--- title: "amazon-sagemaker-examples vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aws-amazon-sagemaker-examples-vs-tensorchord-awesome-llmops" tools: ["aws-amazon-sagemaker-examples", "tensorchord-awesome-llmops"] --- # amazon-sagemaker-examples vs Awesome-LLMOps *GraphCanon updated Sep 20, 2026* ## Verdict Pick amazon-sagemaker-examples if jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker; pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. [amazon-sagemaker-examples](https://sagemaker-examples.readthedocs.io) reports 11k GitHub stars, 7.0k forks, and 854 open issues, last pushed Sep 9, 2026. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 1.1k forks, and 317 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [amazon-sagemaker-examples's repository](https://github.com/aws/amazon-sagemaker-examples) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | Jupyter notebooks for building, training, and deploying ML models using Amazon SageMaker | An awesome & curated list of best LLMOps tools for developers | | Stars | 10,990 | 5,941 | | Forks | 6,955 | 1,058 | | Open issues | 854 | 317 | | Language | Jupyter Notebook | Shell | | Adopt for | Jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker | Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0, allowing free use for any purpose with conditions on attribution and license preservation | CC0-1.0 | | Categories | Inference & Serving, Model Training | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 10d | 121d | | Open issues (now) | 854 | 317 | | Stars delta | +6 (30d) | +26 (30d) | | Open issues delta | +5 (30d) | +70 (30d) | | Full report | [trust report](/tools/aws-amazon-sagemaker-examples/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: amazon-sagemaker-examples - **Adopt for:** Jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker - **License detail:** Apache-2.0, allowing free use for any purpose with conditions on attribution and license preservation ## Decision facts: Awesome-LLMOps - **Adopt for:** Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. ## Choose when ### Choose amazon-sagemaker-examples if… - amazon-sagemaker-examples is primarily Jupyter Notebook; Awesome-LLMOps is Shell. - License: amazon-sagemaker-examples is Apache-2.0, Awesome-LLMOps is CC0-1.0. - Tags unique to amazon-sagemaker-examples: aws, data-science, deep-learning, inference. - When you need examples specific to building models with Amazon SageMaker ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; amazon-sagemaker-examples is Jupyter Notebook. - License: Awesome-LLMOps is CC0-1.0, amazon-sagemaker-examples is Apache-2.0. - Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops. - Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ## When NOT to use amazon-sagemaker-examples - For non-AWS environments where cost and integration complexities could outweigh benefits - If seeking open-source tools without ties to a single cloud provider ## When NOT to use Awesome-LLMOps - - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ## Common questions ### What is the difference between amazon-sagemaker-examples and Awesome-LLMOps? amazon-sagemaker-examples: Jupyter notebooks for building, training, and deploying ML models using Amazon SageMaker. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories. ### When should I choose amazon-sagemaker-examples over Awesome-LLMOps? Choose amazon-sagemaker-examples over Awesome-LLMOps when amazon-sagemaker-examples is primarily Jupyter Notebook; Awesome-LLMOps is Shell; License: amazon-sagemaker-examples is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to amazon-sagemaker-examples: aws, data-science, deep-learning, inference; When you need examples specific to building models with Amazon SageMaker. ### When should I choose Awesome-LLMOps over amazon-sagemaker-examples? Choose Awesome-LLMOps over amazon-sagemaker-examples when Awesome-LLMOps is primarily Shell; amazon-sagemaker-examples is Jupyter Notebook; License: Awesome-LLMOps is CC0-1.0, amazon-sagemaker-examples is Apache-2.0; Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### When should I avoid amazon-sagemaker-examples? For non-AWS environments where cost and integration complexities could outweigh benefits If seeking open-source tools without ties to a single cloud provider ### When should I avoid Awesome-LLMOps? - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ### Is amazon-sagemaker-examples or Awesome-LLMOps more popular on GitHub? amazon-sagemaker-examples has more GitHub stars (10,990 vs 5,941). Stars measure visibility, not whether either tool fits your constraints. ### Are amazon-sagemaker-examples and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (amazon-sagemaker-examples: Apache-2.0, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to amazon-sagemaker-examples or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [amazon-sagemaker-examples alternatives](/tools/aws-amazon-sagemaker-examples/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([amazon-sagemaker-examples markdown twin](/tools/aws-amazon-sagemaker-examples/alternatives.md), [Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/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-tensorchord-awesome-llmops.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 Awesome-LLMOps? amazon-sagemaker-examples: Active. Awesome-LLMOps: 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 amazon-sagemaker-examples and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [amazon-sagemaker-examples trust report](/tools/aws-amazon-sagemaker-examples/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/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/_