--- title: "Awesome-LLMOps vs zeno" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/tensorchord-awesome-llmops-vs-zeno-ml-zeno" tools: ["tensorchord-awesome-llmops", "zeno-ml-zeno"] --- # Awesome-LLMOps vs zeno *GraphCanon updated Aug 20, 2026* ## Verdict 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; pick zeno if zeno combines Python API with an interactive UI for evaluating ML models across various tasks. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) reports 5.9k GitHub stars, 993 forks, and 247 open issues, last pushed May 21, 2026. [zeno](https://zenoml.com) has 214 stars, 11 forks, and 45 open issues, last pushed Oct 5, 2023. Figures are from public GitHub metadata via [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps) and [zeno's repository](https://github.com/zeno-ml/zeno). | | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | [zeno](/tools/zeno-ml-zeno.md) | | --- | --- | --- | | Tagline | An awesome & curated list of best LLMOps tools for developers | AI Data Management & Evaluation Platform | | Stars | 5,915 | 214 | | Forks | 993 | 11 | | Open issues | 247 | 45 | | Language | Shell | Svelte | | 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. | Zeno combines Python API with an interactive UI for evaluating ML models across various tasks. | | Persona | - | - | | Runtime | - | - | | License | CC0-1.0 | MIT | | Categories | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | [zeno](/tools/zeno-ml-zeno.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Archived (8%) | | Days since push | 91d | 1032d | | Archived on GitHub | No | Yes | | Open issues (now) | 247 | 45 | | Stars delta | +28 (30d) | Unknown | | Open issues delta | +66 (30d) | Unknown | | Full report | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | [trust report](/tools/zeno-ml-zeno/trust.md) | ## 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. ## Decision facts: zeno - **Adopt for:** Zeno combines Python API with an interactive UI for evaluating ML models across various tasks. ## Choose when ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; zeno is Svelte. - License: Awesome-LLMOps is CC0-1.0, zeno is MIT. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Data & Retrieval, 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. ### Choose zeno if… - zeno is primarily Svelte; Awesome-LLMOps is Shell. - License: zeno is MIT, Awesome-LLMOps is CC0-1.0. - Tags unique to zeno: ai, data-science, evaluation-framework, machine-learning. - You need to analyze model performance interactively via a user interface ## 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. ## When NOT to use zeno - Your project requires real-time monitoring capabilities not provided by Zeno's evaluation framework - If you are looking for a specialized tool tailored only to specific data types, like just images or text without the modular versatility of Zeno ## Common questions ### What is the difference between Awesome-LLMOps and zeno? Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. zeno: AI Data Management & Evaluation Platform. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLMOps over zeno? Choose Awesome-LLMOps over zeno when Awesome-LLMOps is primarily Shell; zeno is Svelte; License: Awesome-LLMOps is CC0-1.0, zeno is MIT; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, 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 choose zeno over Awesome-LLMOps? Choose zeno over Awesome-LLMOps when zeno is primarily Svelte; Awesome-LLMOps is Shell; License: zeno is MIT, Awesome-LLMOps is CC0-1.0; Tags unique to zeno: ai, data-science, evaluation-framework, machine-learning; You need to analyze model performance interactively via a user interface. ### 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. ### When should I avoid zeno? Your project requires real-time monitoring capabilities not provided by Zeno's evaluation framework If you are looking for a specialized tool tailored only to specific data types, like just images or text without the modular versatility of Zeno ### Is Awesome-LLMOps or zeno more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 214). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMOps and zeno open source? Yes - both are open-source projects on GitHub (Awesome-LLMOps: CC0-1.0, zeno: MIT). ### Where can I find alternatives to Awesome-LLMOps or zeno? GraphCanon lists graph-backed alternatives at [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) and [zeno alternatives](/tools/zeno-ml-zeno/alternatives) ([Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/alternatives.md), [zeno markdown twin](/tools/zeno-ml-zeno/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/tensorchord-awesome-llmops-vs-zeno-ml-zeno.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-LLMOps or zeno? Awesome-LLMOps: Slowing. zeno: Archived. 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 Awesome-LLMOps and zeno? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust); [zeno trust report](/tools/zeno-ml-zeno/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=tensorchord-awesome-llmops`](/api/graphcanon/graph?tool=tensorchord-awesome-llmops) - 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/_