--- title: "Awesome-LLMOps vs gateway" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/tensorchord-awesome-llmops-vs-trylonai-gateway" tools: ["tensorchord-awesome-llmops", "trylonai-gateway"] --- # Awesome-LLMOps vs gateway *GraphCanon updated Jul 15, 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 gateway if self-hosted firewall for securing AI applications with guardrails and content moderation. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) reports 5.9k GitHub stars, 901 forks, and 157 open issues, last pushed May 21, 2026. [gateway](https://www.trylon.ai) has 126 stars, 12 forks, and 0 open issues, last pushed Jun 25, 2025. Figures are from public GitHub metadata via [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps) and [gateway's repository](https://github.com/trylonai/gateway). | | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | [gateway](/tools/trylonai-gateway.md) | | --- | --- | --- | | Tagline | An awesome & curated list of best LLMOps tools for developers | Self-hosted firewall for securing AI applications with guardrails and content moderation. | | Stars | 5,877 | 126 | | Forks | 901 | 12 | | Open issues | 157 | 0 | | Language | Shell | Python | | 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. | Self-hosted firewall for securing AI applications with guardrails and content moderation. | | Persona | - | - | | Runtime | - | - | | License | CC0-1.0 | Other | | Categories | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | [gateway](/tools/trylonai-gateway.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 51d | 385d | | Open issues (now) | 157 | 0 | | Full report | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | [trust report](/tools/trylonai-gateway/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: gateway - **Pricing:** freemium - Open-source with no explicit monetary cost, but requires users to handle infrastructure costs associated with local Docker deployment - **Requirements:** Min 4 GB RAM; Requires Docker; Docker and Docker Compose are required for deployment; Initial launch takes several minutes due to downloading machine learning models (~1.5GB+) for the first time - **Adopt for:** Self-hosted firewall for securing AI applications with guardrails and content moderation. ## Choose when ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; gateway is Python. - License: Awesome-LLMOps is CC0-1.0, gateway is Other. - 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. ### Choose gateway if… - gateway is primarily Python; Awesome-LLMOps is Shell. - License: gateway is Other, Awesome-LLMOps is CC0-1.0. - Pricing: Open-source with no explicit monetary cost, but requires users to handle infrastructure costs associated with local Docker deployment. - Requirements: Min 4 GB RAM; Requires Docker; Docker and Docker Compose are required for deployment; Initial launch takes several minutes due to downloading machine learning models (~1.5GB+) for the first time. - Tags unique to gateway: content-moderation, docker-compose, guardrails, pii-redaction. - gateway ships Docker support for self-hosted deployment. - When you need to self-host a secure gateway for LLM-based AI applications that requires local deployment via 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 gateway - Avoid if you are looking for cloud-managed services, as this tool is designed solely for local or self-hosted environments - Not suitable if you require extensive customization beyond the provided policies.yaml file and predefined guardrails for content moderation and security ## Common questions ### What is the difference between Awesome-LLMOps and gateway? Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. gateway: Self-hosted firewall for securing AI applications with guardrails and content moderation.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLMOps over gateway? Choose Awesome-LLMOps over gateway when Awesome-LLMOps is primarily Shell; gateway is Python; License: Awesome-LLMOps is CC0-1.0, gateway is Other; 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 choose gateway over Awesome-LLMOps? Choose gateway over Awesome-LLMOps when gateway is primarily Python; Awesome-LLMOps is Shell; License: gateway is Other, Awesome-LLMOps is CC0-1.0; Pricing: Open-source with no explicit monetary cost, but requires users to handle infrastructure costs associated with local Docker deployment; Requirements: Min 4 GB RAM; Requires Docker; Docker and Docker Compose are required for deployment; Initial launch takes several minutes due to downloading machine learning models (~1.5GB+) for the first time; Tags unique to gateway: content-moderation, docker-compose, guardrails, pii-redaction; gateway ships Docker support for self-hosted deployment; When you need to self-host a secure gateway for LLM-based AI applications that requires local deployment via 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 gateway? Avoid if you are looking for cloud-managed services, as this tool is designed solely for local or self-hosted environments Not suitable if you require extensive customization beyond the provided policies.yaml file and predefined guardrails for content moderation and security ### Is Awesome-LLMOps or gateway more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,877 vs 126). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMOps and gateway open source? Yes - both are open-source projects on GitHub (Awesome-LLMOps: CC0-1.0, gateway: Other). ### Where can I find alternatives to Awesome-LLMOps or gateway? GraphCanon lists graph-backed alternatives at [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) and [gateway alternatives](/tools/trylonai-gateway/alternatives) ([Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/alternatives.md), [gateway markdown twin](/tools/trylonai-gateway/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-trylonai-gateway.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-LLMOps or gateway? Awesome-LLMOps: Steady. gateway: 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 gateway? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust); [gateway trust report](/tools/trylonai-gateway/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/_