--- title: "amazon-bedrock-samples vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aws-samples-amazon-bedrock-samples-vs-tensorchord-awesome-llmops" tools: ["aws-samples-amazon-bedrock-samples", "tensorchord-awesome-llmops"] --- # amazon-bedrock-samples vs Awesome-LLMOps *GraphCanon updated Jul 11, 2026* ## Verdict Pick amazon-bedrock-samples when amazon-bedrock-samples is primarily Jupyter Notebook; Awesome-LLMOps is Shell; pick Awesome-LLMOps when awesome-LLMOps is primarily Shell; amazon-bedrock-samples is Jupyter Notebook. [amazon-bedrock-samples](https://aws.amazon.com/bedrock/) reports 1.5k GitHub stars, 701 forks, and 130 open issues, last pushed Jun 30, 2026. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 901 forks, and 157 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [amazon-bedrock-samples's repository](https://github.com/aws-samples/amazon-bedrock-samples) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [amazon-bedrock-samples](/tools/aws-samples-amazon-bedrock-samples.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | This repository contains examples for customers to get started using the Amazon Bedrock Service. This contains examples for all available foundational models | An awesome & curated list of best LLMOps tools for developers | | Stars | 1,470 | 5,877 | | Forks | 701 | 901 | | Open issues | 130 | 157 | | Language | Jupyter Notebook | Shell | | 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. | | Persona | - | - | | Runtime | - | - | | License | MIT-0 | CC0-1.0 | | Categories | LLM Frameworks, Vector Databases | Vector Databases, Model Training, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [amazon-bedrock-samples](/tools/aws-samples-amazon-bedrock-samples.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Active (82%) | Steady (60%) | | Days since push | 11d | 51d | | Open issues (now) | 130 | 157 | | Full report | [trust report](/tools/aws-samples-amazon-bedrock-samples/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## 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-bedrock-samples if… - amazon-bedrock-samples is primarily Jupyter Notebook; Awesome-LLMOps is Shell. - License: amazon-bedrock-samples is MIT-0, Awesome-LLMOps is CC0-1.0. - Tags unique to amazon-bedrock-samples: embeddings, amazon-bedrock, amazon-titan, rag. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; amazon-bedrock-samples is Jupyter Notebook. - License: Awesome-LLMOps is CC0-1.0, amazon-bedrock-samples is MIT-0. - Tags unique to Awesome-LLMOps: llmops, shell, awesome-list, mlops. - Also covers Model Training. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ## When NOT to use amazon-bedrock-samples - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## 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-bedrock-samples and Awesome-LLMOps? amazon-bedrock-samples: This repository contains examples for customers to get started using the Amazon Bedrock Service. This contains examples for all available foundational models. 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-bedrock-samples over Awesome-LLMOps? Choose amazon-bedrock-samples over Awesome-LLMOps when amazon-bedrock-samples is primarily Jupyter Notebook; Awesome-LLMOps is Shell; License: amazon-bedrock-samples is MIT-0, Awesome-LLMOps is CC0-1.0; Tags unique to amazon-bedrock-samples: embeddings, amazon-bedrock, amazon-titan, rag. ### When should I choose Awesome-LLMOps over amazon-bedrock-samples? Choose Awesome-LLMOps over amazon-bedrock-samples when Awesome-LLMOps is primarily Shell; amazon-bedrock-samples is Jupyter Notebook; License: Awesome-LLMOps is CC0-1.0, amazon-bedrock-samples is MIT-0; Tags unique to Awesome-LLMOps: llmops, shell, awesome-list, mlops; Also covers Model Training; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### When should I avoid amazon-bedrock-samples? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### 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-bedrock-samples or Awesome-LLMOps more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,877 vs 1,470). Stars measure visibility, not whether either tool fits your constraints. ### Are amazon-bedrock-samples and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (amazon-bedrock-samples: MIT-0, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to amazon-bedrock-samples or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [amazon-bedrock-samples alternatives](/tools/aws-samples-amazon-bedrock-samples/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([amazon-bedrock-samples markdown twin](/tools/aws-samples-amazon-bedrock-samples/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-samples-amazon-bedrock-samples-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-bedrock-samples or Awesome-LLMOps? amazon-bedrock-samples: Active. Awesome-LLMOps: 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-bedrock-samples and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [amazon-bedrock-samples trust report](/tools/aws-samples-amazon-bedrock-samples/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aws-samples-amazon-bedrock-samples`](/api/graphcanon/graph?tool=aws-samples-amazon-bedrock-samples) - 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/_