--- title: "plano vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/katanemo-plano-vs-tensorchord-awesome-llmops" tools: ["katanemo-plano", "tensorchord-awesome-llmops"] --- # plano vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick plano if plano is an AI-native proxy and data plane for agentic applications built with Rust under the Apache-2.0 license, focusing on smart LLM routing capabilities alongside orchestration and observability features; 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. [plano](https://planoai.dev) reports 7.0k GitHub stars, 482 forks, and 138 open issues, last pushed Aug 19, 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 [plano's repository](https://github.com/katanemo/plano) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [plano](/tools/katanemo-plano.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | An AI-native proxy and data plane for agentic apps | An awesome & curated list of best LLMOps tools for developers | | Stars | 7,004 | 5,915 | | Forks | 482 | 993 | | Open issues | 138 | 247 | | Language | Rust | Shell | | Adopt for | Plano is an AI-native proxy and data plane for agentic applications built with Rust under the Apache-2.0 license, focusing on smart LLM routing capabilities alongside orchestration and observability features. | 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 | CC0-1.0 | | Categories | AI Agents, Evaluation & Observability, Inference & Serving | 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._ | | [plano](/tools/katanemo-plano.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 91d | | Open issues (now) | 138 | 247 | | Stars delta | +127 (30d) | +28 (30d) | | Open issues delta | +7 (30d) | +66 (30d) | | Full report | [trust report](/tools/katanemo-plano/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: plano - **Pricing:** freemium - Freely available under Apache-2.0 license but potential for premium services around support and advanced features. - **Requirements:** Min 1 GB RAM - **Adopt for:** Plano is an AI-native proxy and data plane for agentic applications built with Rust under the Apache-2.0 license, focusing on smart LLM routing capabilities alongside orchestration and observability features. ## 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 plano if… - plano is primarily Rust; Awesome-LLMOps is Shell. - License: plano is Apache-2.0, Awesome-LLMOps is CC0-1.0. - Pricing: Freely available under Apache-2.0 license but potential for premium services around support and advanced features.. - Requirements: Min 1 GB RAM. - Tags unique to plano: agency-apps, ai-gateway, llm-proxy, llm-routing. - Also covers AI Agents. - plano ships Docker support for self-hosted deployment. - You are working on building complex multi-agent workflows where intelligent LLM (Language Model) routing is required. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; plano is Rust. - License: Awesome-LLMOps is CC0-1.0, plano is Apache-2.0. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Data & Retrieval, LLM Frameworks, 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 plano - If your application does not require deep orchestration or smart routing between multiple models, choosing Plano might introduce unnecessary complexity. - For scenarios where minimal intervention routing is preferred without advanced observability and guardrail features, this tool may over-deliver on certain functionalities, possibly increasing overhead ## 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 plano and Awesome-LLMOps? plano: An AI-native proxy and data plane for agentic apps. 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 plano over Awesome-LLMOps? Choose plano over Awesome-LLMOps when plano is primarily Rust; Awesome-LLMOps is Shell; License: plano is Apache-2.0, Awesome-LLMOps is CC0-1.0; Pricing: Freely available under Apache-2.0 license but potential for premium services around support and advanced features.; Requirements: Min 1 GB RAM; Tags unique to plano: agency-apps, ai-gateway, llm-proxy, llm-routing; Also covers AI Agents; plano ships Docker support for self-hosted deployment; You are working on building complex multi-agent workflows where intelligent LLM (Language Model) routing is required. ### When should I choose Awesome-LLMOps over plano? Choose Awesome-LLMOps over plano when Awesome-LLMOps is primarily Shell; plano is Rust; License: Awesome-LLMOps is CC0-1.0, plano is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, LLM Frameworks, 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 plano? If your application does not require deep orchestration or smart routing between multiple models, choosing Plano might introduce unnecessary complexity. For scenarios where minimal intervention routing is preferred without advanced observability and guardrail features, this tool may over-deliver on certain functionalities, possibly increasing overhead ### 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 plano or Awesome-LLMOps more popular on GitHub? plano has more GitHub stars (7,004 vs 5,915). Stars measure visibility, not whether either tool fits your constraints. ### Are plano and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (plano: Apache-2.0, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to plano or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [plano alternatives](/tools/katanemo-plano/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([plano markdown twin](/tools/katanemo-plano/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/katanemo-plano-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, plano or Awesome-LLMOps? plano: Very 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 plano and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [plano trust report](/tools/katanemo-plano/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=katanemo-plano`](/api/graphcanon/graph?tool=katanemo-plano) - 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/_