Google Cloud Model Armor vs Llama Guard 4

Google Cloud Model Armor

2 million free tokens a month, then $0.10 per million. OAuth only, and a template must exist in the same location as the endpoint before the first call.

Llama Guard 4

A single self-hosted model classifies text and multi-image prompts against 14 MLCommons-aligned hazard categories and answers in a few tokens. The weights have not changed since 29 April 2025, download access needs Meta's manual approval, and the licence withholds the grant from individuals and companies based in the European Union.

Google Cloud Model Armor

1. Create one template per location you call from. A template in us-central1 doesn't answer on the europe-west2 endpoint
2. Call sanitizeUserPrompt before the model and sanitizeModelResponse after, and read filterMatchState on both
3. Treat EXECUTION_SKIPPED as unchecked, not clean. It means the input went over the filter's 65,536-token cap
4. Pin the template to the Stable alias and move off v1 and v2 before 17 December 2026. In asia-northeast3, v1 stays the Stable version
5. Retry 500, 502, 503 and 504 with truncated exponential backoff, and keep fan-out under the 1,200 queries a minute shared by the project

Llama Guard 4

1. Request access on the Hugging Face page before anything else. Approval is manual, and the form cannot be edited after submission
2. Send only the user turn to check an input, and the user turn plus the model's answer to check an output. The template picks the role from the message count
3. Parse the first line for safe or unsafe and the second for category codes. Set max_new_tokens to about 10 and turn sampling off
4. Do not send an image with no text. Meta says the model is not an image-only classifier, and S14 is skipped when an image is present
5. Pair it with a prompt-attack detector. The card says the model can itself be moved by adversarial or injected text