--- title: "dstack vs kubeai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dstackai-dstack-vs-kubeai-project-kubeai" tools: ["dstackai-dstack", "kubeai-project-kubeai"] --- # dstack vs kubeai *GraphCanon updated Sep 20, 2026* ## Verdict Pick dstack if dstack is a vendor-agnostic orchestration tool for AI workloads, including training, inference, and agentic tasks, across various hardware and environments; pick kubeai if kubeAI is an AI inference operator for Kubernetes that simplifies the deployment and scaling of machine learning models, including LLMs, embeddings, and speech-to-text services, with intelligent scaling and optimized for. [dstack](https://dstack.ai/docs) reports 2.3k GitHub stars, 262 forks, and 64 open issues, last pushed Sep 17, 2026. [kubeai](https://www.kubeai.org) has 1.3k stars, 137 forks, and 121 open issues, last pushed Sep 17, 2026. Figures are from public GitHub metadata via [dstack's repository](https://github.com/dstackai/dstack) and [kubeai's repository](https://github.com/kubeai-project/kubeai). | | [dstack](/tools/dstackai-dstack.md) | [kubeai](/tools/kubeai-project-kubeai.md) | | --- | --- | --- | | Tagline | Vendor-agnostic orchestration for training, inference and agentic workloads | AI Inference Operator for Kubernetes | | Stars | 2,253 | 1,265 | | Forks | 262 | 137 | | Open issues | 64 | 121 | | Language | Python | Go | | Adopt for | dstack is a vendor-agnostic orchestration tool for AI workloads, including training, inference, and agentic tasks, across various hardware and environments. | KubeAI is an AI inference operator for Kubernetes that simplifies the deployment and scaling of machine learning models, including LLMs, embeddings, and speech-to-text services, with intelligent scaling and optimized for | | Persona | - | - | | Runtime | - | - | | License | dstack is licensed under the Mozilla Public License 2.0, which is an open-source license. | Apache-2.0 | | Categories | AI Agents, Inference & Serving, Model Training | Inference & Serving, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dstack](/tools/dstackai-dstack.md) | [kubeai](/tools/kubeai-project-kubeai.md) | | --- | --- | --- | | Open issues (now) | 64 | 121 | | Stars delta | +61 (30d) | +28 (30d) | | Open issues delta | +3 (30d) | +9 (30d) | | Full report | [trust report](/tools/dstackai-dstack/trust.md) | [trust report](/tools/kubeai-project-kubeai/trust.md) | ## Decision facts: dstack - **Pricing:** freemium - 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. - **Adopt for:** dstack is a vendor-agnostic orchestration tool for AI workloads, including training, inference, and agentic tasks, across various hardware and environments. - **License detail:** dstack is licensed under the Mozilla Public License 2.0, which is an open-source license. ## Decision facts: kubeai - **Pricing:** freemium - The tool is free and open-source under the Apache-2.0 license, but additional services or support might incur costs - **Requirements:** Min 4 GB RAM; Requires Docker; Requires a Kubernetes cluster; Supports deployment on CPU, GPU, or TPU hardware - **Adopt for:** KubeAI is an AI inference operator for Kubernetes that simplifies the deployment and scaling of machine learning models, including LLMs, embeddings, and speech-to-text services, with intelligent scaling and optimized for - **License detail:** Apache-2.0 ## Choose when ### Choose dstack if… - dstack is primarily Python; kubeai is Go. - License: dstack is MPL-2.0, kubeai 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, Model Training. - 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. ### Choose kubeai if… - kubeai is primarily Go; dstack is Python. - License: kubeai is Apache-2.0, dstack is MPL-2.0. - Pricing: The tool is free and open-source under the Apache-2.0 license, but additional services or support might incur costs. - Requirements: Min 4 GB RAM; Requires Docker; Requires a Kubernetes cluster; Supports deployment on CPU, GPU, or TPU hardware. - Tags unique to kubeai: ai, autoscaler, faster-whisper, inference-operator. - Also covers Speech & Audio. - kubeai ships Docker support for self-hosted deployment. - you need to deploy and scale machine learning models on Kubernetes, especially LLMs, embeddings, and speech-to-text services ## When NOT to use 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 NOT to use kubeai - you are looking for a solution that requires additional systems like Istio or Knative for scale-from-zero - you need a tool that heavily relies on Prometheus metrics adapter for autoscaling - you are working in an environment that does not support Kubernetes - you prefer a more complex setup with multiple dependencies for customization ## Common questions ### What is the difference between dstack and kubeai? dstack: Vendor-agnostic orchestration for training, inference and agentic workloads. kubeai: AI Inference Operator for Kubernetes. See the comparison table for live GitHub stats and shared categories. ### When should I choose dstack over kubeai? Choose dstack over kubeai when dstack is primarily Python; kubeai is Go; License: dstack is MPL-2.0, kubeai 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, Model Training; 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 kubeai over dstack? Choose kubeai over dstack when kubeai is primarily Go; dstack is Python; License: kubeai is Apache-2.0, dstack is MPL-2.0; Pricing: The tool is free and open-source under the Apache-2.0 license, but additional services or support might incur costs; Requirements: Min 4 GB RAM; Requires Docker; Requires a Kubernetes cluster; Supports deployment on CPU, GPU, or TPU hardware; Tags unique to kubeai: ai, autoscaler, faster-whisper, inference-operator; Also covers Speech & Audio; kubeai ships Docker support for self-hosted deployment; you need to deploy and scale machine learning models on Kubernetes, especially LLMs, embeddings, and speech-to-text services. ### 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 kubeai? you are looking for a solution that requires additional systems like Istio or Knative for scale-from-zero you need a tool that heavily relies on Prometheus metrics adapter for autoscaling you are working in an environment that does not support Kubernetes you prefer a more complex setup with multiple dependencies for customization ### Is dstack or kubeai more popular on GitHub? dstack has more GitHub stars (2,253 vs 1,265). Stars measure visibility, not whether either tool fits your constraints. ### Are dstack and kubeai open source? Yes - both are open-source projects on GitHub (dstack: MPL-2.0, kubeai: Apache-2.0). ### Where can I find alternatives to dstack or kubeai? GraphCanon lists graph-backed alternatives at [dstack alternatives](/tools/dstackai-dstack/alternatives) and [kubeai alternatives](/tools/kubeai-project-kubeai/alternatives) ([dstack markdown twin](/tools/dstackai-dstack/alternatives.md), [kubeai markdown twin](/tools/kubeai-project-kubeai/alternatives.md)), 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](/compare/dstackai-dstack-vs-kubeai-project-kubeai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dstack or kubeai? dstack: Very active. kubeai: Very active. 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 kubeai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dstack trust report](/tools/dstackai-dstack/trust); [kubeai trust report](/tools/kubeai-project-kubeai/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dstackai-dstack`](/api/graphcanon/graph?tool=dstackai-dstack) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_