--- title: "gateway vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/portkey-ai-gateway-vs-tensorchord-awesome-llmops" tools: ["portkey-ai-gateway", "tensorchord-awesome-llmops"] --- # gateway vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick gateway if a high-performance AI Gateway named 'gateway', supporting over 1,600 LLM connections and featuring various guardrail mechanisms for safer utilization; 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. [gateway](https://portkey.ai/features/ai-gateway) reports 13k GitHub stars, 1.2k forks, and 242 open issues, last pushed May 25, 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 [gateway's repository](https://github.com/Portkey-AI/gateway) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [gateway](/tools/portkey-ai-gateway.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | A high-performance AI Gateway connecting to over 1,600 LLMs with guardrails. | An awesome & curated list of best LLMOps tools for developers | | Stars | 12,668 | 5,915 | | Forks | 1,236 | 993 | | Open issues | 242 | 247 | | Language | TypeScript | Shell | | Adopt for | A high-performance AI Gateway named 'gateway', supporting over 1,600 LLM connections and featuring various guardrail mechanisms for safer utilization. | 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 | MIT | 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._ | | [gateway](/tools/portkey-ai-gateway.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 74d | 91d | | Open issues (now) | 242 | 247 | | Stars delta | +314 (30d) | +28 (30d) | | Open issues delta | +25 (30d) | +66 (30d) | | Full report | [trust report](/tools/portkey-ai-gateway/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: gateway - **Pricing:** unknown - The service is open source and MIT licensed; specific pricing for related services (if any) not detailed in repository. - **Requirements:** Developed using TypeScript; no Docker requirement specified for operation or development. - **Adopt for:** A high-performance AI Gateway named 'gateway', supporting over 1,600 LLM connections and featuring various guardrail mechanisms for safer utilization. ## 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 gateway if… - gateway is primarily TypeScript; Awesome-LLMOps is Shell. - License: gateway is MIT, Awesome-LLMOps is CC0-1.0. - Pricing: The service is open source and MIT licensed; specific pricing for related services (if any) not detailed in repository.. - Requirements: Developed using TypeScript; no Docker requirement specified for operation or development.. - Tags unique to gateway: ai-gateway, gateway, generative-ai, langchain. - gateway ships Docker support for self-hosted deployment. - Use gateway when you need a versatile platform capable of connecting to more than 1,600 LLM providers. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; gateway is TypeScript. - License: Awesome-LLMOps is CC0-1.0, gateway is MIT. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, 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 gateway - Avoid using gateway if your application only requires connection to a limited number of LLM providers, as its extensive provider support might introduce unnecessary complexity. - If you don't require sophisticated cost management features such as smart caching or usage analytics provided by the tool, consider simpler alternatives. ## 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 gateway and Awesome-LLMOps? gateway: A high-performance AI Gateway connecting to over 1,600 LLMs with guardrails.. 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 gateway over Awesome-LLMOps? Choose gateway over Awesome-LLMOps when gateway is primarily TypeScript; Awesome-LLMOps is Shell; License: gateway is MIT, Awesome-LLMOps is CC0-1.0; Pricing: The service is open source and MIT licensed; specific pricing for related services (if any) not detailed in repository.; Requirements: Developed using TypeScript; no Docker requirement specified for operation or development.; Tags unique to gateway: ai-gateway, gateway, generative-ai, langchain; gateway ships Docker support for self-hosted deployment; Use gateway when you need a versatile platform capable of connecting to more than 1,600 LLM providers. ### When should I choose Awesome-LLMOps over gateway? Choose Awesome-LLMOps over gateway when Awesome-LLMOps is primarily Shell; gateway is TypeScript; License: Awesome-LLMOps is CC0-1.0, gateway is MIT; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, 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 gateway? Avoid using gateway if your application only requires connection to a limited number of LLM providers, as its extensive provider support might introduce unnecessary complexity. If you don't require sophisticated cost management features such as smart caching or usage analytics provided by the tool, consider simpler alternatives. ### 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 gateway or Awesome-LLMOps more popular on GitHub? gateway has more GitHub stars (12,668 vs 5,915). Stars measure visibility, not whether either tool fits your constraints. ### Are gateway and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (gateway: MIT, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to gateway or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [gateway alternatives](/tools/portkey-ai-gateway/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([gateway markdown twin](/tools/portkey-ai-gateway/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/portkey-ai-gateway-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, gateway or Awesome-LLMOps? gateway: Steady. 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 gateway and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [gateway trust report](/tools/portkey-ai-gateway/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=portkey-ai-gateway`](/api/graphcanon/graph?tool=portkey-ai-gateway) - 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/_