Solutions for Artificial Intelligence

The easiest way to build and scale generative AI applications with foundation models

Accelerates development and streamlines experimentation by helping you ingest your business-specific data and documents, evaluate and compare the performance of large language models (LLMs), rapidly build extensible applications, and deploy those applications with an enterprise-grade architecture.

Build, train, and deploy ML models—including FMs—for any use case with fully managed infrastructure, tools, and workflows

The most capable generative AI-powered assistant for accelerating software development and leveraging companies' internal data

This Guidance shows how you can optimize your generative AI models using Amazon SageMaker, a service where you can build, train, and deploy large language models (LLMs) at scale.

This Guidance demonstrates how to build an internal Software-as-a-Service (SaaS) platform that provides access to foundation models, like those available through Amazon Bedrock, to different business units or teams within your organization.

This Guidance demonstrates how to build an application for search based on the information in an enterprise knowledge base through the deployment of interface nodes, including large language models (LLMs).

This Guidance demonstrates how to aggregate public, open-source code repositories into a central location to customize Amazon Q Developer.

This Guidance helps software companies set up a system to detect error logs, generate bug fixes, and create pull requests.