--- title: "aqueduct vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/runllm-aqueduct-vs-tensorchord-awesome-llmops" tools: ["runllm-aqueduct", "tensorchord-awesome-llmops"] --- # aqueduct vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick aqueduct if aqueduct is a deprecated Go-based tool for orchestrating LLM and ML workloads across various cloud infrastructures with Kubernetes support; 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. [aqueduct](https://aqueducthq.com) reports 517 GitHub stars, 20 forks, and 11 open issues, last pushed Jun 7, 2023. [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 [aqueduct's repository](https://github.com/RunLLM/aqueduct) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [aqueduct](/tools/runllm-aqueduct.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | Orchestrate LLM and ML workloads on any cloud infrastructure using Go. | An awesome & curated list of best LLMOps tools for developers | | Stars | 517 | 5,915 | | Forks | 20 | 993 | | Open issues | 11 | 247 | | Language | Go | Shell | | Adopt for | Aqueduct is a deprecated Go-based tool for orchestrating LLM and ML workloads across various cloud infrastructures with Kubernetes support. | 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 | 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._ | | [aqueduct](/tools/runllm-aqueduct.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1152d | 91d | | Open issues (now) | 11 | 247 | | Stars delta | Unknown | +28 (30d) | | Open issues delta | Unknown | +66 (30d) | | Full report | [trust report](/tools/runllm-aqueduct/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: aqueduct - **Adopt for:** Aqueduct is a deprecated Go-based tool for orchestrating LLM and ML workloads across various cloud infrastructures with Kubernetes support. ## 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 aqueduct if… - aqueduct is primarily Go; Awesome-LLMOps is Shell. - License: aqueduct is Apache-2.0, Awesome-LLMOps is CC0-1.0. - Tags unique to aqueduct: ai, data, data-science, kubernetes. - When you need to deploy legacy workflows that depend on Aqueduct's specific implementation of custom ops for resource allocation and training. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; aqueduct is Go. - License: Awesome-LLMOps is CC0-1.0, aqueduct is Apache-2.0. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ## When NOT to use aqueduct - Avoid if active project maintenance or community support is required as Aqueduct is no longer maintained. - Skip this tool for new projects focusing on state-of-the-art ML orchestration, opting instead for actively supported 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 aqueduct and Awesome-LLMOps? aqueduct: Orchestrate LLM and ML workloads on any cloud infrastructure using Go.. 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 aqueduct over Awesome-LLMOps? Choose aqueduct over Awesome-LLMOps when aqueduct is primarily Go; Awesome-LLMOps is Shell; License: aqueduct is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to aqueduct: ai, data, data-science, kubernetes; When you need to deploy legacy workflows that depend on Aqueduct's specific implementation of custom ops for resource allocation and training. ### When should I choose Awesome-LLMOps over aqueduct? Choose Awesome-LLMOps over aqueduct when Awesome-LLMOps is primarily Shell; aqueduct is Go; License: Awesome-LLMOps is CC0-1.0, aqueduct is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### When should I avoid aqueduct? Avoid if active project maintenance or community support is required as Aqueduct is no longer maintained. Skip this tool for new projects focusing on state-of-the-art ML orchestration, opting instead for actively supported 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 aqueduct or Awesome-LLMOps more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 517). Stars measure visibility, not whether either tool fits your constraints. ### Are aqueduct and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (aqueduct: Apache-2.0, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to aqueduct or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [aqueduct alternatives](/tools/runllm-aqueduct/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([aqueduct markdown twin](/tools/runllm-aqueduct/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/runllm-aqueduct-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, aqueduct or Awesome-LLMOps? aqueduct: 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 aqueduct and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aqueduct trust report](/tools/runllm-aqueduct/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=runllm-aqueduct`](/api/graphcanon/graph?tool=runllm-aqueduct) - 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/_