MLOps & experiment tracking · Kubeflow project (CNCF)

Kubeflow

Open-source, Kubernetes-native toolkit for orchestrating ML pipelines, notebooks, and training jobs.

Visit Kubeflow Alternatives

Kubeflow is an open-source, Kubernetes-native platform for orchestrating machine learning workloads — it is a pipeline-orchestration and infrastructure tool, not an experiment tracker or a production model-monitoring product. Its components include Kubeflow Pipelines for building and scheduling multi-step ML workflows, notebook servers for interactive development, distributed training operators (for TensorFlow, PyTorch, and others), and KServe for model serving. Because it runs on any Kubernetes cluster, Kubeflow can be deployed on-premises or on a self-managed cluster in any public cloud; there is no vendor-hosted SaaS from the project itself, though several cloud providers ship managed distributions based on it. Kubeflow is governed as a Cloud Native Computing Foundation project, reaching CNCF graduated status in 2026, and is maintained by a broad community of contributors rather than a single company.

At a glance

Vendor Kubeflow project (CNCF)
Pricing model Open source + paid options
Free tier Yes
Deployment Self-hosted
Open source Yes (Apache-2.0)
Best for Platform teams that already run Kubernetes and want to orchestrate ML pipelines on their own infrastructure.

Pricing

Kubeflow is free, open-source software; costs come from the Kubernetes cluster and cloud infrastructure it runs on.

Pricing has not been verified yet — see the vendor's site.

Features

  • Multi-step ML pipeline orchestration
  • Distributed training operators (TensorFlow, PyTorch, etc.)
  • Managed Jupyter notebook servers
  • Hyperparameter tuning (Katib)
  • Model serving via KServe
  • Runs on any Kubernetes cluster
  • Multi-tenancy and access control for shared clusters

Integrations

Profile last reviewed September 21, 2026

Alternatives

Metaflow Open-source Python framework for orchestrating data-science and ML workflows from laptop to production. BentoML Open-source framework for packaging and serving ML models as production inference APIs, with a paid managed cloud option. Seldon Kubernetes-native model deployment and serving platform for putting trained ML models into production.

Kubeflow in the index now

Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. Hyperparameter tuning The process of finding the settings that control how a model learns, such as learning rate or tree depth, rather than what it learns. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems. Workflow orchestration Scheduling, sequencing and monitoring interdependent data tasks so each step runs only after the ones it depends on succeed.

Related guides

How to choose an MLOps & experiment tracking tool "MLOps" covers five different jobs — tracking, monitoring, orchestration, serving, versioning — and most teams need more than one, not the biggest single tool.