Comparison
katib vs awesome-AutoML
Verdict
Pick katib if katib is an automated machine learning solution for Kubernetes environments, focused on hyperparameter tuning and neural architecture search; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · katib alternatives · awesome-AutoML alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | katib | awesome-AutoML |
|---|---|---|
| Maintenance | Very active (0d since push) As of 3w · github_public_v1 | Slowing (133d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Personal account As of 3w · 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
- katib
- Automated Machine Learning on Kubernetes
- awesome-AutoML
- Curating AutoML research and resources
Stars
- katib
- 1.7k
- awesome-AutoML
- 941
Forks
- katib
- 534
- awesome-AutoML
- 156
Open issues
- katib
- 106
- awesome-AutoML
- 1
Language
- katib
- Python
- awesome-AutoML
- -
Adopt for
- katib
- Katib is an automated machine learning solution for Kubernetes environments, focused on hyperparameter tuning and neural architecture search.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- katib
- -
- awesome-AutoML
- -
Runtime
- katib
- -
- awesome-AutoML
- -
License
- katib
- Apache-2.0
- awesome-AutoML
- GPL-3.0
Last pushed
- katib
- Aug 4, 2026
- awesome-AutoML
- Mar 24, 2026
Categories
- katib
- Evaluation & Observability, Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- katib
- Very active (96%)
- awesome-AutoML
- Slowing (36%)
Days since push
- katib
- 0d
- awesome-AutoML
- 133d
Open issues (now)
- katib
- 106
- awesome-AutoML
- 1
Owner type
- katib
- Organization
- awesome-AutoML
- User
OSV dependency advisories
- katib
- Published findings
- awesome-AutoML
- No lockfile (source not queried)
Full report
- katib
- Trust report
- awesome-AutoML
- Trust report
Choose katib if…
- License: katib is Apache-2.0, awesome-AutoML is GPL-3.0.
- Tags unique to katib: ai, hyperparameter-tuning.
- Also covers Evaluation & Observability.
- When you need to perform comprehensive hyperparameter tuning tasks within a Kubernetes cluster setup.
When NOT to use katib
- Avoid using Katib if you do not have a Kubernetes cluster setup, as it heavily relies on this platform for operation.
- If your project's requirements do not extend beyond simple model training tasks and you lack the resources to support a complex CI/CD pipeline like Kubeflow with Katib.
- Not suitable when working in environments with strict constraints preventing the use of open-source tools under Apache-2.0 licenses.
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, katib is Apache-2.0.
- Tags unique to awesome-AutoML: hyperparameter-optimization, meta-learning.
- When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
When NOT to use awesome-AutoML
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides.
- When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (kubeflow/katib) · observed Aug 4, 2026
- GitHub forks (kubeflow/katib) · observed Aug 4, 2026
- Last push (kubeflow/katib) · observed Aug 4, 2026
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (windmaple/awesome-AutoML) · observed Aug 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Aug 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: katib 1.7k · awesome-AutoML 941 (synced Aug 4, 2026).
Common questions
- What is the difference between katib and awesome-AutoML?
- katib: Automated Machine Learning on Kubernetes. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose katib over awesome-AutoML?
- Choose katib over awesome-AutoML when License: katib is Apache-2.0, awesome-AutoML is GPL-3.0; Tags unique to katib: ai, hyperparameter-tuning; Also covers Evaluation & Observability; When you need to perform comprehensive hyperparameter tuning tasks within a Kubernetes cluster setup.
- When should I choose awesome-AutoML over katib?
- Choose awesome-AutoML over katib when License: awesome-AutoML is GPL-3.0, katib is Apache-2.0; Tags unique to awesome-AutoML: hyperparameter-optimization, meta-learning; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
- When should I avoid katib?
- Avoid using Katib if you do not have a Kubernetes cluster setup, as it heavily relies on this platform for operation. If your project's requirements do not extend beyond simple model training tasks and you lack the resources to support a complex CI/CD pipeline like Kubeflow with Katib. Not suitable when working in environments with strict constraints preventing the use of open-source tools under Apache-2.0 licenses.
- When should I avoid awesome-AutoML?
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
- Is katib or awesome-AutoML more popular on GitHub?
- katib has more GitHub stars (1,694 vs 941). Stars measure visibility, not whether either tool fits your constraints.
- Are katib and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (katib: Apache-2.0, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to katib or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at katib alternatives and awesome-AutoML alternatives (katib markdown twin, awesome-AutoML 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, katib or awesome-AutoML?
- katib: Very active. awesome-AutoML: 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 katib and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: katib trust report; awesome-AutoML trust report.