dstack vs kubeflow

Common questions

What is the difference between dstack and kubeflow?

dstack: Vendor-agnostic orchestration for training, inference and agentic workloads. kubeflow: Machine Learning Toolkit for Kubernetes. See the comparison table for live GitHub stats and shared categories.

When should I choose dstack over kubeflow?

Choose dstack over kubeflow when License: dstack is MPL-2.0, kubeflow is Apache-2.0; Pricing: The core functionality of dstack is free and open-source under the Mozilla Public License 2.0. Additional services or support may be available for a fee.; Requirements: Requires Docker; Requires Git and OpenSSH for CLI installation and configuration.; Tags unique to dstack: agent-skills, agentic-orchestration, amd, cloud; Also covers AI Agents, Inference & Serving; When you need orchestration for AI workloads that can run on a variety of hardware like NVIDIA, AMD, TPU, and Tenstorrent, and across different environments such as clouds, Kubernetes, and bare metal.

When should I choose kubeflow over dstack?

Choose kubeflow over dstack when License: kubeflow is Apache-2.0, dstack is MPL-2.0; Requirements: Requires Docker; Requires familiarity with Kubernetes and its ecosystem.; Primarily licensed under Apache-2.0.; Tags unique to kubeflow: google-kubernetes-engine, jupyter, kubeflow, kubernetes; Also covers Developer Tools; When you are working on a Kubernetes-based platform and aim to streamline the process of deploying, scaling, and managing machine-learning workloads.

When should I avoid dstack?

If you are looking for a tool that is tightly integrated with a specific cloud provider or hardware vendor, as dstack is designed to be vendor-agnostic. When your project strictly requires proprietary licensing, as dstack is licensed under the Mozilla Public License 2.0, which is open source. If your team does not have the capability to install and configure the dstack CLI, which requires Git and OpenSSH, and involves setting up a server and configuring it with a user token and project URL

When should I avoid kubeflow?

If your organization does not use or plan to leverage Kubernetes infrastructure in its operations as Kubeflow tightly integrates with it. When you seek a low-code solution for machine learning or have minimal Kubernetes expertise, as Kubeflow requires advanced Kubernetes skills and management capability.

Is dstack or kubeflow more popular on GitHub?

kubeflow has more GitHub stars (15,881 vs 2,253). Stars measure visibility, not whether either tool fits your constraints.

Are dstack and kubeflow open source?

Yes - both are open-source projects on GitHub (dstack: MPL-2.0, kubeflow: Apache-2.0).

Where can I find alternatives to dstack or kubeflow?

GraphCanon lists graph-backed alternatives at dstack alternatives and kubeflow alternatives (dstack markdown twin, kubeflow markdown twin), ranked by typed relationship edges rather than popularity votes.

Is there a machine-readable version of this comparison?

Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, dstack or kubeflow?

dstack: Very active. kubeflow: Steady. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

Where are the full trust reports for dstack and kubeflow?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: dstack trust report; kubeflow trust report.