--- title: "LLMKube vs anubis-oss" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/defilantech-llmkube-vs-uncsoft-anubis-oss" tools: ["defilantech-llmkube", "uncsoft-anubis-oss"] --- # LLMKube vs anubis-oss *GraphCanon updated Aug 13, 2026* ## Verdict Pick LLMKube if lLMKube is a Kubernetes operator designed for deploying and scaling Language Model (LM) inference across different GPU types, supporting multiple runtimes; pick anubis-oss if anubis-oss, specifically tailored for Apple Silicon devices using Swift, is distinguished by its focus on local large language model evaluation and testing within the macOS environment. [LLMKube](https://llmkube.com) reports 183 GitHub stars, 27 forks, and 77 open issues, last pushed Aug 1, 2026. [anubis-oss](https://devpadapp.com/leaderboard.html) has 198 stars, 12 forks, and 4 open issues, last pushed Jun 18, 2026. Figures are from public GitHub metadata via [LLMKube's repository](https://github.com/defilantech/LLMKube) and [anubis-oss's repository](https://github.com/uncSoft/anubis-oss). | | [LLMKube](/tools/defilantech-llmkube.md) | [anubis-oss](/tools/uncsoft-anubis-oss.md) | | --- | --- | --- | | Tagline | Kubernetes operator for self-hosted LLM inference | Local LLM Testing & Benchmarking for Apple Silicon | | Stars | 183 | 198 | | Forks | 27 | 12 | | Open issues | 77 | 4 | | Language | Go | Swift | | Adopt for | LLMKube is a Kubernetes operator designed for deploying and scaling Language Model (LM) inference across different GPU types, supporting multiple runtimes. | Anubis-oss, specifically tailored for Apple Silicon devices using Swift, is distinguished by its focus on local large language model evaluation and testing within the macOS environment. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | GPL-3.0 license ensures that any derivative works related to anubis-oss must also be open source under the same licensing terms. | | Categories | Inference & Serving | Evaluation & Observability, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMKube](/tools/defilantech-llmkube.md) | [anubis-oss](/tools/uncsoft-anubis-oss.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 56d | | Open issues (now) | 77 | 4 | | Owner type | Organization | User | | Full report | [trust report](/tools/defilantech-llmkube/trust.md) | [trust report](/tools/uncsoft-anubis-oss/trust.md) | ## Decision facts: LLMKube - **Adopt for:** LLMKube is a Kubernetes operator designed for deploying and scaling Language Model (LM) inference across different GPU types, supporting multiple runtimes. ## Decision facts: anubis-oss - **Pricing:** freemium - The tool is free and open-source with no monetary costs for usage or distribution. - **Requirements:** Min 8 GB RAM - **Adopt for:** Anubis-oss, specifically tailored for Apple Silicon devices using Swift, is distinguished by its focus on local large language model evaluation and testing within the macOS environment. - **License detail:** GPL-3.0 license ensures that any derivative works related to anubis-oss must also be open source under the same licensing terms. ## Choose when ### Choose LLMKube if… - LLMKube is primarily Go; anubis-oss is Swift. - License: LLMKube is Apache-2.0, anubis-oss is GPL-3.0. - Tags unique to LLMKube: ai, autoscaling, edge-computing, gguf. - LLMKube ships Docker support for self-hosted deployment. - Use LLMKube if you need to run self-hosted Language Model inference with support for various GPU types like NVIDIA CUDA, AMD Vulkan, or Apple Silicon Metal. ### Choose anubis-oss if… - anubis-oss is primarily Swift; LLMKube is Go. - License: anubis-oss is GPL-3.0, LLMKube is Apache-2.0. - Pricing: The tool is free and open-source with no monetary costs for usage or distribution.. - Requirements: Min 8 GB RAM. - Tags unique to anubis-oss: benchmarking, llm, local-llm, macos. - Also covers Evaluation & Observability. - When developing and evaluating large language models intended to run natively on Apple Silicon hardware. ## When NOT to use LLMKube - Avoid LLMKube if your deployment environment strictly limits the use of Kubernetes or does not support the specified GPU types - NVIDIA CUDA, AMD Vulkan, Apple Silicon Metal. - Not recommended for users who require a solution that only supports specific models or runtimes which are not covered by the runtime options provided (llama.cpp, vLLM, TGI, mlx-server). ## When NOT to use anubis-oss - If your development does not involve Apple Silicon or macOS environments as Anubis-oss is tightly integrated with these platforms. - When preferring a language other than Swift, since Anubis-oss depends on this for its operations. ## Common questions ### What is the difference between LLMKube and anubis-oss? LLMKube: Kubernetes operator for self-hosted LLM inference. anubis-oss: Local LLM Testing & Benchmarking for Apple Silicon. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLMKube over anubis-oss? Choose LLMKube over anubis-oss when LLMKube is primarily Go; anubis-oss is Swift; License: LLMKube is Apache-2.0, anubis-oss is GPL-3.0; Tags unique to LLMKube: ai, autoscaling, edge-computing, gguf; LLMKube ships Docker support for self-hosted deployment; Use LLMKube if you need to run self-hosted Language Model inference with support for various GPU types like NVIDIA CUDA, AMD Vulkan, or Apple Silicon Metal. ### When should I choose anubis-oss over LLMKube? Choose anubis-oss over LLMKube when anubis-oss is primarily Swift; LLMKube is Go; License: anubis-oss is GPL-3.0, LLMKube is Apache-2.0; Pricing: The tool is free and open-source with no monetary costs for usage or distribution.; Requirements: Min 8 GB RAM; Tags unique to anubis-oss: benchmarking, llm, local-llm, macos; Also covers Evaluation & Observability; When developing and evaluating large language models intended to run natively on Apple Silicon hardware. ### When should I avoid LLMKube? Avoid LLMKube if your deployment environment strictly limits the use of Kubernetes or does not support the specified GPU types - NVIDIA CUDA, AMD Vulkan, Apple Silicon Metal. Not recommended for users who require a solution that only supports specific models or runtimes which are not covered by the runtime options provided (llama.cpp, vLLM, TGI, mlx-server). ### When should I avoid anubis-oss? If your development does not involve Apple Silicon or macOS environments as Anubis-oss is tightly integrated with these platforms. When preferring a language other than Swift, since Anubis-oss depends on this for its operations. ### Is LLMKube or anubis-oss more popular on GitHub? anubis-oss has more GitHub stars (198 vs 183). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMKube and anubis-oss open source? Yes - both are open-source projects on GitHub (LLMKube: Apache-2.0, anubis-oss: GPL-3.0). ### Where can I find alternatives to LLMKube or anubis-oss? GraphCanon lists graph-backed alternatives at [LLMKube alternatives](/tools/defilantech-llmkube/alternatives) and [anubis-oss alternatives](/tools/uncsoft-anubis-oss/alternatives) ([LLMKube markdown twin](/tools/defilantech-llmkube/alternatives.md), [anubis-oss markdown twin](/tools/uncsoft-anubis-oss/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-uncsoft-anubis-oss.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLMKube or anubis-oss? LLMKube: Very active. anubis-oss: 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 LLMKube and anubis-oss? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMKube trust report](/tools/defilantech-llmkube/trust); [anubis-oss trust report](/tools/uncsoft-anubis-oss/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/_