Amazon Bedrock Now Offers Web Search for Foundation Model Grounding

A new feature has been added to Amazon Bedrock, a comprehensive data analysis tool that helps developers build and deploy AI models. The feature is called Web Search, and it allows foundation models to access current web knowledge in real-time. This means that when a model needs information about recent events or updates, it can now retrieve this information directly from the web without requiring manual intervention.

When building AI-powered applications, one of the biggest challenges developers face is grounding their models in up-to-date knowledge. Foundation models are trained on vast amounts of data but often lack current information, leading to inaccuracies and hallucinations. Web Search addresses this issue by providing a seamless way for models to access web-based knowledge without requiring manual integration or maintenance.

The traditional approach to connecting foundation models with web knowledge involves identifying, integrating, and maintaining third-party Web Search providers. This process can be time-consuming and introduces data residency risks and operational overheads. With the introduction of Web Search on Amazon Bedrock, developers no longer need to worry about these complexities, as it becomes a native capability of the platform.

Web Search is designed specifically for Amazon Bedrock model inference and offers several key differentiators. Firstly, its multi-source grounding approach combines a web index operated by Amazon with a built-in knowledge graph that anchors entities in a domain along with their connections. This enables Web Search to answer factual questions with strong confidence rather than relying on the model’s inference capabilities.

Another significant advantage of Web Search is its context-efficient retrieval mechanism. Rather than handing the model raw pages and expecting it to find relevant information, Web Search performs semantic snippet extraction – pulling passages from web pages that bear on the query and returning them in a form optimized for the model’s context window. This reduces the number of tokens spent on boilerplate and speeds up retrieval.

Enabling Web Search is straightforward and requires only three steps: configuring AWS credentials, pointing the OpenAI client at the bedrock-mantle endpoint, and adding the Web Search tool to the request. The Responses API supports built-in tools natively, so no function schema needs to be defined or a client-side loop managed.

Once enabled, Web Search becomes a single parameter in existing OpenAI-compatible API calls, removing the need for vendor onboarding, API keys, orchestration layers, and separate SDKs. This simplifies development and reduces operational overheads. Additionally, Web Search operates entirely within Amazon Bedrock’s infrastructure, supporting customers’ compliance requirements.

When using Web Search, developers can choose between two retrieval modes: indexed-web or live-web. Indexed-web is currently available, while live-web will be enabled in a future update. The default mode requires the bedrock-websearch:ExternalWebAccess permission, which grants access to Amazon’s pre-indexed web corpus.

The API returns grounded responses with structured citation annotations, including URL and page title for each referenced source. This enables developers to render inline footnotes or highlight exact spans supported by citations. Web Search is integrated with AWS CloudTrail out of the box, providing a comprehensive audit trail without exposing query text or raw page content retrieved during searches.

The integration with CloudTrail captures calling identity, timestamp, action, and account context for each request. This provides security and compliance teams with a clear view of who used the tool when, without revealing what end users searched for. The lack of exposure to sensitive information is particularly beneficial in environments where data residency requirements are stringent.

Web Search on Amazon Bedrock removes the undifferentiated heavy lifting associated with connecting foundation models to up-to-date web knowledge. It delivers context-efficient, multi-source grounded results with low-latency and simple enablement through a single API parameter – allowing developers to add web grounding without managing vendors or compliance reviews.

The feature is generally available in US regions (us-east-1, us-east-2, and us-west-2) for pricing details. To get started, see the Web Search documentation for complete API references and examples.