Amazon Bedrock Guardrails vs Guardrails AI

Amazon Bedrock Guardrails

ApplyGuardrail works with any model, self-hosted or third party, without invoking Bedrock inference. Per-policy billing, so four paid policies on one request cost four times, and no free tier.

Guardrails AI

Validators have configurable actions for failed checks. Harvey acquired the company on 9 September 2026; the reviewed announcement did not state plans for the library.

Amazon Bedrock Guardrails

1. Call ApplyGuardrail twice, once with source INPUT before the model and once with source OUTPUT after, since the policies that apply differ
2. Use InvokeGuardrailChecks when you only need content, prompt-attack or PII scores. It needs no guardrail id and runs in detect-only mode
3. Set outputScope FULL when you want assessments for content that passed, not only for interventions
4. Budget in text units of 1,000 characters per policy. A 5,000-character tool result is five units on every paid policy
5. Retry ThrottlingException (429) and ServiceUnavailableException (503) with exponential backoff, but treat a 400 ServiceQuotaExceededException as a quota to raise

Guardrails AI

1. Pin guardrails-ai==0.11.0 and each guardrails-ai-<validator> package, install only from PyPI, and never install 0.10.1
2. Import validators from guardrails_ai.<name>, not guardrails.hub, and don't run guardrails hub install
3. Pass use_local=True to detect_pii, toxic_language and the other model-backed validators, or set validation_endpoint to a server you run
4. Set enable_metrics to false in ~/.guardrailsrc if you don't want usage metrics sent
5. Avoid building on reask and RAIL. The open 1.0.0 issues plan to remove both