--- title: "starwhale vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/star-whale-starwhale-vs-tensorchord-awesome-llmops" tools: ["star-whale-starwhale", "tensorchord-awesome-llmops"] --- # starwhale vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick starwhale if starwhale is an MLOps/LLMOps platform that focuses on model management for AI and large language models through various runtime setups; 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. [starwhale](https://starwhale.ai) reports 237 GitHub stars, 38 forks, and 120 open issues, last pushed Dec 20, 2024. [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 [starwhale's repository](https://github.com/star-whale/starwhale) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [starwhale](/tools/star-whale-starwhale.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | an MLOps/LLMOps platform | An awesome & curated list of best LLMOps tools for developers | | Stars | 237 | 5,915 | | Forks | 38 | 993 | | Open issues | 120 | 247 | | Language | Java | Shell | | Adopt for | Starwhale is an MLOps/LLMOps platform that focuses on model management for AI and large language models through various runtime setups. | 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 | Starwhale uses the Apache License 2.0, which is permissive and allows for usage in both open source and commercial applications with attribution. | CC0-1.0 | | Categories | Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | 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._ | | [starwhale](/tools/star-whale-starwhale.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 591d | 91d | | Open issues (now) | 120 | 247 | | Stars delta | Unknown | +28 (30d) | | Open issues delta | Unknown | +66 (30d) | | Full report | [trust report](/tools/star-whale-starwhale/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: starwhale - **Requirements:** Requires Docker; Supports runtime builds through Docker images for compatibility across various system environments. - **Adopt for:** Starwhale is an MLOps/LLMOps platform that focuses on model management for AI and large language models through various runtime setups. - **License detail:** Starwhale uses the Apache License 2.0, which is permissive and allows for usage in both open source and commercial applications with attribution. ## 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 starwhale if… - starwhale is primarily Java; Awesome-LLMOps is Shell. - License: starwhale is Apache-2.0, Awesome-LLMOps is CC0-1.0. - Requirements: Requires Docker; Supports runtime builds through Docker images for compatibility across various system environments.. - Tags unique to starwhale: ai, cloud-native, dataset, datastore. - When the need arises to manage models across different runtimes, including local environment configurations via runtime.yaml or conda environments, Docker images, or shell commands. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; starwhale is Java. - License: Awesome-LLMOps is CC0-1.0, starwhale is Apache-2.0. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ## When NOT to use starwhale - In scenarios where an exclusive user preference leans towards Python-based operations over Java and the tool's CLI interactions do not meet operational demands. - For teams that require real-time model serving and have strict latency requirements, as Starwhale may not optimize for such use cases beyond its MLOps capabilities. ## 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 starwhale and Awesome-LLMOps? starwhale: an MLOps/LLMOps platform. 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 starwhale over Awesome-LLMOps? Choose starwhale over Awesome-LLMOps when starwhale is primarily Java; Awesome-LLMOps is Shell; License: starwhale is Apache-2.0, Awesome-LLMOps is CC0-1.0; Requirements: Requires Docker; Supports runtime builds through Docker images for compatibility across various system environments.; Tags unique to starwhale: ai, cloud-native, dataset, datastore; When the need arises to manage models across different runtimes, including local environment configurations via runtime.yaml or conda environments, Docker images, or shell commands. ### When should I choose Awesome-LLMOps over starwhale? Choose Awesome-LLMOps over starwhale when Awesome-LLMOps is primarily Shell; starwhale is Java; License: Awesome-LLMOps is CC0-1.0, starwhale is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### When should I avoid starwhale? In scenarios where an exclusive user preference leans towards Python-based operations over Java and the tool's CLI interactions do not meet operational demands. For teams that require real-time model serving and have strict latency requirements, as Starwhale may not optimize for such use cases beyond its MLOps capabilities. ### 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 starwhale or Awesome-LLMOps more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 237). Stars measure visibility, not whether either tool fits your constraints. ### Are starwhale and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (starwhale: Apache-2.0, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to starwhale or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [starwhale alternatives](/tools/star-whale-starwhale/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([starwhale markdown twin](/tools/star-whale-starwhale/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/star-whale-starwhale-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, starwhale or Awesome-LLMOps? starwhale: Dormant. 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 starwhale and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [starwhale trust report](/tools/star-whale-starwhale/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=star-whale-starwhale`](/api/graphcanon/graph?tool=star-whale-starwhale) - 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/_