What is Kubeflow used for?

Kubeflow is an open source platform for deploying ML workflows on Kubernetes. It is a good fit for deploying ML workflows on Kubernetes, scalable LLM fine-tuning and AI model training, and automated machine learning with hyperparameter tuning and neural architecture search.

Is Kubeflow open source?

Yes. Kubeflow is open source under the Apache-2.0 licence. Its source code is on GitHub at kubeflow/kubeflow.

Yes. Kubeflow is open source, so the software itself is free to use.

Can I self-host Kubeflow?

Yes. Kubeflow can be self-hosted on your own server or infrastructure.

What is Kubeflow an alternative to?

Kubeflow is an open-source alternative to Together AI, Amazon SageMaker, Google Vertex AI and Arize AI. Other open-source alternatives to Together AI include MLflow, Ray and vLLM.

Is Kubeflow actively maintained?

Yes. The most recent commit to Kubeflow was on 30 September 2026. The project has 16k stars on GitHub.

Kubeflow: open-source alternative to Together AI

About Kubeflow

Kubeflow is the Cloud Native AI platform composed of modular, open source projects that form a Kubernetes-native stack for data and AI workloads. It is designed for AI practitioners, platform administrators, and decision-makers who need modular, scalable, and extensible tools for data, AI/ML, and HPC use-cases.

The platform's mission is to bridge the Data, AI, and Cloud Native ecosystems, enabling teams to deliver more models, agents, and AI applications into production across the AI lifecycle. It is built by an open global community and aims to be the standard for Data & AI workloads on Kubernetes.

Key features

Runs workloads at any scale without requiring Kubernetes expertise
Portable across local laptops, on-premises, and any cloud
Manages hyperscale training jobs and high-throughput AI agents
Composable tools that can be mixed and matched across the AI lifecycle
Includes subprojects: Spark Operator, Notebooks, Trainer, Katib, and Hub

Good fit for

Deploying ML workflows on Kubernetes
Scalable LLM fine-tuning and AI model training
Automated machine learning with hyperparameter tuning and neural architecture search

Built with: Kubernetes tools
Tags: kubernetes; machine learning; ai; open source; cloud native; mlops

Kubeflow: questions and answers

What is Kubeflow used for?: Kubeflow is an open source platform for deploying ML workflows on Kubernetes. It is a good fit for deploying ML workflows on Kubernetes, scalable LLM fine-tuning and AI model training, and automated machine learning with hyperparameter tuning and neural architecture search.
Is Kubeflow open source?: Yes. Kubeflow is open source under the Apache-2.0 licence. Its source code is on GitHub at kubeflow/kubeflow.
Is Kubeflow free?: Yes. Kubeflow is open source, so the software itself is free to use.
Can I self-host Kubeflow?: Yes. Kubeflow can be self-hosted on your own server or infrastructure.
What is Kubeflow an alternative to?: Kubeflow is an open-source alternative to Together AI, Amazon SageMaker, Google Vertex AI and Arize AI. Other open-source alternatives to Together AI include MLflow, Ray and vLLM.
Is Kubeflow actively maintained?: Yes. The most recent commit to Kubeflow was on 30 September 2026. The project has 16k stars on GitHub.