Kubeflow

Free tier

The open-source foundation of tools for AI Platforms on Kubernetes

Free·Technical·API available·Open source

Key strengths

CNCF graduated projectKubernetes-native AI/ML platformModular and composable subprojectsLarge community with 3K+ contributorsSupports distributed training, AutoML, pipelines, and model registry258M+ PyPI downloads

Completely free

Founded 2018

Self-hostable

Facts verified against kubeflow.org on Oct 5, 2026

Get alerts when Kubeflow changes

Free. We'll email you when pricing, the free tier, API access or open-source status changes.

No ratings yet

Kubeflow — Technical Overview

Kubeflow is an open-source, Kubernetes-native AI/ML platform and a CNCF graduated project designed for AI practitioners and platform administrators running Data, AI/ML, and HPC workloads. First released in 2018, it has grown to 33,100+ GitHub stars and 258M+ PyPI downloads, backed by a community of 3,000+ contributors.

Kubeflow is architected as a modular, composable set of subprojects, allowing teams to adopt only the components they need. Core capabilities include:

Distributed training — integrates natively with PyTorch, JAX, XGBoost, DeepSpeed, Megatron, and MLX
AutoML — automated hyperparameter tuning and neural architecture search
Pipelines — DAG-based ML workflow orchestration on Kubernetes
Model Registry — versioned model management and lineage tracking
HuggingFace & Apache Spark integration — for data processing and model serving at scale

Because it runs entirely on Kubernetes, Kubeflow is fully self-hostable and portable across any conformant cluster — on-premises, hybrid, or any cloud provider. Its Kubernetes-native design means workloads are expressed as custom resources (CRDs), enabling GitOps-friendly, declarative ML infrastructure.

Alternatives