--- title: "llm-app vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pathwaycom-llm-app-vs-tensorchord-awesome-llmops" tools: ["pathwaycom-llm-app", "tensorchord-awesome-llmops"] --- # llm-app vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick llm-app if llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz; 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. [llm-app](https://pathway.com/developers/templates/) reports 59k GitHub stars, 1.5k forks, and 8 open issues, last pushed Jul 5, 2026. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 993 forks, and 247 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [llm-app's repository](https://github.com/pathwaycom/llm-app) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [llm-app](/tools/pathwaycom-llm-app.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | An awesome & curated list of best LLMOps tools for developers | | Stars | 59,037 | 5,915 | | Forks | 1,466 | 993 | | Open issues | 8 | 247 | | Language | Jupyter Notebook | Shell | | Adopt for | llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz | 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 | CC0-1.0 | | Categories | Data & Retrieval, LLM Frameworks, Vector Databases | 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._ | | [llm-app](/tools/pathwaycom-llm-app.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 41d | 91d | | Open issues (now) | 8 | 247 | | Stars delta | +11 (30d) | +28 (30d) | | Open issues delta | -2 (30d) | +66 (30d) | | Full report | [trust report](/tools/pathwaycom-llm-app/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | **Typed relationship:** llm-app _(related)_ Awesome-LLMOps Both repositories are curated lists or collections of tools and resources focused on LLM development, making them relevant to each other but not directly integrating or being alternatives. ## Decision facts: llm-app - **Requirements:** Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others. - **Adopt for:** llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz ## 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 llm-app if… - llm-app is primarily Jupyter Notebook; Awesome-LLMOps is Shell. - License: llm-app is MIT, Awesome-LLMOps is CC0-1.0. - Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.. - Both repositories are curated lists or collections of tools and resources focused on LLM development, making them relevant to each other but not directly integrating or being alternatives. - Tags unique to llm-app: chatbot, hugging-face, llm, retrieval-augmented-generation. - Also covers Vector Databases. - - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; llm-app is Jupyter Notebook. - License: Awesome-LLMOps is CC0-1.0, llm-app is MIT. - Both repositories are curated lists or collections of tools and resources focused on LLM development, making them relevant to each other but not directly integrating or being alternatives. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Evaluation & Observability, Inference & Serving, Model Training, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ## When NOT to use llm-app - - You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app. - - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app. ## 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 llm-app and Awesome-LLMOps? llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. 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 llm-app over Awesome-LLMOps? Choose llm-app over Awesome-LLMOps when llm-app is primarily Jupyter Notebook; Awesome-LLMOps is Shell; License: llm-app is MIT, Awesome-LLMOps is CC0-1.0; Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.; Both repositories are curated lists or collections of tools and resources focused on LLM development, making them relevant to each other but not directly integrating or being alternatives; Tags unique to llm-app: chatbot, hugging-face, llm, retrieval-augmented-generation; Also covers Vector Databases; - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs. ### When should I choose Awesome-LLMOps over llm-app? Choose Awesome-LLMOps over llm-app when Awesome-LLMOps is primarily Shell; llm-app is Jupyter Notebook; License: Awesome-LLMOps is CC0-1.0, llm-app is MIT; Both repositories are curated lists or collections of tools and resources focused on LLM development, making them relevant to each other but not directly integrating or being alternatives; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Evaluation & Observability, Inference & Serving, Model Training, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### When should I avoid llm-app? - You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app. - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app. ### 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 llm-app or Awesome-LLMOps more popular on GitHub? llm-app has more GitHub stars (59,037 vs 5,915). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-app and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (llm-app: MIT, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to llm-app or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([llm-app markdown twin](/tools/pathwaycom-llm-app/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/pathwaycom-llm-app-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, llm-app or Awesome-LLMOps? llm-app: Steady. 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 llm-app and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-app trust report](/tools/pathwaycom-llm-app/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=pathwaycom-llm-app`](/api/graphcanon/graph?tool=pathwaycom-llm-app) - 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/_