--- title: "amazon-sagemaker-examples vs llm-app" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aws-amazon-sagemaker-examples-vs-pathwaycom-llm-app" tools: ["aws-amazon-sagemaker-examples", "pathwaycom-llm-app"] --- # amazon-sagemaker-examples vs llm-app *GraphCanon updated Sep 20, 2026* ## Verdict Pick amazon-sagemaker-examples if jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker; pick llm-app if llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. [amazon-sagemaker-examples](https://sagemaker-examples.readthedocs.io) reports 11k GitHub stars, 7.0k forks, and 854 open issues, last pushed Sep 9, 2026. [llm-app](https://pathway.com/developers/templates/) has 59k stars, 1.5k forks, and 8 open issues, last pushed Jul 5, 2026. Figures are from public GitHub metadata via [amazon-sagemaker-examples's repository](https://github.com/aws/amazon-sagemaker-examples) and [llm-app's repository](https://github.com/pathwaycom/llm-app). | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Tagline | Jupyter notebooks for building, training, and deploying ML models using Amazon SageMaker | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data | | Stars | 10,990 | 58,920 | | Forks | 6,955 | 1,498 | | Open issues | 854 | 8 | | Language | Jupyter Notebook | Jupyter Notebook | | Adopt for | Jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker | llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0, allowing free use for any purpose with conditions on attribution and license preservation | MIT License | | Categories | Inference & Serving, Model Training | Data & Retrieval, Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Maintenance | Active (82%) | Steady (60%) | | Days since push | 10d | 74d | | Open issues (now) | 854 | 8 | | Stars delta | +6 (30d) | -117 (30d) | | Open issues delta | +5 (30d) | 0 (30d) | | Full report | [trust report](/tools/aws-amazon-sagemaker-examples/trust.md) | [trust report](/tools/pathwaycom-llm-app/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: llm-app - **Pricing:** freemium - The repository is open-source under the MIT License, but additional services or support might incur costs. - **Requirements:** Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. - **Adopt for:** llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. - **License detail:** MIT License ## Choose when ### Choose amazon-sagemaker-examples if… - License: amazon-sagemaker-examples is Apache-2.0, llm-app is MIT. - Tags unique to amazon-sagemaker-examples: aws, data-science, deep-learning, inference. - When you need examples specific to building models with Amazon SageMaker ### Choose llm-app if… - License: llm-app is MIT, amazon-sagemaker-examples is Apache-2.0. - Pricing: The repository is open-source under the MIT License, but additional services or support might incur costs.. - Requirements: Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs.. - Tags unique to llm-app: chatbot, hugging-face, llm, llm-local. - Also covers Data & Retrieval, Evaluation & Observability. - When you need ready-to-run cloud templates for RAG, AI pipelines, and enterprise search that integrate seamlessly with data sources such as Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-ti ## 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 llm-app - Avoid using llm-app if your project does not require integration with specific data sources like Sharepoint or Google Drive, as the tool's strength lies in its broad data source support. - Do not use llm-app if you are looking for a tool that focuses solely on model training or inference without the need for cloud templates or enterprise search capabilities. ## Common questions ### What is the difference between amazon-sagemaker-examples and llm-app? amazon-sagemaker-examples: Jupyter notebooks for building, training, and deploying ML models using Amazon SageMaker. llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. See the comparison table for live GitHub stats and shared categories. ### When should I choose amazon-sagemaker-examples over llm-app? Choose amazon-sagemaker-examples over llm-app when License: amazon-sagemaker-examples is Apache-2.0, llm-app is MIT; 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 llm-app over amazon-sagemaker-examples? Choose llm-app over amazon-sagemaker-examples when License: llm-app is MIT, amazon-sagemaker-examples is Apache-2.0; Pricing: The repository is open-source under the MIT License, but additional services or support might incur costs.; Requirements: Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs.; Tags unique to llm-app: chatbot, hugging-face, llm, llm-local; Also covers Data & Retrieval, Evaluation & Observability; When you need ready-to-run cloud templates for RAG, AI pipelines, and enterprise search that integrate seamlessly with data sources such as Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-ti. ### 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 llm-app? Avoid using llm-app if your project does not require integration with specific data sources like Sharepoint or Google Drive, as the tool's strength lies in its broad data source support. Do not use llm-app if you are looking for a tool that focuses solely on model training or inference without the need for cloud templates or enterprise search capabilities. ### Is amazon-sagemaker-examples or llm-app more popular on GitHub? llm-app has more GitHub stars (58,920 vs 10,990). Stars measure visibility, not whether either tool fits your constraints. ### Are amazon-sagemaker-examples and llm-app open source? Yes - both are open-source projects on GitHub (amazon-sagemaker-examples: Apache-2.0, llm-app: MIT). ### Where can I find alternatives to amazon-sagemaker-examples or llm-app? GraphCanon lists graph-backed alternatives at [amazon-sagemaker-examples alternatives](/tools/aws-amazon-sagemaker-examples/alternatives) and [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) ([amazon-sagemaker-examples markdown twin](/tools/aws-amazon-sagemaker-examples/alternatives.md), [llm-app markdown twin](/tools/pathwaycom-llm-app/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-pathwaycom-llm-app.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 llm-app? amazon-sagemaker-examples: Active. llm-app: 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 llm-app? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [amazon-sagemaker-examples trust report](/tools/aws-amazon-sagemaker-examples/trust); [llm-app trust report](/tools/pathwaycom-llm-app/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/_