---
title: "DeepSeek-R1 vs mlrun"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/deepseek-ai-deepseek-r1-vs-mlrun-mlrun"
tools: ["deepseek-ai-deepseek-r1", "mlrun-mlrun"]
---

# DeepSeek-R1 vs mlrun

*GraphCanon updated Jul 12, 2026*

## Verdict

Pick DeepSeek-R1 when license: DeepSeek-R1 is MIT, mlrun is Apache-2.0; pick mlrun when license: mlrun is Apache-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. [mlrun](https://mlrun.org) has 1.7k stars, 308 forks, and 104 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 [mlrun's repository](https://github.com/mlrun/mlrun).

| | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [mlrun](/tools/mlrun-mlrun.md) |
| --- | --- | --- |
| Tagline | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates t |
| Stars | 91,991 | 1,684 |
| Forks | 11,711 | 308 |
| Open issues | 45 | 104 |
| 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 | Apache-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) | [mlrun](/tools/mlrun-mlrun.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Very active (96%) |
| Days since push | 379d | 1d |
| Open issues (now) | 45 | 104 |
| Security scan | No lockfile | 8 low (8 low) |
| Full report | [trust report](/tools/deepseek-ai-deepseek-r1/trust.md) | [trust report](/tools/mlrun-mlrun/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, mlrun 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: 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 mlrun if…

- License: mlrun is Apache-2.0, DeepSeek-R1 is MIT.
- Tags unique to mlrun: data-engineering, data-science, experiment-tracking, kubernetes.
- 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 mlrun

- 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 mlrun?

DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. mlrun: MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates t. See the comparison table for live GitHub stats and shared categories.

### When should I choose DeepSeek-R1 over mlrun?

Choose DeepSeek-R1 over mlrun when License: DeepSeek-R1 is MIT, mlrun 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: 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 mlrun over DeepSeek-R1?

Choose mlrun over DeepSeek-R1 when License: mlrun is Apache-2.0, DeepSeek-R1 is MIT; Tags unique to mlrun: data-engineering, data-science, experiment-tracking, kubernetes; 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 mlrun?

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 mlrun more popular on GitHub?

DeepSeek-R1 has more GitHub stars (91,991 vs 1,684). Stars measure visibility, not whether either tool fits your constraints.

### Are DeepSeek-R1 and mlrun open source?

Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, mlrun: Apache-2.0).

### Where can I find alternatives to DeepSeek-R1 or mlrun?

GraphCanon lists graph-backed alternatives at [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) and [mlrun alternatives](/tools/mlrun-mlrun/alternatives) ([DeepSeek-R1 markdown twin](/tools/deepseek-ai-deepseek-r1/alternatives.md), [mlrun markdown twin](/tools/mlrun-mlrun/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-mlrun-mlrun.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, DeepSeek-R1 or mlrun?

DeepSeek-R1: Dormant. mlrun: 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 mlrun?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust); [mlrun trust report](/tools/mlrun-mlrun/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/_