--- title: "DeepSeek-R1 vs dstack" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepseek-ai-deepseek-r1-vs-dstackai-dstack" tools: ["deepseek-ai-deepseek-r1", "dstackai-dstack"] --- # DeepSeek-R1 vs dstack *GraphCanon updated Jul 12, 2026* ## Verdict Pick DeepSeek-R1 when license: DeepSeek-R1 is MIT, dstack is MPL-2.0; pick dstack when license: dstack is MPL-2.0, DeepSeek-R1 is MIT. [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) reports 92k GitHub stars, 12k forks, and 45 open issues, last pushed Jun 27, 2025. [dstack](https://dstack.ai/docs) has 2.2k stars, 237 forks, and 62 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1) and [dstack's repository](https://github.com/dstackai/dstack). | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [dstack](/tools/dstackai-dstack.md) | | --- | --- | --- | | Tagline | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal. | | Stars | 91,991 | 2,172 | | Forks | 11,711 | 237 | | Open issues | 45 | 62 | | Language | - | Python | | Adopt for | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. | - | | Persona | - | - | | Runtime | - | - | | License | MIT | MPL-2.0 | | Categories | LLM Frameworks, Model Training | AI Agents, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [dstack](/tools/dstackai-dstack.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 379d | 0d | | Open issues (now) | 45 | 62 | | Full report | [trust report](/tools/deepseek-ai-deepseek-r1/trust.md) | [trust report](/tools/dstackai-dstack/trust.md) | ## Decision facts: DeepSeek-R1 - **Pricing:** freemium - The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository. - **Requirements:** Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs. - **Adopt for:** DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. ## Choose when ### Choose DeepSeek-R1 if… - License: DeepSeek-R1 is MIT, dstack is MPL-2.0. - Pricing: The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository.. - Requirements: Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs.. - Tags unique to DeepSeek-R1: commercial use, derived models, distilled models, mit license. - When you need to work with pre-trained models derived specifically from the Qwen-2.5 and Llama3.x series, benefiting from their unique characteristics. ### Choose dstack if… - License: dstack is MPL-2.0, DeepSeek-R1 is MIT. - Tags unique to dstack: agent-skills, agentic-orchestration, amd, cloud. - Also covers AI Agents. ## When NOT to use DeepSeek-R1 - Avoid if you need foundational models rather than distilled versions, as DeepSeek-R1 specializes in providing smaller, more efficient models suitable for resource-constrained environments. - If your project is tightly regulated or requires models from a different lineage, as DeepSeek-R1 exclusively provides derivatives of Qwen and LLaMA series. ## When NOT to use dstack - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between DeepSeek-R1 and dstack? DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. dstack: Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepSeek-R1 over dstack? Choose DeepSeek-R1 over dstack when License: DeepSeek-R1 is MIT, dstack is MPL-2.0; Pricing: The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository.; Requirements: Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs.; Tags unique to DeepSeek-R1: commercial use, derived models, distilled models, mit license; When you need to work with pre-trained models derived specifically from the Qwen-2.5 and Llama3.x series, benefiting from their unique characteristics. ### When should I choose dstack over DeepSeek-R1? Choose dstack over DeepSeek-R1 when License: dstack is MPL-2.0, DeepSeek-R1 is MIT; Tags unique to dstack: agent-skills, agentic-orchestration, amd, cloud; Also covers AI Agents. ### When should I avoid DeepSeek-R1? Avoid if you need foundational models rather than distilled versions, as DeepSeek-R1 specializes in providing smaller, more efficient models suitable for resource-constrained environments. If your project is tightly regulated or requires models from a different lineage, as DeepSeek-R1 exclusively provides derivatives of Qwen and LLaMA series. ### When should I avoid dstack? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is DeepSeek-R1 or dstack more popular on GitHub? DeepSeek-R1 has more GitHub stars (91,991 vs 2,172). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSeek-R1 and dstack open source? Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, dstack: MPL-2.0). ### Where can I find alternatives to DeepSeek-R1 or dstack? GraphCanon lists graph-backed alternatives at [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) and [dstack alternatives](/tools/dstackai-dstack/alternatives) ([DeepSeek-R1 markdown twin](/tools/deepseek-ai-deepseek-r1/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/deepseek-ai-deepseek-r1-vs-dstackai-dstack.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSeek-R1 or dstack? DeepSeek-R1: Dormant. 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 DeepSeek-R1 and dstack? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust); [dstack trust report](/tools/dstackai-dstack/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=deepseek-ai-deepseek-r1`](/api/graphcanon/graph?tool=deepseek-ai-deepseek-r1) - 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/_