Comparison
auto-sklearn vs katib
Verdict
Pick auto-sklearn if auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows; pick katib if katib is an automated machine learning solution for Kubernetes environments, focused on hyperparameter tuning and neural architecture search.
Markdown twin · auto-sklearn alternatives · katib alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | auto-sklearn | katib |
|---|---|---|
| Maintenance | Steady (35d since push) As of 3w · github_public_v1 | Very active (0d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | Published findings 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
- auto-sklearn
- Automated Machine Learning with scikit-learn
- katib
- Automated Machine Learning on Kubernetes
Stars
- auto-sklearn
- 8.1k
- katib
- 1.7k
Forks
- auto-sklearn
- 1.3k
- katib
- 534
Open issues
- auto-sklearn
- 209
- katib
- 106
Language
- auto-sklearn
- Python
- katib
- Python
Adopt for
- auto-sklearn
- auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows.
- katib
- Katib is an automated machine learning solution for Kubernetes environments, focused on hyperparameter tuning and neural architecture search.
Persona
- auto-sklearn
- -
- katib
- -
Runtime
- auto-sklearn
- -
- katib
- -
License
- auto-sklearn
- BSD-3-Clause
- katib
- Apache-2.0
Last pushed
- auto-sklearn
- Jun 29, 2026
- katib
- Aug 4, 2026
Categories
- auto-sklearn
- Model Training
- katib
- Evaluation & Observability, Model Training
Trust and health
Maintenance
- auto-sklearn
- Steady (60%)
- katib
- Very active (96%)
Days since push
- auto-sklearn
- 35d
- katib
- 0d
Open issues (now)
- auto-sklearn
- 209
- katib
- 106
Full report
- auto-sklearn
- Trust report
- katib
- Trust report
Shared compatibility
- Python · auto-sklearn: Python runtime · katib: Python runtime
Choose auto-sklearn if…
- License: auto-sklearn is BSD-3-Clause, katib is Apache-2.0.
- Tags unique to auto-sklearn: automated-machine-learning, bayesian-optimization, hyperparameter-optimization, hyperparameter-search.
- auto-sklearn ships Docker support for self-hosted deployment.
- When you need a drop-in replacement estimator for your existing scikit-learn pipeline that can handle the complexity of hyperparameter tuning automatically.
When NOT to use auto-sklearn
- If extensive customization or control over individual machine learning components is required beyond what auto-sklearn's automation offers.
- In cases requiring non-scikit-learn model ensembles, as the toolkit primarily supports models that are part of the scikit-earn library.
Choose katib if…
- License: katib is Apache-2.0, auto-sklearn is BSD-3-Clause.
- Tags unique to katib: ai, neural-architecture-search.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (automl/auto-sklearn) · observed Aug 4, 2026
- GitHub forks (automl/auto-sklearn) · observed Aug 4, 2026
- Last push (automl/auto-sklearn) · observed Jun 29, 2026
- License file (BSD-3-Clause) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: auto-sklearn 8.1k · katib 1.7k (synced Aug 4, 2026).
Common questions
- What is the difference between auto-sklearn and katib?
- auto-sklearn: Automated Machine Learning with scikit-learn. katib: Automated Machine Learning on Kubernetes. See the comparison table for live GitHub stats and shared categories.
- When should I choose auto-sklearn over katib?
- Choose auto-sklearn over katib when License: auto-sklearn is BSD-3-Clause, katib is Apache-2.0; Tags unique to auto-sklearn: automated-machine-learning, bayesian-optimization, hyperparameter-optimization, hyperparameter-search; auto-sklearn ships Docker support for self-hosted deployment; When you need a drop-in replacement estimator for your existing scikit-learn pipeline that can handle the complexity of hyperparameter tuning automatically.
- When should I choose katib over auto-sklearn?
- Choose katib over auto-sklearn when License: katib is Apache-2.0, auto-sklearn is BSD-3-Clause; Tags unique to katib: ai, neural-architecture-search; Also covers Evaluation & Observability; When you need to perform comprehensive hyperparameter tuning tasks within a Kubernetes cluster setup.
- When should I avoid auto-sklearn?
- If extensive customization or control over individual machine learning components is required beyond what auto-sklearn's automation offers. In cases requiring non-scikit-learn model ensembles, as the toolkit primarily supports models that are part of the scikit-earn library.
- 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.
- Is auto-sklearn or katib more popular on GitHub?
- auto-sklearn has more GitHub stars (8,127 vs 1,694). Stars measure visibility, not whether either tool fits your constraints.
- Are auto-sklearn and katib open source?
- Yes - both are open-source projects on GitHub (auto-sklearn: BSD-3-Clause, katib: Apache-2.0).
- Where can I find alternatives to auto-sklearn or katib?
- GraphCanon lists graph-backed alternatives at auto-sklearn alternatives and katib alternatives (auto-sklearn markdown twin, katib 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, auto-sklearn or katib?
- auto-sklearn: Steady. katib: Very active. 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 auto-sklearn and katib?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: auto-sklearn trust report; katib trust report.