Comparison
uncloud vs Awesome-LLMOps
Verdict
Pick uncloud if uncloud is a Go-based deployment tool that aims to bridge the functionality gap between Docker and Kubernetes with an emphasis on simplicity; 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 · uncloud alternatives · Awesome-LLMOps alternatives
GraphCanon updated Sep 18, 2026
6views this month
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Trust & integrity
| Signal | uncloud | Awesome-LLMOps |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 | Slowing (91d since push) As of Aug 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Aug 20, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 15, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- uncloud
- A lightweight tool for deploying and managing containerised applications across a network of Docker hosts.
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- uncloud
- 5.5k
- Awesome-LLMOps
- 5.9k
Forks
- uncloud
- 179
- Awesome-LLMOps
- 993
Open issues
- uncloud
- 89
- Awesome-LLMOps
- 247
Language
- uncloud
- Go
- Awesome-LLMOps
- Shell
Adopt for
- uncloud
- Uncloud is a Go-based deployment tool that aims to bridge the functionality gap between Docker and Kubernetes with an emphasis on simplicity.
- 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
- uncloud
- -
- Awesome-LLMOps
- -
Runtime
- uncloud
- -
- Awesome-LLMOps
- -
License
- uncloud
- Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- uncloud
- Sep 17, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- uncloud
- Inference & Serving
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- uncloud
- Very active (96%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- uncloud
- 0d
- Awesome-LLMOps
- 91d
Open issues (now)
- uncloud
- 89
- Awesome-LLMOps
- 247
Stars delta
- uncloud
- +52 (30d)
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- uncloud
- +3 (30d)
- Awesome-LLMOps
- +66 (30d)
Owner type
- uncloud
- User
- Awesome-LLMOps
- Organization
OSV dependency advisories
- uncloud
- Published findings
- Awesome-LLMOps
- No lockfile (source not queried)
Full report
- uncloud
- Trust report
- Awesome-LLMOps
- Trust report
Choose uncloud if…
- uncloud is primarily Go; Awesome-LLMOps is Shell.
- License: uncloud is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Tags unique to uncloud: containers, deployment, devops, docker.
- uncloud ships Docker support for self-hosted deployment.
- When you need a lightweight solution for deploying containerized apps across multiple Docker hosts without the complexity of full Kubernetes.
When NOT to use uncloud
- For environments that demand advanced load balancing and auto-scaling features typically found in full-fledged orchestrators like Kubernetes.
- In large-scale production setups where the maturity level of a more established tool is preferred to minimize risk.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; uncloud is Go.
- License: Awesome-LLMOps is CC0-1.0, uncloud is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, 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 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 (psviderski/uncloud) · observed Sep 18, 2026
- GitHub forks (psviderski/uncloud) · observed Sep 18, 2026
- Last push (psviderski/uncloud) · observed Sep 17, 2026
- License file (Apache-2.0) · observed Sep 18, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 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: uncloud 5.5k · Awesome-LLMOps 5.9k (synced Sep 18, 2026).
Common questions
- What is the difference between uncloud and Awesome-LLMOps?
- uncloud: A lightweight tool for deploying and managing containerised applications across a network of Docker hosts.. 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 uncloud over Awesome-LLMOps?
- Choose uncloud over Awesome-LLMOps when uncloud is primarily Go; Awesome-LLMOps is Shell; License: uncloud is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to uncloud: containers, deployment, devops, docker; uncloud ships Docker support for self-hosted deployment; When you need a lightweight solution for deploying containerized apps across multiple Docker hosts without the complexity of full Kubernetes.
- When should I choose Awesome-LLMOps over uncloud?
- Choose Awesome-LLMOps over uncloud when Awesome-LLMOps is primarily Shell; uncloud is Go; License: Awesome-LLMOps is CC0-1.0, uncloud is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, 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 uncloud?
- For environments that demand advanced load balancing and auto-scaling features typically found in full-fledged orchestrators like Kubernetes. In large-scale production setups where the maturity level of a more established tool is preferred to minimize risk.
- 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 uncloud or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,915 vs 5,493). Stars measure visibility, not whether either tool fits your constraints.
- Are uncloud and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (uncloud: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to uncloud or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at uncloud alternatives and Awesome-LLMOps alternatives (uncloud 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, uncloud or Awesome-LLMOps?
- uncloud: 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 uncloud and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: uncloud trust report; Awesome-LLMOps trust report.