--- title: "kedro-viz vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kedro-org-kedro-viz-vs-tensorchord-awesome-llmops" tools: ["kedro-org-kedro-viz", "tensorchord-awesome-llmops"] --- # kedro-viz vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick kedro-viz if kedro-Viz is tailored for visualizing Kedro projects and tracking experiments within the Kedro framework; 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. [kedro-viz](https://demo.kedro.org) reports 753 GitHub stars, 125 forks, and 73 open issues, last pushed Aug 1, 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 [kedro-viz's repository](https://github.com/kedro-org/kedro-viz) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [kedro-viz](/tools/kedro-org-kedro-viz.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | Visualise Kedro data pipelines and track experiments. | An awesome & curated list of best LLMOps tools for developers | | Stars | 753 | 5,915 | | Forks | 125 | 993 | | Open issues | 73 | 247 | | Language | JavaScript | Shell | | Adopt for | Kedro-Viz is tailored for visualizing Kedro projects and tracking experiments within the Kedro framework. | 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 | Licensed under Apache-2.0 | CC0-1.0 | | Categories | Data & Retrieval | 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._ | | [kedro-viz](/tools/kedro-org-kedro-viz.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 1d | 91d | | Open issues (now) | 73 | 247 | | Stars delta | Unknown | +28 (30d) | | Open issues delta | Unknown | +66 (30d) | | Full report | [trust report](/tools/kedro-org-kedro-viz/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: kedro-viz - **Hosting:** self hosted - **Adopt for:** Kedro-Viz is tailored for visualizing Kedro projects and tracking experiments within the Kedro framework. - **License detail:** Licensed under Apache-2.0 ## 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 kedro-viz if… - kedro-viz is primarily JavaScript; Awesome-LLMOps is Shell. - License: kedro-viz is Apache-2.0, Awesome-LLMOps is CC0-1.0. - Tags unique to kedro-viz: data-visualization, experiment tracking, kedro-extension, kedro-plugin. - When working with existing Kedro projects to visualize pipelines and gain insights into experiment outcomes efficiently. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; kedro-viz is JavaScript. - License: Awesome-LLMOps is CC0-1.0, kedro-viz is Apache-2.0. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Evaluation & Observability, Inference & Serving, 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 kedro-viz - For pipeline visualization and experiment tracking if the project is not built on the Kedro framework, as integration may be cumbersome or unsupported. - In settings where real-time collaboration features are essential, as its focus leans towards individual or pre-defined team workflows within a specific tech stack. ## 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 kedro-viz and Awesome-LLMOps? kedro-viz: Visualise Kedro data pipelines and track experiments.. 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 kedro-viz over Awesome-LLMOps? Choose kedro-viz over Awesome-LLMOps when kedro-viz is primarily JavaScript; Awesome-LLMOps is Shell; License: kedro-viz is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to kedro-viz: data-visualization, experiment tracking, kedro-extension, kedro-plugin; When working with existing Kedro projects to visualize pipelines and gain insights into experiment outcomes efficiently. ### When should I choose Awesome-LLMOps over kedro-viz? Choose Awesome-LLMOps over kedro-viz when Awesome-LLMOps is primarily Shell; kedro-viz is JavaScript; License: Awesome-LLMOps is CC0-1.0, kedro-viz is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Evaluation & Observability, Inference & Serving, 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 kedro-viz? For pipeline visualization and experiment tracking if the project is not built on the Kedro framework, as integration may be cumbersome or unsupported. In settings where real-time collaboration features are essential, as its focus leans towards individual or pre-defined team workflows within a specific tech stack. ### 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 kedro-viz or Awesome-LLMOps more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 753). Stars measure visibility, not whether either tool fits your constraints. ### Are kedro-viz and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (kedro-viz: Apache-2.0, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to kedro-viz or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [kedro-viz alternatives](/tools/kedro-org-kedro-viz/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([kedro-viz markdown twin](/tools/kedro-org-kedro-viz/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/kedro-org-kedro-viz-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, kedro-viz or Awesome-LLMOps? kedro-viz: 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 kedro-viz and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [kedro-viz trust report](/tools/kedro-org-kedro-viz/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kedro-org-kedro-viz`](/api/graphcanon/graph?tool=kedro-org-kedro-viz) - 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/_