--- title: "llm-app vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pathwaycom-llm-app-vs-visenger-awesome-mlops" tools: ["pathwaycom-llm-app", "visenger-awesome-mlops"] --- # llm-app vs awesome-mlops *GraphCanon updated Oct 4, 2026* ## Verdict 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; pick awesome-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. [llm-app](https://pathway.com/developers/templates/) reports 59k GitHub stars, 1.5k forks, and 8 open issues, last pushed Jul 5, 2026. [awesome-mlops](https://ml-ops.org) has 14k stars, 2.1k forks, and 47 open issues, last pushed Nov 21, 2024. Figures are from public GitHub metadata via [llm-app's repository](https://github.com/pathwaycom/llm-app) and [awesome-mlops's repository](https://github.com/visenger/awesome-mlops). | | [llm-app](/tools/pathwaycom-llm-app.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Tagline | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data | A curated list of references for MLOps | | Stars | 58,920 | 14,228 | | Forks | 1,498 | 2,123 | | Open issues | 8 | 47 | | Language | Jupyter Notebook | - | | 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. | awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. | | Persona | - | - | | Runtime | - | - | | License | MIT License | - | | Categories | Data & Retrieval, Evaluation & Observability, Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-app](/tools/pathwaycom-llm-app.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 74d | 681d | | Open issues (now) | 8 | 47 | | Stars delta | -117 (30d) | +101 (30d) | | Open issues delta | 0 (30d) | +3 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/pathwaycom-llm-app/trust.md) | [trust report](/tools/visenger-awesome-mlops/trust.md) | ## 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 ## Decision facts: awesome-mlops - **Adopt for:** awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. ## Choose when ### Choose llm-app if… - 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 ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, data-science, devops, engineering. - If you need references covering online training and inference service architecture patterns, consider awesome-mlops. ## 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. ## When NOT to use awesome-mlops - Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. - Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance. ## Common questions ### What is the difference between llm-app and awesome-mlops? llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-app over awesome-mlops? Choose llm-app over awesome-mlops when 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 choose awesome-mlops over llm-app? Choose awesome-mlops over llm-app when Tags unique to awesome-mlops: ai, data-science, devops, engineering; If you need references covering online training and inference service architecture patterns, consider awesome-mlops. ### 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. ### When should I avoid awesome-mlops? Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance. ### Is llm-app or awesome-mlops more popular on GitHub? llm-app has more GitHub stars (58,920 vs 14,228). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-app and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to llm-app or awesome-mlops? GraphCanon lists graph-backed alternatives at [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) and [awesome-mlops alternatives](/tools/visenger-awesome-mlops/alternatives) ([llm-app markdown twin](/tools/pathwaycom-llm-app/alternatives.md), [awesome-mlops markdown twin](/tools/visenger-awesome-mlops/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-visenger-awesome-mlops.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-mlops? llm-app: Steady. awesome-mlops: Dormant. 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-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-app trust report](/tools/pathwaycom-llm-app/trust); [awesome-mlops trust report](/tools/visenger-awesome-mlops/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/_