--- title: "Awesome-LLMOps vs deploy-llms-with-ansible" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/tensorchord-awesome-llmops-vs-xamey-deploy-llms-with-ansible" tools: ["tensorchord-awesome-llmops", "xamey-deploy-llms-with-ansible"] --- # Awesome-LLMOps vs deploy-llms-with-ansible *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 deploy-llms-with-ansible if deploy-llms-with-ansible. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) reports 5.9k GitHub stars, 993 forks, and 247 open issues, last pushed May 21, 2026. [deploy-llms-with-ansible](https://github.com/xamey/deploy-llms-with-ansible) has 3 stars, 0 forks, and 0 open issues, last pushed May 1, 2025. Figures are from public GitHub metadata via [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps) and [deploy-llms-with-ansible's repository](https://github.com/xamey/deploy-llms-with-ansible). | | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | [deploy-llms-with-ansible](/tools/xamey-deploy-llms-with-ansible.md) | | --- | --- | --- | | Tagline | An awesome & curated list of best LLMOps tools for developers | Easily deploy LLMs using Ansible | | Stars | 5,915 | 3 | | Forks | 993 | 0 | | Open issues | 247 | 0 | | Language | Shell | - | | 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. | deploy-llms-with-ansible | | Persona | - | - | | Runtime | - | - | | License | CC0-1.0 | - | | Categories | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | [deploy-llms-with-ansible](/tools/xamey-deploy-llms-with-ansible.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 91d | 462d | | Open issues (now) | 247 | 0 | | Stars delta | +28 (30d) | Unknown | | Open issues delta | +66 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | [trust report](/tools/xamey-deploy-llms-with-ansible/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: deploy-llms-with-ansible - **Pricing:** unknown - **Requirements:** Requires Docker; Requires Ansible installed and configured on the local machine.; Debian-based VM with SSH access and Docker must be present. - **Adopt for:** deploy-llms-with-ansible ## Choose when ### Choose Awesome-LLMOps if… - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, 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 deploy-llms-with-ansible if… - Requirements: Requires Docker; Requires Ansible installed and configured on the local machine.; Debian-based VM with SSH access and Docker must be present.. - Tags unique to deploy-llms-with-ansible: ansible, deployment, docker, llama-cpp. - When you prefer using Ansible to automate the deployment of LLMs on a Debian-based virtual machine equipped with Docker. ## 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 deploy-llms-with-ansible - When working in an environment that uses alternative automation tools like Terraform or Chef, as this tool specifically requires Ansible knowledge. - If the infrastructure does not support or permit the use of Docker for containerizing applications. - In cases where extensive customization of models beyond what llama.cpp and Ollama offer is required. ## Common questions ### What is the difference between Awesome-LLMOps and deploy-llms-with-ansible? Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. deploy-llms-with-ansible: Easily deploy LLMs using Ansible. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLMOps over deploy-llms-with-ansible? Choose Awesome-LLMOps over deploy-llms-with-ansible when Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, 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 deploy-llms-with-ansible over Awesome-LLMOps? Choose deploy-llms-with-ansible over Awesome-LLMOps when Requirements: Requires Docker; Requires Ansible installed and configured on the local machine.; Debian-based VM with SSH access and Docker must be present.; Tags unique to deploy-llms-with-ansible: ansible, deployment, docker, llama-cpp; When you prefer using Ansible to automate the deployment of LLMs on a Debian-based virtual machine equipped with Docker. ### 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 deploy-llms-with-ansible? When working in an environment that uses alternative automation tools like Terraform or Chef, as this tool specifically requires Ansible knowledge. If the infrastructure does not support or permit the use of Docker for containerizing applications. In cases where extensive customization of models beyond what llama.cpp and Ollama offer is required. ### Is Awesome-LLMOps or deploy-llms-with-ansible more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 3). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMOps and deploy-llms-with-ansible open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Awesome-LLMOps or deploy-llms-with-ansible? GraphCanon lists graph-backed alternatives at [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) and [deploy-llms-with-ansible alternatives](/tools/xamey-deploy-llms-with-ansible/alternatives) ([Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/alternatives.md), [deploy-llms-with-ansible markdown twin](/tools/xamey-deploy-llms-with-ansible/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-xamey-deploy-llms-with-ansible.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-LLMOps or deploy-llms-with-ansible? Awesome-LLMOps: Slowing. deploy-llms-with-ansible: 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 Awesome-LLMOps and deploy-llms-with-ansible? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust); [deploy-llms-with-ansible trust report](/tools/xamey-deploy-llms-with-ansible/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/_