Amazon Bedrock Guardrails vs OpenAI Guardrails

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.

OpenAI Guardrails

MIT-licensed wrapper that adds twelve configurable checks to OpenAI client calls from one JSON file, with tool-level injection checks for the Agents SDK. The README labels it a preview at version 0.3.3, and by default a check that fails to run is reported as passed unless raise_guardrail_errors=True is set.

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

OpenAI Guardrails

1. Pass raise_guardrail_errors=True to the client. The default treats a check that failed to run as passed
2. Run python -m spacy download en_core_web_sm before using Contains PII, or client initialisation fails
3. Catch GuardrailTripwireTriggered, and append a user message to history only after the call returns without it
4. Use block=true for Contains PII in the output stage. Masking works only in the pre-flight stage
5. Keep stream=False where output must be checked before it is shown, and budget one extra model call per LLM-based check