LLM Guard 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 LLM Guard — same comparison, one verdict.
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.
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
| LLM Guard | Llama Guard | |
|---|---|---|
| Sovereignty Score | 93 | 72 |
| Open source | Yes | No |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | MIT | Llama Community License (source-available) |
| Pricing | Free, MIT licensed. | Free to download and run under Meta's community licence terms. |
LLM Guard edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
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
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
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Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.