Microsoft Presidio vs LLM Guard
Both are alternatives to Azure AI Content Safety. Here's how they stack up — verified facts, no spin.
Also searched as LLM 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.
LLM Guard
A scanner suite for input and output. The fastest thing to put in front of an app.
LLM Guard from Protect AI is a collection of composable scanners covering the practical threat surface: prompt injection, jailbreak attempts, personal data, toxicity, secrets in prompts, code detection, relevance and refusal detection on output. You choose which scanners to run and in what order, and it sits as a layer in front of and behind the model. MIT licensed, self-hosted, and the quickest of these to add to something already running.
Side by side
| Microsoft Presidio | LLM Guard | |
|---|---|---|
| Sovereignty Score | 94 | 93 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | MIT | MIT |
| Pricing | Free, MIT, from Microsoft's open-source organisation. | Free, MIT licensed. |
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
LLM Guard
Strengths
- +Broad scanner set covering both input and output threats
- +Compose only the checks you need — each is independent
- +Straightforward to insert into an existing application
- +MIT, fully self-hosted
Trade-offs
- −Model-based scanners need their own compute
- −Every added scanner adds latency
- −Thresholds require real tuning to avoid false positives
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.