--- title: "AI-Infra-Guard vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/tencent-ai-infra-guard-vs-tensorchord-awesome-llmops" tools: ["tencent-ai-infra-guard", "tensorchord-awesome-llmops"] --- # AI-Infra-Guard vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick AI-Infra-Guard if aI-Infra-Guard is designed for organizations looking to secure their AI infrastructures comprehensively through various scanning and evaluation tools; 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. [AI-Infra-Guard](https://tencent.github.io/AI-Infra-Guard/) reports 4.3k GitHub stars, 419 forks, and 13 open issues, last pushed Jul 28, 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 [AI-Infra-Guard's repository](https://github.com/Tencent/AI-Infra-Guard) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [AI-Infra-Guard](/tools/tencent-ai-infra-guard.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | A full-stack AI Red Teaming platform securing AI ecosystems | An awesome & curated list of best LLMOps tools for developers | | Stars | 4,316 | 5,915 | | Forks | 419 | 993 | | Open issues | 13 | 247 | | Language | Python | Shell | | Adopt for | AI-Infra-Guard is designed for organizations looking to secure their AI infrastructures comprehensively through various scanning and evaluation tools. | 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 | Evaluation & Observability, LLM Frameworks | 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._ | | [AI-Infra-Guard](/tools/tencent-ai-infra-guard.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 91d | | Open issues (now) | 13 | 247 | | Stars delta | Unknown | +28 (30d) | | Open issues delta | Unknown | +66 (30d) | | Full report | [trust report](/tools/tencent-ai-infra-guard/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: AI-Infra-Guard - **Adopt for:** AI-Infra-Guard is designed for organizations looking to secure their AI infrastructures comprehensively through various scanning and evaluation tools. ## 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 AI-Infra-Guard if… - AI-Infra-Guard is primarily Python; Awesome-LLMOps is Shell. - License: AI-Infra-Guard is Apache-2.0, Awesome-LLMOps is CC0-1.0. - Tags unique to AI-Infra-Guard: agent-security, ai-red-teaming, llm-evaluation, security-tools. - AI-Infra-Guard ships Docker support for self-hosted deployment. - If you need advanced LLM jailbreak evaluation capabilities specific to the vulnerabilities identified by Tencent's research, consider using AI-Infra-Guard. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; AI-Infra-Guard is Python. - License: Awesome-LLMOps is CC0-1.0, AI-Infra-Guard is Apache-2.0. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Data & Retrieval, Inference & Serving, 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 AI-Infra-Guard - Avoid if you are looking exclusively for a tool that focuses solely on the runtime behavior of LLMs without broader infrastructural scanning capabilities. - Not recommended when your primary focus is on network-level security rather than comprehensive AI infrastructure security assessments and evaluations. ## 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 AI-Infra-Guard and Awesome-LLMOps? AI-Infra-Guard: A full-stack AI Red Teaming platform securing AI ecosystems. 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 AI-Infra-Guard over Awesome-LLMOps? Choose AI-Infra-Guard over Awesome-LLMOps when AI-Infra-Guard is primarily Python; Awesome-LLMOps is Shell; License: AI-Infra-Guard is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to AI-Infra-Guard: agent-security, ai-red-teaming, llm-evaluation, security-tools; AI-Infra-Guard ships Docker support for self-hosted deployment; If you need advanced LLM jailbreak evaluation capabilities specific to the vulnerabilities identified by Tencent's research, consider using AI-Infra-Guard. ### When should I choose Awesome-LLMOps over AI-Infra-Guard? Choose Awesome-LLMOps over AI-Infra-Guard when Awesome-LLMOps is primarily Shell; AI-Infra-Guard is Python; License: Awesome-LLMOps is CC0-1.0, AI-Infra-Guard is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Inference & Serving, 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 AI-Infra-Guard? Avoid if you are looking exclusively for a tool that focuses solely on the runtime behavior of LLMs without broader infrastructural scanning capabilities. Not recommended when your primary focus is on network-level security rather than comprehensive AI infrastructure security assessments and evaluations. ### 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 AI-Infra-Guard or Awesome-LLMOps more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 4,316). Stars measure visibility, not whether either tool fits your constraints. ### Are AI-Infra-Guard and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (AI-Infra-Guard: Apache-2.0, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to AI-Infra-Guard or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [AI-Infra-Guard alternatives](/tools/tencent-ai-infra-guard/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([AI-Infra-Guard markdown twin](/tools/tencent-ai-infra-guard/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/tencent-ai-infra-guard-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, AI-Infra-Guard or Awesome-LLMOps? AI-Infra-Guard: 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 AI-Infra-Guard and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AI-Infra-Guard trust report](/tools/tencent-ai-infra-guard/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=tencent-ai-infra-guard`](/api/graphcanon/graph?tool=tencent-ai-infra-guard) - 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/_