Comparison
kedro-viz vs Awesome-LLMOps
Verdict
Pick kedro-viz if kedro-Viz is tailored for visualizing Kedro projects and tracking experiments within the Kedro framework; 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 · kedro-viz alternatives · Awesome-LLMOps alternatives
GraphCanon updated 5d
vs
Trust & integrity
| Signal | kedro-viz | Awesome-LLMOps |
|---|---|---|
| Maintenance | Very active (1d since push) As of 3w · github_public_v1 | Slowing (91d since push) As of 5d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 5d · 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
- kedro-viz
- Visualise Kedro data pipelines and track experiments.
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- kedro-viz
- 753
- Awesome-LLMOps
- 5.9k
Forks
- kedro-viz
- 125
- Awesome-LLMOps
- 993
Open issues
- kedro-viz
- 73
- Awesome-LLMOps
- 247
Language
- kedro-viz
- JavaScript
- Awesome-LLMOps
- Shell
Adopt for
- kedro-viz
- Kedro-Viz is tailored for visualizing Kedro projects and tracking experiments within the Kedro framework.
- 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
- kedro-viz
- -
- Awesome-LLMOps
- -
Runtime
- kedro-viz
- -
- Awesome-LLMOps
- -
License
- kedro-viz
- Licensed under Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- kedro-viz
- Aug 1, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- kedro-viz
- Data & Retrieval
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- kedro-viz
- Very active (96%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- kedro-viz
- 1d
- Awesome-LLMOps
- 91d
Open issues (now)
- kedro-viz
- 73
- Awesome-LLMOps
- 247
Stars delta
- kedro-viz
- Unknown
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- kedro-viz
- Unknown
- Awesome-LLMOps
- +66 (30d)
Full report
- kedro-viz
- Trust report
- Awesome-LLMOps
- Trust report
Choose kedro-viz if…
- kedro-viz is primarily JavaScript; Awesome-LLMOps is Shell.
- License: kedro-viz is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Tags unique to kedro-viz: data-visualization, experiment tracking, kedro-extension, kedro-plugin.
- When working with existing Kedro projects to visualize pipelines and gain insights into experiment outcomes efficiently.
When NOT to use kedro-viz
- For pipeline visualization and experiment tracking if the project is not built on the Kedro framework, as integration may be cumbersome or unsupported.
- In settings where real-time collaboration features are essential, as its focus leans towards individual or pre-defined team workflows within a specific tech stack.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; kedro-viz is JavaScript.
- License: Awesome-LLMOps is CC0-1.0, kedro-viz is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Evaluation & Observability, Inference & Serving, 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 (kedro-org/kedro-viz) · observed Aug 3, 2026
- GitHub forks (kedro-org/kedro-viz) · observed Aug 3, 2026
- Last push (kedro-org/kedro-viz) · observed Aug 1, 2026
- License file (Apache-2.0) · observed Aug 3, 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: kedro-viz 753 · Awesome-LLMOps 5.9k (synced Aug 3, 2026).
Common questions
- What is the difference between kedro-viz and Awesome-LLMOps?
- kedro-viz: Visualise Kedro data pipelines and track experiments.. 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 kedro-viz over Awesome-LLMOps?
- Choose kedro-viz over Awesome-LLMOps when kedro-viz is primarily JavaScript; Awesome-LLMOps is Shell; License: kedro-viz is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to kedro-viz: data-visualization, experiment tracking, kedro-extension, kedro-plugin; When working with existing Kedro projects to visualize pipelines and gain insights into experiment outcomes efficiently.
- When should I choose Awesome-LLMOps over kedro-viz?
- Choose Awesome-LLMOps over kedro-viz when Awesome-LLMOps is primarily Shell; kedro-viz is JavaScript; License: Awesome-LLMOps is CC0-1.0, kedro-viz is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Evaluation & Observability, Inference & Serving, 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 kedro-viz?
- For pipeline visualization and experiment tracking if the project is not built on the Kedro framework, as integration may be cumbersome or unsupported. In settings where real-time collaboration features are essential, as its focus leans towards individual or pre-defined team workflows within a specific tech stack.
- 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 kedro-viz or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,915 vs 753). Stars measure visibility, not whether either tool fits your constraints.
- Are kedro-viz and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (kedro-viz: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to kedro-viz or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at kedro-viz alternatives and Awesome-LLMOps alternatives (kedro-viz 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, kedro-viz or Awesome-LLMOps?
- kedro-viz: 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 kedro-viz and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: kedro-viz trust report; Awesome-LLMOps trust report.