChat is Python; Tags unique to amazon-sagemaker-examples: aws, data-science, deep-learning, examples; More recently updated (last pushed Jul 7, 2026). ### When should I choose FastChat over amazon-sagemaker-examples? Choose FastChat over amazon-sagemaker-examples when FastChat is primarily Python; amazon-sagemaker-examples is Jupyter Notebook; 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 amazon-sagemaker-examples? 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 amazon-sagemaker-examples or FastChat more popular on GitHub? FastChat has more GitHub stars (39,494 vs 10,971). Stars measure visibility, not whether either tool fits your constraints. ### Are amazon-sagemaker-examples and FastChat open source? Yes - both are open-source projects on GitHub (amazon-sagemaker-examples: Apache-2.0, FastChat: Apache-2.0). ### Where can I find alternatives to amazon-sagemaker-examples or FastChat? GraphCanon lists graph-backed alternatives at [amazon-sagemaker-examples alternatives](/tools/aws-amazon-sagemaker-examples/alternatives) and [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) ([amazon-sagemaker-examples markdown twin](/tools/aws-amazon-sagemaker-examples/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/aws-amazon-sagemaker-examples-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, amazon-sagemaker-examples or FastChat? amazon-sagemaker-examples: Active. 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 amazon-sagemaker-examples and FastChat? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [amazon-sagemaker-examples trust report](/tools/aws-amazon-sagemaker-examples/trust); [FastChat trust report](/tools/lm-sys-fastchat/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aws-amazon-sagemaker-examples`](/api/graphcanon/graph?tool=aws-amazon-sagemaker-examples) - 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/_