Microsoft Presidio vs Llama Guard
Both are alternatives to Azure AI Content Safety. Here's how they stack up — verified facts, no spin.
Also searched as Llama Guard vs Microsoft Presidio — same comparison, one verdict.
Microsoft Presidio
Find and redact personal data before it reaches the model — or the logs.
Presidio is Microsoft's open-source PII detection and anonymisation toolkit, and it solves the guardrail problem most teams discover last: personal data flowing into prompts, and from there into a provider's logs and possibly their training data. It detects a wide range of entity types across text and images, supports custom recognisers for your own identifier formats, and offers redaction, masking and reversible pseudonymisation. MIT licensed, and it runs entirely locally — which is the only sane place to do this work.
Llama Guard
The strongest classifier here — but read the licence before you ship it.
Llama Guard is Meta's safety-classification model family, fine-tuned to classify prompts and responses against a configurable taxonomy — and unusually, the taxonomy is a parameter you can edit rather than a fixed list, so your categories can be your own. As a purpose-trained model it outperforms rule-based scanners on nuanced content. The caveat we will not bury: it ships under Meta's Llama Community License, not an OSI-approved open-source licence. It is free for most use but carries acceptable-use terms and a scale threshold, so it is not open source in the sense the rest of this list is.
Side by side
| Microsoft Presidio | Llama Guard | |
|---|---|---|
| Sovereignty Score | 94 | 72 |
| Open source | Yes | No |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | MIT | Llama Community License (source-available) |
| Pricing | Free, MIT, from Microsoft's open-source organisation. | Free to download and run under Meta's community licence terms. |
Microsoft Presidio edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
Microsoft Presidio
Strengths
- +Purpose-built for the PII problem, and best in class at it
- +Custom recognisers for your own identifier formats
- +Reversible pseudonymisation as well as redaction
- +MIT and fully local — the data never has to move
Trade-offs
- −PII only — not a general safety or injection layer
- −Detection needs tuning per domain to avoid over-redaction
- −Adds a processing step before every model call
Llama Guard
Strengths
- +Best classification quality of the options here
- +Editable taxonomy — your safety categories, not a vendor's
- +Runs entirely on your own hardware
- +Classifies both prompts and responses
Trade-offs
- −Not open source — Llama Community Licence with acceptable-use terms
- −Licence carries a monthly-active-user threshold; check it applies to you
- −Needs GPU capacity alongside your main model
More Azure AI Content Safety head-to-heads
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Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.