--- title: "awesome-open-mlops vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fuzzylabs-awesome-open-mlops-vs-visenger-awesome-mlops" tools: ["fuzzylabs-awesome-open-mlops", "visenger-awesome-mlops"] --- # awesome-open-mlops vs awesome-mlops *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 awesome-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. [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. [awesome-mlops](https://ml-ops.org) has 14k stars, 2.1k forks, and 44 open issues, last pushed Nov 21, 2024. Figures are from public GitHub metadata via [awesome-open-mlops's repository](https://github.com/fuzzylabs/awesome-open-mlops) and [awesome-mlops's repository](https://github.com/visenger/awesome-mlops). | | [awesome-open-mlops](/tools/fuzzylabs-awesome-open-mlops.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Tagline | Model deployment and serving guide with open-source MLOps tools | A curated list of references for MLOps | | Stars | 482 | 14,127 | | Forks | 54 | 2,101 | | Open issues | 6 | 44 | | Language | - | - | | Adopt for | awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs. | awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. | | Persona | - | - | | Runtime | - | - | | License | Apache 2.0 licensed, compatible with other Apache software, promoting free use in both commercial and non-commercial contexts. | - | | Categories | Inference & Serving | 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) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Days since push | 442d | 621d | | Open issues (now) | 6 | 44 | | Owner type | Organization | User | | Full report | [trust report](/tools/fuzzylabs-awesome-open-mlops/trust.md) | [trust report](/tools/visenger-awesome-mlops/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: awesome-mlops - **Adopt for:** awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. ## 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, infrastructure. - When seeking a comprehensive list of open-source models focused on deploying and serving ML models for REST API use cases ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, data-science, engineering, federated-learning. - Also covers Model Training. - If you need references covering online training and inference service architecture patterns, consider awesome-mlops. ## 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 awesome-mlops - Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. - Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance. ## Common questions ### What is the difference between awesome-open-mlops and awesome-mlops? awesome-open-mlops: Model deployment and serving guide with open-source MLOps tools. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-open-mlops over awesome-mlops? Choose awesome-open-mlops over awesome-mlops 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, infrastructure; 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 awesome-mlops over awesome-open-mlops? Choose awesome-mlops over awesome-open-mlops when Tags unique to awesome-mlops: ai, data-science, engineering, federated-learning; Also covers Model Training; If you need references covering online training and inference service architecture patterns, consider awesome-mlops. ### 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 awesome-mlops? Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance. ### Is awesome-open-mlops or awesome-mlops more popular on GitHub? awesome-mlops has more GitHub stars (14,127 vs 482). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-open-mlops and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-open-mlops or awesome-mlops? GraphCanon lists graph-backed alternatives at [awesome-open-mlops alternatives](/tools/fuzzylabs-awesome-open-mlops/alternatives) and [awesome-mlops alternatives](/tools/visenger-awesome-mlops/alternatives) ([awesome-open-mlops markdown twin](/tools/fuzzylabs-awesome-open-mlops/alternatives.md), [awesome-mlops markdown twin](/tools/visenger-awesome-mlops/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-visenger-awesome-mlops.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 awesome-mlops? awesome-open-mlops: Dormant. awesome-mlops: Dormant. 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 awesome-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-open-mlops trust report](/tools/fuzzylabs-awesome-open-mlops/trust); [awesome-mlops trust report](/tools/visenger-awesome-mlops/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/_