--- title: "presidio vs Made-With-ML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/data-privacy-stack-presidio-vs-gokumohandas-made-with-ml" tools: ["data-privacy-stack-presidio", "gokumohandas-made-with-ml"] --- # presidio vs Made-With-ML *GraphCanon updated Jul 15, 2026* ## Verdict Pick presidio when presidio is primarily Python; Made-With-ML is Jupyter Notebook; pick Made-With-ML when made-With-ML is primarily Jupyter Notebook; presidio is Python. [presidio](https://presidio.dataprivacystack.org) reports 10k GitHub stars, 1.2k forks, and 82 open issues, last pushed Jul 15, 2026. [Made-With-ML](https://madewithml.com) has 49k stars, 7.7k forks, and 27 open issues, last pushed Mar 4, 2026. Figures are from public GitHub metadata via [presidio's repository](https://github.com/data-privacy-stack/presidio) and [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML). | | [presidio](/tools/data-privacy-stack-presidio.md) | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | | --- | --- | --- | | Tagline | An open-source framework for detecting, redacting, masking, and anonymizing sensitive data (PII) across text, images, and structured data. Supports NLP, pattern matching, and customizable pipelines. | Learn how to develop, deploy and iterate on production-grade ML applications. | | Stars | 10,005 | 48,703 | | Forks | 1,202 | 7,661 | | Open issues | 82 | 27 | | Language | Python | Jupyter Notebook | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Inference & Serving, LLM Frameworks, Model Training | AI Agents, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [presidio](/tools/data-privacy-stack-presidio.md) | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 132d | | Open issues (now) | 82 | 27 | | Owner type | Organization | User | | Full report | [trust report](/tools/data-privacy-stack-presidio/trust.md) | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | ## Shared compatibility - **Python**: [presidio](/tools/data-privacy-stack-presidio.md) - Python runtime; [Made-With-ML](/tools/gokumohandas-made-with-ml.md) - Python runtime ## Choose when ### Choose presidio if… - presidio is primarily Python; Made-With-ML is Jupyter Notebook. - Tags unique to presidio: anonymization, data-anonymization, data-masking, data-obfuscation. - Also covers Inference & Serving. - presidio ships Docker support for self-hosted deployment. ### Choose Made-With-ML if… - Made-With-ML is primarily Jupyter Notebook; presidio is Python. - Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning. - Also covers AI Agents. ## When NOT to use presidio - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - 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. ## When NOT to use Made-With-ML - Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. - 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 presidio and Made-With-ML? presidio: An open-source framework for detecting, redacting, masking, and anonymizing sensitive data (PII) across text, images, and structured data. Supports NLP, pattern matching, and customizable pipelines.. Made-With-ML: Learn how to develop, deploy and iterate on production-grade ML applications.. See the comparison table for live GitHub stats and shared categories. ### When should I choose presidio over Made-With-ML? Choose presidio over Made-With-ML when presidio is primarily Python; Made-With-ML is Jupyter Notebook; Tags unique to presidio: anonymization, data-anonymization, data-masking, data-obfuscation; Also covers Inference & Serving; presidio ships Docker support for self-hosted deployment. ### When should I choose Made-With-ML over presidio? Choose Made-With-ML over presidio when Made-With-ML is primarily Jupyter Notebook; presidio is Python; Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning; Also covers AI Agents. ### When should I avoid presidio? Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. 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. ### When should I avoid Made-With-ML? Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. 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 presidio or Made-With-ML more popular on GitHub? Made-With-ML has more GitHub stars (48,703 vs 10,005). Stars measure visibility, not whether either tool fits your constraints. ### Are presidio and Made-With-ML open source? Yes - both are open-source projects on GitHub (presidio: MIT, Made-With-ML: MIT). ### Where can I find alternatives to presidio or Made-With-ML? GraphCanon lists graph-backed alternatives at [presidio alternatives](/tools/data-privacy-stack-presidio/alternatives) and [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) ([presidio markdown twin](/tools/data-privacy-stack-presidio/alternatives.md), [Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/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/data-privacy-stack-presidio-vs-gokumohandas-made-with-ml.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, presidio or Made-With-ML? presidio: Very active. Made-With-ML: Slowing. 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 presidio and Made-With-ML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [presidio trust report](/tools/data-privacy-stack-presidio/trust); [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=data-privacy-stack-presidio`](/api/graphcanon/graph?tool=data-privacy-stack-presidio) - 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/_