Last updated 06-28-2026
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Kubeflow
Kubeflow is the foundation of tools for building AI platforms on Kubernetes. AI platform teams can deploy individual subprojects or the full Kubeflow Community Distribution to run machine learning and generative AI workloads wherever Kubernetes runs. It is an open source project under the Cloud Native Computing Foundation with 33,000+ GitHub stars and 3,000 contributors.
The platform is composable and modular: each subproject covers a distinct stage of the AI lifecycle, from data preparation through model serving. Kubeflow Pipelines orchestrates portable ML workflows, Kubeflow Trainer handles distributed training and LLM fine-tuning across PyTorch, DeepSpeed, JAX, and other frameworks, and Katib automates hyperparameter tuning and neural architecture search.
Platform engineers, ML engineers, and data science teams use Kubeflow to standardize how models move from experimentation to production on Kubernetes clusters. Adopters include AWS, Oracle, and Red Hat, and the project integrates with ecosystem tools like KServe for inference, Feast for feature stores, and the Spark Operator for large-scale data processing.
Kubeflow Pipelines builds portable ML workflows that run on any Kubernetes cluster
Trainer fine-tunes LLMs with PyTorch, DeepSpeed, MLX, and Megatron at distributed scale
Katib runs hyperparameter tuning, early stopping, and neural architecture search jobs
Notebooks spin up Jupyter and VS Code environments for interactive ML development
Hub indexes model versions, artifacts, and metadata in one registry
Spark Operator runs distributed Spark jobs for large-scale data prep and embedding
Central Dashboard connects authenticated UIs for every Kubeflow component in one hub
Modular subprojects let teams adopt only the components they need on existing Kubernetes infrastructure.
Backed by 3,000+ contributors and adopted by AWS, Oracle, and Red Hat for production ML platforms.
Kubeflow SDK provides Python APIs to run training workloads without deep Kubernetes expertise.
Covers the full AI lifecycle from data prep through training, tuning, registry, and serving.
CNCF project with active Slack channels, mailing lists, and weekly community calls.
Requires operational Kubernetes expertise to install and maintain in production.
No managed SaaS offering from the project itself; teams self-host on their own clusters.
The breadth of subprojects can make initial setup and component selection overwhelming for newcomers.
Is Kubeflow free to use?
Yes. Kubeflow is an open source project under the Cloud Native Computing Foundation. You can deploy subprojects individually or the full Kubeflow Community Distribution on any Kubernetes cluster without licensing fees.
What Kubernetes platforms support Kubeflow?
Kubeflow deploys anywhere Kubernetes runs. The installation docs cover local clusters, cloud providers, and the Kubeflow Community Distribution releases (versions 1.0 through 26.03).
Can Kubeflow fine-tune large language models?
Yes. Kubeflow Trainer supports LLM fine-tuning with PyTorch, DeepSpeed, MLX, and BuiltinTrainers blueprints. The GenAI docs cover supervised fine-tuning, DPO, PPO, GRPO, and quantization-aware training workflows.
What are the main Kubeflow subprojects?
Core subprojects include Kubeflow Pipelines for workflow orchestration, Trainer for distributed training, Katib for AutoML, Notebooks for interactive development, Hub for model registry, Spark Operator for data processing, and the Central Dashboard as a unified UI hub.
How do I get support for Kubeflow?
Kubeflow offers community support through CNCF Slack channels, a kubeflow-discuss Google Group mailing list, weekly community calls, and documentation at kubeflow.org/docs/started/support/.
Does Kubeflow support GenAI use cases like RAG?
Yes. Kubeflow documents GenAI workflows including retrieval-augmented generation, synthetic data generation, LLM fine-tuning, hyperparameter optimization, and inference at scale using Pipelines, Trainer, Katib, and KServe.