Building an enterprise-ready generative AI platform on AWS - AWS Prescriptive Guidance
Intended audienceObjectives

Building an enterprise-ready generative AI platform on AWS

Ratan Kumar, Jeffrey Zeng, and Viral Shah, Amazon Web Services

June 2025 (document history)

It's common to hear that prototypes are easy, demos are cool, but production is hard. This is especially true with generative AI applications. This strategy document provides comprehensive guidance to help organizations implement secure, scalable, and effective generative AI platforms on AWS. It outlines a four-layer architectural approach that encompasses reliable infrastructure, foundation model selection, robust security and governance, and repeatable application patterns.

AI can help organization deliver differentiated products and services. Among the different domains of AI, generative AI has emerged as a powerful catalyst. It redefines how enterprises can simulate scenarios, optimize operations, and deliver compelling digital experiences to their customers.

While many organizations are creating numerous generative AI prototypes, they struggle to scale these into production-ready solutions. To facilitate widespread generative AI adoption, enterprises must address several critical challenges:

  • Infrastructure readiness and scalability

  • Security and compliance requirements

  • Responsible AI implementation

  • Integration with existing applications and processes

  • Protection of sensitive data and intellectual property

  • Cost optimization and return on investment (ROI) measurement

Additionally, you need to think about the ROI of solutions that are powered by generative AI. How can you integrate generative AI into existing applications? How can you make sure that AI-generated recommendations are safe and reliable? How can you help protect sensitive data and intellectual property when using existing foundation models or fine-tuned models?

By developing enterprise-ready generative AI capabilities, you can overcome these challenges. This strategy document offers a roadmap to help you understand and accelerate generative AI initiatives while maintaining compliance with security and ethical standards. It also describes how you can use AWS services, such as Amazon Bedrock and Amazon Q, to achieve your target business outcomes. These services can power everything from customer service automations to developer productivity tools. They can also help you implement chat-based assistants and AI agents that transform your business operations.

Note

Before implementing this guidance, we recommend that you assess your organization's generative AI readiness by using the Generative AI workload assessment. This strategy document builds upon the established AWS best practices and patterns for enterprise generative AI adoption that are documented in this guide.

Intended audience

This strategy document is intended for chief executive officers (CEOs), chief technology officer (CTOs), chief information officers (CIOs), and other senior technology leaders who are responsible for driving AI innovation and digital transformation. To use this strategy document, you should be familiar with business and technical management concepts, such as enterprise architectures, cloud computing models, IT governance frameworks, strategic technology planning, and digital transformation initiatives.

Objectives

By implementing the recommendations in this strategy document, organizations can achieve the following targeted business outcomes:

  • Transform prototypes into production-ready generative AI solutions that deliver measurable business value

  • Help distributed teams experiment faster while maintaining security and governance

  • Establish enterprise-wide access to approved foundation models with proper controls and governance

  • Reduce development time and costs through reusable application patterns and standardized tooling

  • Accelerate innovation through secure, self-service access to generative AI capabilities

  • Implement responsible AI practices that promote ethical use and compliance

  • Create a scalable framework for evaluating and adopting new foundation models

  • Build trust in AI-generated content through proper validation and control mechanisms

  • Optimize operational costs while maintaining high performance and security standards

  • Seamlessly integrate generative AI capabilities into existing enterprise applications