--- title: "katib vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kubeflow-katib-vs-windmaple-awesome-automl" tools: ["kubeflow-katib", "windmaple-awesome-automl"] --- # katib vs awesome-AutoML *GraphCanon updated Aug 4, 2026* ## 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. [katib](https://www.kubeflow.org/docs/components/katib) reports 1.7k GitHub stars, 534 forks, and 106 open issues, last pushed Aug 4, 2026. [awesome-AutoML](https://github.com/windmaple/awesome-AutoML) has 941 stars, 156 forks, and 1 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [katib's repository](https://github.com/kubeflow/katib) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [katib](/tools/kubeflow-katib.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | Automated Machine Learning on Kubernetes | Curating AutoML research and resources | | Stars | 1,694 | 941 | | Forks | 534 | 156 | | Open issues | 106 | 1 | | Language | Python | - | | Adopt for | Katib is an automated machine learning solution for Kubernetes environments, focused on hyperparameter tuning and neural architecture search. | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | GPL-3.0 | | Categories | Evaluation & Observability, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [katib](/tools/kubeflow-katib.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 133d | | Open issues (now) | 106 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/kubeflow-katib/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## Decision facts: katib - **Adopt for:** Katib is an automated machine learning solution for Kubernetes environments, focused on hyperparameter tuning and neural architecture search. ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/kubeflow-katib/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([katib markdown twin](/tools/kubeflow-katib/alternatives.md), [awesome-AutoML markdown twin](/tools/windmaple-awesome-automl/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/kubeflow-katib-vs-windmaple-awesome-automl.md) 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](/tools/kubeflow-katib/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kubeflow-katib`](/api/graphcanon/graph?tool=kubeflow-katib) - 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/_