Understand the project
Practical overview
When it fits
Use Kubeflow when Kubernetes is the target infrastructure for AI/ML operations and the team needs modular tools that can span training, deployment and related lifecycle stages.
Who it is for
- AI practitioners managing machine-learning workloads
- Platform administrators building Kubernetes-based AI infrastructure
- Decision-makers evaluating portable and composable AI platforms
Documented uses
- Run data and AI workloads on Kubernetes
- Support model training and production delivery
- Manage distributed AI workloads and high-throughput AI agents
- Assemble modular tools across the AI lifecycle
- Provide a portable platform spanning local, on-premises and cloud environments
Before you choose
Requirements & limits
The project is publicly available as open-source software under the Apache-2.0 license.
- Kubernetes is the platform foundation.
- The architecture is modular, with development distributed across individual subproject repositories.
- The project targets data, AI/ML and HPC workloads rather than a single end-user application.
- The documented portability goal covers local, on-premises and cloud environments.
- Installation and cluster-compatibility details are not established by the supplied sources.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Evidence and transparency
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 2 October 2026
- Original website title
- kubeflow/kubeflow · Official homepage title · retained in its source language