Does DeepInspect replace Bedrock Guardrails or sit alongside it?

Either pattern works. Customers who want a single policy plane across all models use DeepInspect as the system of record and disable Guardrails. Customers who want belt-and-braces leave Guardrails on for Bedrock traffic and use DeepInspect for everything else plus a unified audit log.

Will an alternative add latency?

A well-designed gateway adds under 50ms end-to-end. Compared to typical LLM inference (500ms to 5 seconds), the gateway overhead is negligible. Configuration choices that pull in side-call detection models can add more; the architecture matters more than the marketing number.

Pricing models vary by vendor. Per-request gateway pricing tends to be predictable. Per-detection-model pricing scales with API call volume. For multi-million-call deployments, the per-request gateway pattern often comes out cheaper than per-call detection layers.

Which alternative fits a high-risk EU AI Act deployment?

The system of record must be a per-decision audit log under Article 12 and Article 19. DeepInspect was designed around that artifact. Lakera and Aporia cover detection but produce different log shapes. Aim and Credal cover narrower surfaces. The gateway pattern is the architecture the regulatio

AWS Bedrock Guardrails alternatives: where the model-bound control falls short

AWS Bedrock Guardrails ships as part of the Bedrock service. The feature covers content filtering for harm categories, denied topics, word filters, and PII redaction. Configuration lives inside the AWS console. Enforcement runs when the request crosses Bedrock's inference path. The control is bound to Bedrock-mediated traffic. The moment the deployment includes any model that does not flow through Bedrock, Guardrails does not see the request.

I want to walk through the architectural gap, then who fills it, and what a security or compliance lead should test when evaluating alternatives.

The Bedrock-bound problem

A typical enterprise AI program does not run on one model provider. The customer-service team uses Claude through Anthropic's API directly. The data-science group calls OpenAI through Azure. The internal-tools team uses Bedrock for Claude on AWS. The agentic-workflow team self-hosts Llama on a GPU cluster. The vendor SaaS that handles email summarization calls OpenAI under the hood with its own keys.

Bedrock Guardrails covers the Bedrock-mediated traffic. The OpenAI direct call is invisible. The Anthropic direct call is invisible. The Azure-hosted call is invisible. The self-hosted call is invisible. The vendor-embedded call is invisible.

For a compliance lead under EU AI Act, NIST AI RMF, or DORA, the audit population is "every AI request that affected a high-risk decision." A control that covers a fraction of that population is not the system of record. It is one input to the system of record.

What an alternative needs to do

Five architectural properties separate a complete alternative from a single-provider feature.

First, model-agnostic enforcement. The control point sits in the HTTP path between any client and any LLM, not bound to a specific cloud's inference API.

Second, identity at the request layer. The resolved principal (user, agent, service account) attaches to every audit record. EU AI Act Article 19 requires it; HIPAA requires it; NIST RMF MAP function expects it.

Third, per-decision audit log. The log is append-only, signed, retained for at least six months, and structured around the request decision rather than the user session.

Fourth, policy evaluation that covers more than content filtering. Identity-bound access (who can call which model with what data), data classification, redaction, tool-use policies for agents, and response treatment.

Fifth, sub-100ms overhead so the gateway is viable in production for latency-sensitive applications.

DeepInspect

DeepInspect is a stateless policy gateway for any LLM. The gateway intercepts HTTP AI traffic between authenticated users or agents and any model, evaluates identity-bound policy, treats the data, and writes a per-decision audit record. Coverage extends to Bedrock, OpenAI, Anthropic direct, Azure AI, Vertex, self-hosted models, and vendor-embedded usage that round-trips through the customer's environment.

End-to-end overhead measures under 50ms in production tests. Identity resolves through OAuth, SSO, or signed agent identities. The audit log is append-only, signed, and retained per the deployer's regulatory profile.

DeepInspect is the right alternative when the obligation is to produce a single per-decision audit trail across all AI traffic and to enforce identity-bound policy at the request layer.

Lakera (now part of Check Point)

Check Point acquired Lakera in early 2025. Lakera's primary capability is prompt-injection detection and content classification. The detection model runs as a side-call from the application; the integration is application-mediated rather than HTTP-path-mediated. Coverage of identity at the request layer and per-decision audit logging is application-side, not gateway-side.

Lakera is a strong fit when the priority is prompt-injection defense and the team can integrate the detection model into every AI request site in code. Lakera is a weaker fit when the obligation is to cover identity-less programmatic and vendor-embedded traffic with a single audit record.

Aim Security

Aim Security focuses on browser and endpoint DLP for SaaS AI usage. Coverage is user-pasted data into browser AI tabs and corporate document movement. The architecture is pre-network. Vendor-embedded usage and machine-to-machine API traffic sit outside Aim's coverage. See the dedicated DeepInspect vs Aim Security comparison for the field-by-field breakdown.

Aporia

Aporia operates as a guardrail and observability layer that integrates per-model. The integration pattern is similar to Lakera in that the control sits as a side-call from the application. Aporia covers prompt classification, output validation, and a usage dashboard. The audit artifact is structured around model performance and policy hits, not the per-decision regulatory log.

Credal

Credal is an internal AI portal that gives employees a sanctioned chat interface to corporate LLMs. Controls evaluate at the portal layer. Traffic outside the portal (developer API calls, scheduled jobs, vendor-embedded usage) sits outside Credal's coverage. See the DeepInspect vs Credal breakdown for the boundary detail.

Evaluation checklist

When the goal is to replace or extend Bedrock Guardrails with a true alternative, the test cases that separate the categories:

Send a request through OpenAI's API directly from a Python script on a build server. Does the alternative produce a per-decision audit record with the resolved principal attached?
Send a request through Anthropic's API from an agent identity in a CI workflow. Does the alternative resolve the agent identity and enforce policy?
Send a vendor SaaS request that uses the customer's API keys. Does the alternative intercept the call?
Trigger a prompt-injection attempt against an agent that calls an internal tool. Does the alternative block the unsafe tool call?
Produce a regulator-ready report of every AI decision touching a specific data classification across all models in the last 30 days. Does the alternative export the report from a single log?

A gateway-pattern alternative answers all five. A model-bound feature answers none.

Where DeepInspect fits

DeepInspect was built for the five test cases above. The gateway sits in the HTTP path between any authenticated user or agent and any LLM. Identity travels with the request. Policy evaluates at the gateway. The decision and the data shaping happen in the request path. The audit log is per-decision, append-only, signed, and exportable.

If you are running multi-model AI and Bedrock Guardrails covers a fraction of your audit population, let's talk today about closing the gap before the August 2 deadline.