Comparison
presidio vs Made-With-ML
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.
Markdown twin · presidio alternatives · Made-With-ML alternatives
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Trust & integrity
| Signal | presidio | Made-With-ML |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Slowing (132d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of today · osv@v1 | Published findings As of today · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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.
Stars
- presidio
- 10k
- Made-With-ML
- 49k
Forks
- presidio
- 1.2k
- Made-With-ML
- 7.7k
Open issues
- presidio
- 82
- Made-With-ML
- 27
Language
- presidio
- Python
- Made-With-ML
- Jupyter Notebook
Adopt for
- presidio
- -
- Made-With-ML
- -
Persona
- presidio
- -
- Made-With-ML
- -
Runtime
- presidio
- -
- Made-With-ML
- -
License
- presidio
- MIT
- Made-With-ML
- MIT
Last pushed
- presidio
- Jul 15, 2026
- Made-With-ML
- Mar 4, 2026
Categories
- presidio
- Inference & Serving, LLM Frameworks, Model Training
- Made-With-ML
- AI Agents, LLM Frameworks, Model Training
Trust and health
Maintenance
- presidio
- Very active (96%)
- Made-With-ML
- Slowing (36%)
Days since push
- presidio
- 0d
- Made-With-ML
- 132d
Open issues (now)
- presidio
- 82
- Made-With-ML
- 27
Owner type
- presidio
- Organization
- Made-With-ML
- User
OSV dependency advisories
- presidio
- No lockfile (source not queried)
- Made-With-ML
- Published findings
Full report
- presidio
- Trust report
- Made-With-ML
- Trust report
Shared compatibility
- Python · presidio: Python runtime · Made-With-ML: Python runtime
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (data-privacy-stack/presidio) · observed Jul 15, 2026
- GitHub forks (data-privacy-stack/presidio) · observed Jul 15, 2026
- Last push (data-privacy-stack/presidio) · observed Jul 15, 2026
- License file (MIT) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (GokuMohandas/Made-With-ML) · observed Jul 15, 2026
- GitHub forks (GokuMohandas/Made-With-ML) · observed Jul 15, 2026
- Last push (GokuMohandas/Made-With-ML) · observed Mar 4, 2026
- License file (MIT) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: presidio 10k · Made-With-ML 49k (synced Jul 15, 2026).
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 and Made-With-ML alternatives (presidio markdown twin, Made-With-ML markdown twin), 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 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; Made-With-ML trust report.