Amazon Bedrock Guardrails vs Lakera Guard (Check Point AI 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.

Lakera Guard (Check Point AI Guardrails)

OpenAI message format in, including tools, tool calls and tool results. Prompts and outputs are stored for the dashboard by default.

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

Lakera Guard (Check Point AI Guardrails)

1. Start the project in Detect mode and calibrate before Enforce. In Detect mode flagged is always false
2. Send the whole conversation, but expect only the last user, assistant or tool turn to be scored
3. Ask for breakdown=true and treat the confidence levels as a dial, not a boolean
4. Turn off prompt logging in General Settings if the dashboard shouldn't hold user content
5. Pin eu.api.lakera.ai or us.api.lakera.ai rather than api.lakera.ai when residency matters