--- title: "awesome-open-mlops vs mlrun" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fuzzylabs-awesome-open-mlops-vs-mlrun-mlrun" tools: ["fuzzylabs-awesome-open-mlops", "mlrun-mlrun"] --- # awesome-open-mlops vs mlrun *GraphCanon updated Aug 4, 2026* ## Verdict Pick awesome-open-mlops if awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs; pick mlrun if mLRun: Open-source MLOps platform for rapid ML application development integrated into CI/CD pipelines using Python. [awesome-open-mlops](https://github.com/fuzzylabs/awesome-open-mlops) reports 482 GitHub stars, 54 forks, and 6 open issues, last pushed May 19, 2025. [mlrun](https://mlrun.org) has 1.7k stars, 315 forks, and 110 open issues, last pushed Aug 2, 2026. Figures are from public GitHub metadata via [awesome-open-mlops's repository](https://github.com/fuzzylabs/awesome-open-mlops) and [mlrun's repository](https://github.com/mlrun/mlrun). | | [awesome-open-mlops](/tools/fuzzylabs-awesome-open-mlops.md) | [mlrun](/tools/mlrun-mlrun.md) | | --- | --- | --- | | Tagline | Model deployment and serving guide with open-source MLOps tools | MLOps Platform for Building and Managing Continuous ML Applications | | Stars | 482 | 1,690 | | Forks | 54 | 315 | | Open issues | 6 | 110 | | Language | - | Python | | Adopt for | awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs. | MLRun: Open-source MLOps platform for rapid ML application development integrated into CI/CD pipelines using Python. | | Persona | - | - | | Runtime | - | - | | License | Apache 2.0 licensed, compatible with other Apache software, promoting free use in both commercial and non-commercial contexts. | Apache-2.0 | | Categories | Inference & Serving | Developer Tools, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-open-mlops](/tools/fuzzylabs-awesome-open-mlops.md) | [mlrun](/tools/mlrun-mlrun.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 442d | 0d | | Open issues (now) | 6 | 110 | | Full report | [trust report](/tools/fuzzylabs-awesome-open-mlops/trust.md) | [trust report](/tools/mlrun-mlrun/trust.md) | ## Decision facts: awesome-open-mlops - **Hosting:** unknown - No specific details available. - **Pricing:** freemium - `awesome-open-mlops` is freely accessible but depends on the community for updates and content contributions. No paid services are associated with this repository, making it purely a curated resource. - **Adopt for:** awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs. - **License detail:** Apache 2.0 licensed, compatible with other Apache software, promoting free use in both commercial and non-commercial contexts. ## Decision facts: mlrun - **Adopt for:** MLRun: Open-source MLOps platform for rapid ML application development integrated into CI/CD pipelines using Python. ## Choose when ### Choose awesome-open-mlops if… - No specific details available. - Pricing: `awesome-open-mlops` is freely accessible but depends on the community for updates and content contributions. No paid services are associated with this repository, making it purely a curated resource.. - Tags unique to awesome-open-mlops: datascience, devops, infrastructure, machine-learning. - When seeking a comprehensive list of open-source models focused on deploying and serving ML models for REST API use cases ### Choose mlrun if… - Tags unique to mlrun: ci-cd, machine learning pipelines, serverless functions. - Also covers Developer Tools, Model Training. - Requires seamless integration of ML workflows into existing CI/CD environments ## When NOT to use awesome-open-mlops - Avoid if you need proprietary or commercial MLOps solutions that offer enterprise support or features not covered by open-source projects - Not suitable for scenarios where model serving frameworks outside of the curated list, such as those under different licenses like AGPL-3.0 used by BodyworkML, are required ## When NOT to use mlrun - Lacks requirement for serverless function deployment with auto-scaling capabilities - CI/CD integration is not a priority or already fully catered to by alternative tools ## Common questions ### What is the difference between awesome-open-mlops and mlrun? awesome-open-mlops: Model deployment and serving guide with open-source MLOps tools. mlrun: MLOps Platform for Building and Managing Continuous ML Applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-open-mlops over mlrun? Choose awesome-open-mlops over mlrun when No specific details available; Pricing: `awesome-open-mlops` is freely accessible but depends on the community for updates and content contributions. No paid services are associated with this repository, making it purely a curated resource.; Tags unique to awesome-open-mlops: datascience, devops, infrastructure, machine-learning; When seeking a comprehensive list of open-source models focused on deploying and serving ML models for REST API use cases. ### When should I choose mlrun over awesome-open-mlops? Choose mlrun over awesome-open-mlops when Tags unique to mlrun: ci-cd, machine learning pipelines, serverless functions; Also covers Developer Tools, Model Training; Requires seamless integration of ML workflows into existing CI/CD environments. ### When should I avoid awesome-open-mlops? Avoid if you need proprietary or commercial MLOps solutions that offer enterprise support or features not covered by open-source projects Not suitable for scenarios where model serving frameworks outside of the curated list, such as those under different licenses like AGPL-3.0 used by BodyworkML, are required ### When should I avoid mlrun? Lacks requirement for serverless function deployment with auto-scaling capabilities CI/CD integration is not a priority or already fully catered to by alternative tools ### Is awesome-open-mlops or mlrun more popular on GitHub? mlrun has more GitHub stars (1,690 vs 482). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-open-mlops and mlrun open source? Yes - both are open-source projects on GitHub (awesome-open-mlops: Apache-2.0, mlrun: Apache-2.0). ### Where can I find alternatives to awesome-open-mlops or mlrun? GraphCanon lists graph-backed alternatives at [awesome-open-mlops alternatives](/tools/fuzzylabs-awesome-open-mlops/alternatives) and [mlrun alternatives](/tools/mlrun-mlrun/alternatives) ([awesome-open-mlops markdown twin](/tools/fuzzylabs-awesome-open-mlops/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/fuzzylabs-awesome-open-mlops-vs-mlrun-mlrun.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-open-mlops or mlrun? awesome-open-mlops: 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 awesome-open-mlops and mlrun? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-open-mlops trust report](/tools/fuzzylabs-awesome-open-mlops/trust); [mlrun trust report](/tools/mlrun-mlrun/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=fuzzylabs-awesome-open-mlops`](/api/graphcanon/graph?tool=fuzzylabs-awesome-open-mlops) - 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/_