--- title: "presidio vs GLiNER" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/data-privacy-stack-presidio-vs-urchade-gliner" tools: ["data-privacy-stack-presidio", "urchade-gliner"] --- # presidio vs GLiNER *GraphCanon updated Aug 18, 2026* ## Verdict Pick presidio if presidio is an open-source framework for identifying and anonymizing sensitive data including text, images, and structured formats through its NLP, pattern matching, and customizable pipeline capabilities; pick GLiNER if gLiNER is ideal for extracting named entities from text with minimal computational resources. [presidio](https://presidio.dataprivacystack.org) reports 10k GitHub stars, 1.2k forks, and 102 open issues, last pushed Aug 8, 2026. [GLiNER](https://urchade.github.io/GLiNER) has 3.5k stars, 299 forks, and 96 open issues, last pushed Aug 10, 2026. Figures are from public GitHub metadata via [presidio's repository](https://github.com/data-privacy-stack/presidio) and [GLiNER's repository](https://github.com/urchade/GLiNER). | | [presidio](/tools/data-privacy-stack-presidio.md) | [GLiNER](/tools/urchade-gliner.md) | | --- | --- | --- | | Tagline | A framework for detecting and anonymizing sensitive data | Generalist and Lightweight Model for Named Entity Recognition | | Stars | 10,395 | 3,545 | | Forks | 1,237 | 299 | | Open issues | 102 | 96 | | Language | Python | Python | | Adopt for | Presidio is an open-source framework for identifying and anonymizing sensitive data including text, images, and structured formats through its NLP, pattern matching, and customizable pipeline capabilities. | GLiNER is ideal for extracting named entities from text with minimal computational resources. | | Persona | - | - | | Runtime | - | - | | License | MIT License for use under permissive terms that allows free usage for commercial or non-commercial purposes with full source code available. | Apache-2.0 | | Categories | Data & Retrieval, Evaluation & Observability | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [presidio](/tools/data-privacy-stack-presidio.md) | [GLiNER](/tools/urchade-gliner.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 7d | | Open issues (now) | 102 | 96 | | Stars delta | Unknown | +143 (30d) | | Open issues delta | Unknown | -1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/data-privacy-stack-presidio/trust.md) | [trust report](/tools/urchade-gliner/trust.md) | ## Shared compatibility - **Python**: [presidio](/tools/data-privacy-stack-presidio.md) - Python runtime; [GLiNER](/tools/urchade-gliner.md) - Python runtime ## Decision facts: presidio - **Pricing:** freemium - Open-source and freely usable as it relies on the MIT license; however, additional support services may incur costs. - **Requirements:** Requires Docker - **Adopt for:** Presidio is an open-source framework for identifying and anonymizing sensitive data including text, images, and structured formats through its NLP, pattern matching, and customizable pipeline capabilities. - **License detail:** MIT License for use under permissive terms that allows free usage for commercial or non-commercial purposes with full source code available. ## Decision facts: GLiNER - **Adopt for:** GLiNER is ideal for extracting named entities from text with minimal computational resources. ## Choose when ### Choose presidio if… - License: presidio is MIT, GLiNER is Apache-2.0. - Pricing: Open-source and freely usable as it relies on the MIT license; however, additional support services may incur costs.. - Requirements: Requires Docker. - Tags unique to presidio: data-anonymization, data-obfuscation, group:python-frameworks. - Also covers Evaluation & Observability. - presidio ships Docker support for self-hosted deployment. - When you need a tool that supports not only text but also image and structured data anonymization, Presidio offers broad coverage for different data types. ### Choose GLiNER if… - License: GLiNER is Apache-2.0, presidio is MIT. - Tags unique to GLiNER: information-extraction, large language models, named-entity-recognition, natural-language-processing. - Also covers Model Training. - When you need a lightweight solution for named entity recognition across various languages ## When NOT to use presidio - Avoid using Presidio if your project strictly requires manual data anonymization processes as it mainly supports automated detection. - Presidio's automated mechanisms may not catch all sensitive information, so you should not solely rely on it when a near-perfect accuracy rate in PII identification is crucial. ## When NOT to use GLiNER - If high precision in niche specializations like medical terms or rare proper nouns is required - In scenarios demanding heavy customization beyond basic named entity recognition capabilities ## Common questions ### What is the difference between presidio and GLiNER? presidio: A framework for detecting and anonymizing sensitive data. GLiNER: Generalist and Lightweight Model for Named Entity Recognition. See the comparison table for live GitHub stats and shared categories. ### When should I choose presidio over GLiNER? Choose presidio over GLiNER when License: presidio is MIT, GLiNER is Apache-2.0; Pricing: Open-source and freely usable as it relies on the MIT license; however, additional support services may incur costs.; Requirements: Requires Docker; Tags unique to presidio: data-anonymization, data-obfuscation, group:python-frameworks; Also covers Evaluation & Observability; presidio ships Docker support for self-hosted deployment; When you need a tool that supports not only text but also image and structured data anonymization, Presidio offers broad coverage for different data types. ### When should I choose GLiNER over presidio? Choose GLiNER over presidio when License: GLiNER is Apache-2.0, presidio is MIT; Tags unique to GLiNER: information-extraction, large language models, named-entity-recognition, natural-language-processing; Also covers Model Training; When you need a lightweight solution for named entity recognition across various languages. ### When should I avoid presidio? Avoid using Presidio if your project strictly requires manual data anonymization processes as it mainly supports automated detection. Presidio's automated mechanisms may not catch all sensitive information, so you should not solely rely on it when a near-perfect accuracy rate in PII identification is crucial. ### When should I avoid GLiNER? If high precision in niche specializations like medical terms or rare proper nouns is required In scenarios demanding heavy customization beyond basic named entity recognition capabilities ### Is presidio or GLiNER more popular on GitHub? presidio has more GitHub stars (10,395 vs 3,545). Stars measure visibility, not whether either tool fits your constraints. ### Are presidio and GLiNER open source? Yes - both are open-source projects on GitHub (presidio: MIT, GLiNER: Apache-2.0). ### Where can I find alternatives to presidio or GLiNER? GraphCanon lists graph-backed alternatives at [presidio alternatives](/tools/data-privacy-stack-presidio/alternatives) and [GLiNER alternatives](/tools/urchade-gliner/alternatives) ([presidio markdown twin](/tools/data-privacy-stack-presidio/alternatives.md), [GLiNER markdown twin](/tools/urchade-gliner/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-urchade-gliner.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, presidio or GLiNER? presidio: Very active. GLiNER: Active. 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 GLiNER? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [presidio trust report](/tools/data-privacy-stack-presidio/trust); [GLiNER trust report](/tools/urchade-gliner/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/_