AWS Bedrock Guardrails

A fully managed service that provides access to hundreds of foundation models from leading AI companies, enabling teams to build and deploy generative AI applications and agents at production scale with enterprise security and compliance.

End-to-end platform for building, connecting, and optimizing highly capable AI agents at scale using any framework and model, with production-grade security, automatic authentication, and built-in tracing and evaluation.

A comprehensive AI safety and governance platform that provides configurable safeguards to detect and filter harmful content, redact sensitive information, detect hallucinations, and prevent prompt attacks across any foundation model—whether hosted on Bedrock or self-hosted/third-party models.

Managed agent service combining OpenAI frontier models with AWS infrastructure, delivering faster execution, built-in memory, and security for teams building with OpenAI models.

API that enables consistent safety guardrails across any foundation model—whether hosted on Bedrock or third-party models like OpenAI and Google Gemini—with real-time content moderation capabilities.

Formal logic-based safeguard using mathematical techniques to verify, correct, and explain AI-generated information, preventing factual errors and hallucinations with auditable, deterministic explainability.

An end-to-end platform for building, connecting, and optimizing highly capable AI agents at scale using any framework and model, with production-grade security and no infrastructure management required.

Flexible batch processing capability for handling large volumes of AI requests asynchronously, optimized for cost-effectiveness while maintaining quality for non-real-time use cases.

A data customization capability that automates the process of preparing and leveraging proprietary data to customize models for business-specific use cases.

A data customization capability that enables fine-tuning foundation models on proprietary datasets to optimize AI applications for specific business needs.

Configurable AI safety framework that detects and filters harmful content, redacts PII, detects hallucinations, and prevents prompt attacks across any foundation model. Offers six safeguard policies including content moderation, topic classification, and automated reasoning checks with up to 99% accuracy.

A cost optimization feature that intelligently routes prompts to the most appropriate model based on complexity and requirements, reducing costs by up to 30% while maintaining quality.

A data customization tool that enables customizing foundation models with proprietary data to build AI applications that understand customer and business context.

A managed agent solution combining OpenAI frontier models with AWS infrastructure, delivering faster execution, built-in memory, and security for teams building with OpenAI models.

A cost and performance optimization feature that creates smaller, faster models from larger ones, delivering up to 500% faster execution and 75% cost reduction with minimal accuracy impact.

A performance and cost optimization feature that caches frequently used prompts to reduce latency and expenses while maintaining output quality.

Centralized enforcement of safety controls across multiple AWS accounts and organizational units, allowing security teams to implement organization-wide guardrail policies with account-level flexibility.

Feature that automatically routes prompts to the most appropriate and cost-effective model based on complexity and requirements, reducing costs by up to 30% while maintaining output quality.

Optimization technique that creates smaller, faster, and more cost-effective models from larger foundation models, running up to 500% faster and costing up to 75% less with minimal accuracy loss.

Access to OpenAI's latest models integrated directly into Bedrock, enabling customers to use frontier OpenAI models with AWS infrastructure and enterprise features.

Performance optimization feature that caches prompt inputs to reduce latency and costs for repeated or similar queries without sacrificing response quality.