--- title: "Made-With-ML vs modelfox" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gokumohandas-made-with-ml-vs-modelfoxdotdev-modelfox" tools: ["gokumohandas-made-with-ml", "modelfoxdotdev-modelfox"] --- # Made-With-ML vs modelfox *GraphCanon updated Sep 28, 2026* ## Verdict Pick Made-With-ML if made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows; pick modelfox if modelFox streamlines ML model training and deployment with support for multiple programming languages like Rust, Python, Elixir, Go, JavaScript, and Ruby. [Made-With-ML](https://madewithml.com) reports 50k GitHub stars, 7.8k forks, and 25 open issues, last pushed Mar 4, 2026. [modelfox](https://github.com/modelfoxdotdev/modelfox) has 1.5k stars, 63 forks, and 39 open issues, last pushed Aug 2, 2024. Figures are from public GitHub metadata via [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML) and [modelfox's repository](https://github.com/modelfoxdotdev/modelfox). | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [modelfox](/tools/modelfoxdotdev-modelfox.md) | | --- | --- | --- | | Tagline | Learn to develop, deploy and iterate on production-grade ML applications | ModelFox simplifies machine learning model training and deployment. | | Stars | 49,547 | 1,467 | | Forks | 7,778 | 63 | | Open issues | 25 | 39 | | Language | Jupyter Notebook | Rust | | Adopt for | Made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows. | ModelFox streamlines ML model training and deployment with support for multiple programming languages like Rust, Python, Elixir, Go, JavaScript, and Ruby. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT licensed with restrictions on the 'crates/app' directory for production use, requiring a paid license. | | Categories | Developer Tools, Inference & Serving, Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [modelfox](/tools/modelfoxdotdev-modelfox.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 199d | 786d | | Open issues (now) | 25 | 39 | | Stars delta | +473 (30d) | 0 (30d) | | Open issues delta | -1 (30d) | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | [trust report](/tools/modelfoxdotdev-modelfox/trust.md) | ## Decision facts: Made-With-ML - **Requirements:** A foundational understanding of Python programming is required to fully benefit from the learning resources provided. - **Adopt for:** Made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows. ## Decision facts: modelfox - **Pricing:** freemium - Free to test, requires paid licenses for production deployment of certain components. - **Adopt for:** ModelFox streamlines ML model training and deployment with support for multiple programming languages like Rust, Python, Elixir, Go, JavaScript, and Ruby. - **License detail:** MIT licensed with restrictions on the 'crates/app' directory for production use, requiring a paid license. ## Choose when ### Choose Made-With-ML if… - Made-With-ML is primarily Jupyter Notebook; modelfox is Rust. - License: Made-With-ML is MIT, modelfox is Other. - Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided.. - Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning. - Also covers Inference & Serving. - If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch. ### Choose modelfox if… - modelfox is primarily Rust; Made-With-ML is Jupyter Notebook. - License: modelfox is Other, Made-With-ML is MIT. - Pricing: Free to test, requires paid licenses for production deployment of certain components.. - Tags unique to modelfox: automl, elixir-lang, golang, javascript. - If you are looking to integrate machine learning models across a variety of programming environments such as Rust or JavaScript without the complexity of language-specific frameworks ## When NOT to use Made-With-ML - If you are looking for a niche-focused tool that caters specifically to a particular machine learning framework other than PyTorch. - For developers who already have strong backgrounds in MLOps and require highly specialized tools for managing production-grade ML deployments without additional educational support. ## When NOT to use modelfox - If you require open-source licensing for all components without exceptions since some parts like the crates/app directory need a paid license for production use - For projects that specifically avoid Rust as their primary language or environment, as ModelFox primarily leverages Rust and its ecosystem ## Common questions ### What is the difference between Made-With-ML and modelfox? Made-With-ML: Learn to develop, deploy and iterate on production-grade ML applications. modelfox: ModelFox simplifies machine learning model training and deployment.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Made-With-ML over modelfox? Choose Made-With-ML over modelfox when Made-With-ML is primarily Jupyter Notebook; modelfox is Rust; License: Made-With-ML is MIT, modelfox is Other; Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided.; Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning; Also covers Inference & Serving; If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch. ### When should I choose modelfox over Made-With-ML? Choose modelfox over Made-With-ML when modelfox is primarily Rust; Made-With-ML is Jupyter Notebook; License: modelfox is Other, Made-With-ML is MIT; Pricing: Free to test, requires paid licenses for production deployment of certain components.; Tags unique to modelfox: automl, elixir-lang, golang, javascript; If you are looking to integrate machine learning models across a variety of programming environments such as Rust or JavaScript without the complexity of language-specific frameworks. ### When should I avoid Made-With-ML? If you are looking for a niche-focused tool that caters specifically to a particular machine learning framework other than PyTorch. For developers who already have strong backgrounds in MLOps and require highly specialized tools for managing production-grade ML deployments without additional educational support. ### When should I avoid modelfox? If you require open-source licensing for all components without exceptions since some parts like the crates/app directory need a paid license for production use For projects that specifically avoid Rust as their primary language or environment, as ModelFox primarily leverages Rust and its ecosystem ### Is Made-With-ML or modelfox more popular on GitHub? Made-With-ML has more GitHub stars (49,547 vs 1,467). Stars measure visibility, not whether either tool fits your constraints. ### Are Made-With-ML and modelfox open source? Yes - both are open-source projects on GitHub (Made-With-ML: MIT, modelfox: Other). ### Where can I find alternatives to Made-With-ML or modelfox? GraphCanon lists graph-backed alternatives at [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) and [modelfox alternatives](/tools/modelfoxdotdev-modelfox/alternatives) ([Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/alternatives.md), [modelfox markdown twin](/tools/modelfoxdotdev-modelfox/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/gokumohandas-made-with-ml-vs-modelfoxdotdev-modelfox.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Made-With-ML or modelfox? Made-With-ML: Slowing. modelfox: 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 Made-With-ML and modelfox? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust); [modelfox trust report](/tools/modelfoxdotdev-modelfox/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gokumohandas-made-with-ml`](/api/graphcanon/graph?tool=gokumohandas-made-with-ml) - 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/_