--- title: "LLMKube vs dstack" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/defilantech-llmkube-vs-dstackai-dstack" tools: ["defilantech-llmkube", "dstackai-dstack"] --- # LLMKube vs dstack *GraphCanon updated Sep 20, 2026* ## Verdict Pick LLMKube if lLMKube is a Kubernetes operator for deploying and managing self-hosted large language model inference services on various GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. It supports runtim; pick dstack if dstack is a vendor-agnostic orchestration tool for AI workloads, including training, inference, and agentic tasks, across various hardware and environments. [LLMKube](https://llmkube.com) reports 210 GitHub stars, 33 forks, and 98 open issues, last pushed Sep 17, 2026. [dstack](https://dstack.ai/docs) has 2.3k stars, 262 forks, and 64 open issues, last pushed Sep 17, 2026. Figures are from public GitHub metadata via [LLMKube's repository](https://github.com/defilantech/LLMKube) and [dstack's repository](https://github.com/dstackai/dstack). | | [LLMKube](/tools/defilantech-llmkube.md) | [dstack](/tools/dstackai-dstack.md) | | --- | --- | --- | | Tagline | Kubernetes operator for self-hosted LLM inference across heterogeneous GPU fleets | Vendor-agnostic orchestration for training, inference and agentic workloads | | Stars | 210 | 2,253 | | Forks | 33 | 262 | | Open issues | 98 | 64 | | Language | Go | Python | | Adopt for | LLMKube is a Kubernetes operator for deploying and managing self-hosted large language model inference services on various GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. It supports runtim | dstack is a vendor-agnostic orchestration tool for AI workloads, including training, inference, and agentic tasks, across various hardware and environments. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | dstack is licensed under the Mozilla Public License 2.0, which is an open-source license. | | Categories | Inference & Serving | AI Agents, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMKube](/tools/defilantech-llmkube.md) | [dstack](/tools/dstackai-dstack.md) | | --- | --- | --- | | Open issues (now) | 98 | 64 | | Stars delta | +27 (30d) | +61 (30d) | | Open issues delta | +21 (30d) | +3 (30d) | | Full report | [trust report](/tools/defilantech-llmkube/trust.md) | [trust report](/tools/dstackai-dstack/trust.md) | ## Decision facts: LLMKube - **Pricing:** freemium - Free and open-source under the Apache-2.0 license. - **Requirements:** Requires a Kubernetes cluster for installation and operation.; Supports deployment on a variety of GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. - **Adopt for:** LLMKube is a Kubernetes operator for deploying and managing self-hosted large language model inference services on various GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. It supports runtim - **License detail:** Apache-2.0 ## 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. ## Choose when ### Choose LLMKube if… - LLMKube is primarily Go; dstack is Python. - License: LLMKube is Apache-2.0, dstack is MPL-2.0. - Pricing: Free and open-source under the Apache-2.0 license.. - Requirements: Requires a Kubernetes cluster for installation and operation.; Supports deployment on a variety of GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal.. - Tags unique to LLMKube: ai, apple-silicon, autoscaling, edge-computing. - LLMKube ships Docker support for self-hosted deployment. - When you need to deploy and manage self-hosted large language model inference services across a variety of GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. ### Choose dstack if… - dstack is primarily Python; LLMKube is Go. - License: dstack is MPL-2.0, LLMKube 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 NOT to use LLMKube - If your infrastructure does not include a Kubernetes cluster, as LLMKube is designed to operate within a Kubernetes environment. - If you are looking for a solution that does not support a heterogeneous mix of GPU architectures, LLMKube is specifically designed to work with NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. - If you do not require or prefer not to use the specific runtimes supported by LLMKube, such as llama.cpp, vLLM, TGI, and mlx-server. ## 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 ## Common questions ### What is the difference between LLMKube and dstack? LLMKube: Kubernetes operator for self-hosted LLM inference across heterogeneous GPU fleets. dstack: Vendor-agnostic orchestration for training, inference and agentic workloads. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLMKube over dstack? Choose LLMKube over dstack when LLMKube is primarily Go; dstack is Python; License: LLMKube is Apache-2.0, dstack is MPL-2.0; Pricing: Free and open-source under the Apache-2.0 license.; Requirements: Requires a Kubernetes cluster for installation and operation.; Supports deployment on a variety of GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal.; Tags unique to LLMKube: ai, apple-silicon, autoscaling, edge-computing; LLMKube ships Docker support for self-hosted deployment; When you need to deploy and manage self-hosted large language model inference services across a variety of GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. ### When should I choose dstack over LLMKube? Choose dstack over LLMKube when dstack is primarily Python; LLMKube is Go; License: dstack is MPL-2.0, LLMKube 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 avoid LLMKube? If your infrastructure does not include a Kubernetes cluster, as LLMKube is designed to operate within a Kubernetes environment. If you are looking for a solution that does not support a heterogeneous mix of GPU architectures, LLMKube is specifically designed to work with NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. If you do not require or prefer not to use the specific runtimes supported by LLMKube, such as llama.cpp, vLLM, TGI, and mlx-server. ### 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 ### Is LLMKube or dstack more popular on GitHub? dstack has more GitHub stars (2,253 vs 210). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMKube and dstack open source? Yes - both are open-source projects on GitHub (LLMKube: Apache-2.0, dstack: MPL-2.0). ### Where can I find alternatives to LLMKube or dstack? GraphCanon lists graph-backed alternatives at [LLMKube alternatives](/tools/defilantech-llmkube/alternatives) and [dstack alternatives](/tools/dstackai-dstack/alternatives) ([LLMKube markdown twin](/tools/defilantech-llmkube/alternatives.md), [dstack markdown twin](/tools/dstackai-dstack/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/defilantech-llmkube-vs-dstackai-dstack.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLMKube or dstack? LLMKube: Very active. dstack: 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 LLMKube and dstack? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMKube trust report](/tools/defilantech-llmkube/trust); [dstack trust report](/tools/dstackai-dstack/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=defilantech-llmkube`](/api/graphcanon/graph?tool=defilantech-llmkube) - 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/_