--- title: "dragonfly vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dragonfly-dragonfly-vs-visenger-awesome-mlops" tools: ["dragonfly-dragonfly", "visenger-awesome-mlops"] --- # dragonfly vs awesome-mlops *GraphCanon updated Aug 4, 2026* ## Verdict Pick dragonfly if dragonfly is an open-source Python library that specializes in scalable Bayesian optimization; pick awesome-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. [dragonfly](https://github.com/dragonfly/dragonfly) reports 894 GitHub stars, 238 forks, and 43 open issues, last pushed Jun 19, 2023. [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 [dragonfly's repository](https://github.com/dragonfly/dragonfly) and [awesome-mlops's repository](https://github.com/visenger/awesome-mlops). | | [dragonfly](/tools/dragonfly-dragonfly.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Tagline | An open source Python library for scalable Bayesian optimisation. | A curated list of references for MLOps | | Stars | 894 | 14,127 | | Forks | 238 | 2,101 | | Open issues | 43 | 44 | | Language | Python | - | | Adopt for | Dragonfly is an open-source Python library that specializes in scalable Bayesian optimization | awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. | | Persona | - | - | | Runtime | - | - | | License | MIT | - | | Categories | Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dragonfly](/tools/dragonfly-dragonfly.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Days since push | 1141d | 621d | | Open issues (now) | 43 | 44 | | Owner type | Organization | User | | Full report | [trust report](/tools/dragonfly-dragonfly/trust.md) | [trust report](/tools/visenger-awesome-mlops/trust.md) | ## Shared compatibility - **Python**: [dragonfly](/tools/dragonfly-dragonfly.md) - Python runtime; [awesome-mlops](/tools/visenger-awesome-mlops.md) - Python runtime ## Decision facts: dragonfly - **Pricing:** freemium - Available under the MIT License, free to use but does require attention to licensing when redistributing derivative works. - **Requirements:** Installation requires Python and gfortran.; Additional dependencies can be installed via the `pip` package manager. - **Adopt for:** Dragonfly is an open-source Python library that specializes in scalable Bayesian optimization ## Decision facts: awesome-mlops - **Adopt for:** awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. ## Choose when ### Choose dragonfly if… - Pricing: Available under the MIT License, free to use but does require attention to licensing when redistributing derivative works.. - Requirements: Installation requires Python and gfortran.; Additional dependencies can be installed via the `pip` package manager.. - Tags unique to dragonfly: bayesian optimisation, python library, scalable optimisation. - When dealing with large-scale problems where traditional optimization methods may not be efficient enough. ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, data-science, devops, engineering. - Also covers Inference & Serving. - If you need references covering online training and inference service architecture patterns, consider awesome-mlops. ## When NOT to use dragonfly - If the problem at hand can be effectively managed by simpler or more lightweight optimization tools; Dragonfly’s strength lies in scalability and complex scenario management. - In environments where Python or extensive dependencies are not desirable, as installing and running Dragonfly requires specific setup including gfortran for certain operations. ## 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 dragonfly and awesome-mlops? dragonfly: An open source Python library for scalable Bayesian optimisation.. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories. ### When should I choose dragonfly over awesome-mlops? Choose dragonfly over awesome-mlops when Pricing: Available under the MIT License, free to use but does require attention to licensing when redistributing derivative works.; Requirements: Installation requires Python and gfortran.; Additional dependencies can be installed via the `pip` package manager.; Tags unique to dragonfly: bayesian optimisation, python library, scalable optimisation; When dealing with large-scale problems where traditional optimization methods may not be efficient enough. ### When should I choose awesome-mlops over dragonfly? Choose awesome-mlops over dragonfly when Tags unique to awesome-mlops: ai, data-science, devops, engineering; Also covers Inference & Serving; If you need references covering online training and inference service architecture patterns, consider awesome-mlops. ### When should I avoid dragonfly? If the problem at hand can be effectively managed by simpler or more lightweight optimization tools; Dragonfly’s strength lies in scalability and complex scenario management. In environments where Python or extensive dependencies are not desirable, as installing and running Dragonfly requires specific setup including gfortran for certain operations. ### 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 dragonfly or awesome-mlops more popular on GitHub? awesome-mlops has more GitHub stars (14,127 vs 894). Stars measure visibility, not whether either tool fits your constraints. ### Are dragonfly and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to dragonfly or awesome-mlops? GraphCanon lists graph-backed alternatives at [dragonfly alternatives](/tools/dragonfly-dragonfly/alternatives) and [awesome-mlops alternatives](/tools/visenger-awesome-mlops/alternatives) ([dragonfly markdown twin](/tools/dragonfly-dragonfly/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/dragonfly-dragonfly-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, dragonfly or awesome-mlops? dragonfly: 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 dragonfly and awesome-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dragonfly trust report](/tools/dragonfly-dragonfly/trust); [awesome-mlops trust report](/tools/visenger-awesome-mlops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dragonfly-dragonfly`](/api/graphcanon/graph?tool=dragonfly-dragonfly) - 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/_