--- title: "auto-sklearn vs katib" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-auto-sklearn-vs-kubeflow-katib" tools: ["automl-auto-sklearn", "kubeflow-katib"] --- # auto-sklearn vs katib *GraphCanon updated Aug 4, 2026* ## 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. [auto-sklearn](https://automl.github.io/auto-sklearn) reports 8.1k GitHub stars, 1.3k forks, and 209 open issues, last pushed Jun 29, 2026. [katib](https://www.kubeflow.org/docs/components/katib) has 1.7k stars, 534 forks, and 106 open issues, last pushed Aug 4, 2026. Figures are from public GitHub metadata via [auto-sklearn's repository](https://github.com/automl/auto-sklearn) and [katib's repository](https://github.com/kubeflow/katib). | | [auto-sklearn](/tools/automl-auto-sklearn.md) | [katib](/tools/kubeflow-katib.md) | | --- | --- | --- | | Tagline | Automated Machine Learning with scikit-learn | Automated Machine Learning on Kubernetes | | Stars | 8,127 | 1,694 | | Forks | 1,327 | 534 | | Open issues | 209 | 106 | | Language | Python | Python | | Adopt for | auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows. | Katib is an automated machine learning solution for Kubernetes environments, focused on hyperparameter tuning and neural architecture search. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | Apache-2.0 | | Categories | Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [auto-sklearn](/tools/automl-auto-sklearn.md) | [katib](/tools/kubeflow-katib.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 35d | 0d | | Open issues (now) | 209 | 106 | | Full report | [trust report](/tools/automl-auto-sklearn/trust.md) | [trust report](/tools/kubeflow-katib/trust.md) | ## Shared compatibility - **Python**: [auto-sklearn](/tools/automl-auto-sklearn.md) - Python runtime; [katib](/tools/kubeflow-katib.md) - Python runtime ## Decision facts: auto-sklearn - **Adopt for:** auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows. ## Decision facts: katib - **Adopt for:** Katib is an automated machine learning solution for Kubernetes environments, focused on hyperparameter tuning and neural architecture search. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/automl-auto-sklearn/alternatives) and [katib alternatives](/tools/kubeflow-katib/alternatives) ([auto-sklearn markdown twin](/tools/automl-auto-sklearn/alternatives.md), [katib markdown twin](/tools/kubeflow-katib/alternatives.md)), 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](/compare/automl-auto-sklearn-vs-kubeflow-katib.md) 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](/tools/automl-auto-sklearn/trust); [katib trust report](/tools/kubeflow-katib/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automl-auto-sklearn`](/api/graphcanon/graph?tool=automl-auto-sklearn) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_