Comparison
aqueduct vs Awesome-LLMOps
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.
Markdown twin · aqueduct alternatives · Awesome-LLMOps alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | aqueduct | Awesome-LLMOps |
|---|---|---|
| Maintenance | Dormant (1152d since push) As of 2w · github_public_v1 | Slowing (91d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 4d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- aqueduct
- Orchestrate LLM and ML workloads on any cloud infrastructure using Go.
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- aqueduct
- 517
- Awesome-LLMOps
- 5.9k
Forks
- aqueduct
- 20
- Awesome-LLMOps
- 993
Open issues
- aqueduct
- 11
- Awesome-LLMOps
- 247
Language
- aqueduct
- Go
- Awesome-LLMOps
- Shell
Adopt for
- aqueduct
- Aqueduct is a deprecated Go-based tool for orchestrating LLM and ML workloads across various cloud infrastructures with Kubernetes support.
- Awesome-LLMOps
- 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
- aqueduct
- -
- Awesome-LLMOps
- -
Runtime
- aqueduct
- -
- Awesome-LLMOps
- -
License
- aqueduct
- Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- aqueduct
- Jun 7, 2023
- Awesome-LLMOps
- May 21, 2026
Categories
- aqueduct
- Inference & Serving, LLM Frameworks, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- aqueduct
- Dormant (18%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- aqueduct
- 1152d
- Awesome-LLMOps
- 91d
Open issues (now)
- aqueduct
- 11
- Awesome-LLMOps
- 247
Stars delta
- aqueduct
- Unknown
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- aqueduct
- Unknown
- Awesome-LLMOps
- +66 (30d)
Full report
- aqueduct
- Trust report
- Awesome-LLMOps
- Trust report
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (RunLLM/aqueduct) · observed Aug 3, 2026
- GitHub forks (RunLLM/aqueduct) · observed Aug 3, 2026
- Last push (RunLLM/aqueduct) · observed Jun 7, 2023
- License file (Apache-2.0) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Aug 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: aqueduct 517 · Awesome-LLMOps 5.9k (synced Aug 3, 2026).
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 and Awesome-LLMOps alternatives (aqueduct markdown twin, Awesome-LLMOps markdown twin), 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 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; Awesome-LLMOps trust report.