Comparison
onepanel vs Awesome-LLMOps
Verdict
Pick onepanel if onepanel is an open source tool designed for computer vision projects with capabilities spanning from data labeling to model tuning and deployment, all supported by a Go-based codebase under the Apache-2.0 license; 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.
Markdown twin · onepanel alternatives · Awesome-LLMOps alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | onepanel | Awesome-LLMOps |
|---|---|---|
| Maintenance | Dormant (1252d since push) As of 3w · github_public_v1 | Slowing (91d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 4d · github_public_v1 |
| OSV dependency advisories | Published findings 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
- onepanel
- The open source, end-to-end computer vision platform.
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- onepanel
- 730
- Awesome-LLMOps
- 5.9k
Forks
- onepanel
- 73
- Awesome-LLMOps
- 993
Open issues
- onepanel
- 102
- Awesome-LLMOps
- 247
Language
- onepanel
- Go
- Awesome-LLMOps
- Shell
Adopt for
- onepanel
- Onepanel is an open source tool designed for computer vision projects with capabilities spanning from data labeling to model tuning and deployment, all supported by a Go-based codebase under the Apache-2.0 license.
- 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
- onepanel
- -
- Awesome-LLMOps
- -
Runtime
- onepanel
- -
- Awesome-LLMOps
- -
License
- onepanel
- Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- onepanel
- Feb 25, 2023
- Awesome-LLMOps
- May 21, 2026
Categories
- onepanel
- Computer Vision, Inference & Serving, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- onepanel
- Dormant (18%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- onepanel
- 1252d
- Awesome-LLMOps
- 91d
Open issues (now)
- onepanel
- 102
- Awesome-LLMOps
- 247
Stars delta
- onepanel
- Unknown
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- onepanel
- Unknown
- Awesome-LLMOps
- +66 (30d)
OSV dependency advisories
- onepanel
- Published findings
- Awesome-LLMOps
- No lockfile (source not queried)
Full report
- onepanel
- Trust report
- Awesome-LLMOps
- Trust report
Choose onepanel if…
- onepanel is primarily Go; Awesome-LLMOps is Shell.
- License: onepanel is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Tags unique to onepanel: aiops, annotation, deeplearning, hyperparameter-tuning.
- onepanel ships Docker support for self-hosted deployment.
- When you need an end-to-end platform that supports multiple aspects of computer vision including advanced functionalities such as hyperparameter tuning and automated workflows.
When NOT to use onepanel
- Avoid using Onepanel for projects that require extensive Java-based development because it is written in Go.
- Not suitable if you seek a platform focusing solely on model serving or inference without capabilities to go back upstream into data labeling and preprocessing stages.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; onepanel is Go.
- License: Awesome-LLMOps is CC0-1.0, onepanel is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops.
- Also covers Data & Retrieval, Evaluation & Observability, LLM Frameworks, 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 (onepanelio/onepanel) · observed Jul 31, 2026
- GitHub forks (onepanelio/onepanel) · observed Jul 31, 2026
- Last push (onepanelio/onepanel) · observed Feb 25, 2023
- License file (Apache-2.0) · observed Jul 31, 2026
- Decision facts (enrichment) · observed Jul 16, 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: onepanel 730 · Awesome-LLMOps 5.9k (synced Jul 31, 2026).
Common questions
- What is the difference between onepanel and Awesome-LLMOps?
- onepanel: The open source, end-to-end computer vision platform.. 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 onepanel over Awesome-LLMOps?
- Choose onepanel over Awesome-LLMOps when onepanel is primarily Go; Awesome-LLMOps is Shell; License: onepanel is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to onepanel: aiops, annotation, deeplearning, hyperparameter-tuning; onepanel ships Docker support for self-hosted deployment; When you need an end-to-end platform that supports multiple aspects of computer vision including advanced functionalities such as hyperparameter tuning and automated workflows.
- When should I choose Awesome-LLMOps over onepanel?
- Choose Awesome-LLMOps over onepanel when Awesome-LLMOps is primarily Shell; onepanel is Go; License: Awesome-LLMOps is CC0-1.0, onepanel is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops; Also covers Data & Retrieval, Evaluation & Observability, LLM Frameworks, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- When should I avoid onepanel?
- Avoid using Onepanel for projects that require extensive Java-based development because it is written in Go. Not suitable if you seek a platform focusing solely on model serving or inference without capabilities to go back upstream into data labeling and preprocessing stages.
- 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 onepanel or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,915 vs 730). Stars measure visibility, not whether either tool fits your constraints.
- Are onepanel and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (onepanel: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to onepanel or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at onepanel alternatives and Awesome-LLMOps alternatives (onepanel 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, onepanel or Awesome-LLMOps?
- onepanel: 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 onepanel and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: onepanel trust report; Awesome-LLMOps trust report.