--- title: "DeepSeek-R1 vs ludwig" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepseek-ai-deepseek-r1-vs-ludwig-ai-ludwig" tools: ["deepseek-ai-deepseek-r1", "ludwig-ai-ludwig"] --- # DeepSeek-R1 vs ludwig *GraphCanon updated Jul 12, 2026* ## Verdict Pick DeepSeek-R1 if deepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use; pick ludwig if ludwig is a low-code framework that simplifies the process of training deep learning models including custom LLMs and neural networks using Python. [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) reports 92k GitHub stars, 12k forks, and 45 open issues, last pushed Jun 27, 2025. [ludwig](http://ludwig.ai) has 12k stars, 1.2k forks, and 1 open issues, last pushed Jul 4, 2026. Figures are from public GitHub metadata via [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1) and [ludwig's repository](https://github.com/ludwig-ai/ludwig). | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [ludwig](/tools/ludwig-ai-ludwig.md) | | --- | --- | --- | | Tagline | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | Low-code framework for building custom LLMs, neural networks, and other AI models | | Stars | 91,991 | 11,734 | | Forks | 11,711 | 1,218 | | Open issues | 45 | 1 | | Language | - | Python | | Adopt for | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. | Ludwig is a low-code framework that simplifies the process of training deep learning models including custom LLMs and neural networks using Python. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0: Permissive open-source license allowing free use in both community and commercial projects. | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training, Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [ludwig](/tools/ludwig-ai-ludwig.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 379d | 7d | | Open issues (now) | 45 | 1 | | Full report | [trust report](/tools/deepseek-ai-deepseek-r1/trust.md) | [trust report](/tools/ludwig-ai-ludwig/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. ## Decision facts: ludwig - **Requirements:** Min 4 GB RAM; Requires Python and is compatible with popular deep learning libraries like PyTorch. - **Adopt for:** Ludwig is a low-code framework that simplifies the process of training deep learning models including custom LLMs and neural networks using Python. - **License detail:** Apache-2.0: Permissive open-source license allowing free use in both community and commercial projects. ## Choose when ### Choose DeepSeek-R1 if… - License: DeepSeek-R1 is MIT, ludwig is Apache-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: derived models, mit license, distilled models, commercial use. - 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 ludwig if… - License: ludwig is Apache-2.0, DeepSeek-R1 is MIT. - Requirements: Min 4 GB RAM; Requires Python and is compatible with popular deep learning libraries like PyTorch.. - Tags unique to ludwig: data-science, deep, deep-learning, fine-tuning. - Also covers Computer Vision. - When you need to build custom language models (LLMs) or other AI models with minimal configuration in Python. ## 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 ludwig - If you require direct access and extensive customization of the model architecture, as Ludwig abstracts some of these details away under its low-code interface. - When your team prefers a high-level of control over all aspects of the training process, including architectural decisions; Ludwig streamlines this process which may limit flexible adjustments. ## Common questions ### What is the difference between DeepSeek-R1 and ludwig? DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. ludwig: Low-code framework for building custom LLMs, neural networks, and other AI models. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepSeek-R1 over ludwig? Choose DeepSeek-R1 over ludwig when License: DeepSeek-R1 is MIT, ludwig is Apache-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: derived models, mit license, distilled models, commercial use; 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 ludwig over DeepSeek-R1? Choose ludwig over DeepSeek-R1 when License: ludwig is Apache-2.0, DeepSeek-R1 is MIT; Requirements: Min 4 GB RAM; Requires Python and is compatible with popular deep learning libraries like PyTorch.; Tags unique to ludwig: data-science, deep, deep-learning, fine-tuning; Also covers Computer Vision; When you need to build custom language models (LLMs) or other AI models with minimal configuration in Python. ### 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 ludwig? If you require direct access and extensive customization of the model architecture, as Ludwig abstracts some of these details away under its low-code interface. When your team prefers a high-level of control over all aspects of the training process, including architectural decisions; Ludwig streamlines this process which may limit flexible adjustments. ### Is DeepSeek-R1 or ludwig more popular on GitHub? DeepSeek-R1 has more GitHub stars (91,991 vs 11,734). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSeek-R1 and ludwig open source? Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, ludwig: Apache-2.0). ### Where can I find alternatives to DeepSeek-R1 or ludwig? GraphCanon lists graph-backed alternatives at [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) and [ludwig alternatives](/tools/ludwig-ai-ludwig/alternatives) ([DeepSeek-R1 markdown twin](/tools/deepseek-ai-deepseek-r1/alternatives.md), [ludwig markdown twin](/tools/ludwig-ai-ludwig/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-ludwig-ai-ludwig.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSeek-R1 or ludwig? DeepSeek-R1: Dormant. ludwig: 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 ludwig? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust); [ludwig trust report](/tools/ludwig-ai-ludwig/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/_