We’re excited to share that AWS has been recognized as a Leader in The Forrester Wave: AI Infrastructure Solutions, Q4 2025. In this evaluation of 13 providers, AWS received the highest score in the Strategy category.

We believe this recognition reflects our commitment to delivering flexible, cost-efficient AI infrastructure that helps you move from experimentation to production-ready AI at scale.

The growing importance of AI infrastructure

As AI adoption accelerates, you need infrastructure that supports the full lifecycle of AI workloads—from training and fine-tuning to inference at scale.

According to Forrester’s evaluation, successful AI infrastructure requires three elements:

Workload-specific architecture tailored for training, fine-tuning, and inference rather than generic computational needs.
Engineering efficiency with coordinated design across compute, network, storage, and orchestration to maximize utilization.
Sustainability and flexibility addressing power, cooling, and avoiding vendor lock-in.

What Forrester evaluated

The Forrester Wave: AI Infrastructure Solutions, Q4 2025 assessed 13 providers across detailed criteria spanning current offering, strategy, and customer feedback. AWS received the highest score possible (5.00) in 16 criteria, including:

Strategy category

Vision
Innovation
Roadmap
Partner ecosystem
Pricing flexibility and transparency

Forrester Wave chart showing AWS in the Leaders segment for AI Infrastructure Solutions, Q4 2025

Figure 1: AWS positioned as a Leader in The Forrester Wave: AI Infrastructure Solutions, Q4 2025

How AWS approaches AI infrastructure

Our approach to AI infrastructure centers on customer choice and cost efficiency. We offer both proprietary AI chips, AWS Inferentia and AWS Trainium, alongside NVIDIA GPU instances. With both options, you can match the right compute to each workload without lock-in.

As Forrester noted in the report: “AWS’ vision is to offer both proprietary accelerators and NVIDIA GPUs, giving customers cost-efficient options for inference workloads without hardware lock-in. Its custom silicon delivers strong performance for mainstream tasks, while NVIDIA supports high-end needs. The compelling roadmap signals continued investment in silicon development and closer integration with AWS services such as Amazon SageMaker. AWS partners closely with leading model developers, giving customers direct access to a diverse set of foundation models.”

Forrester also observed: “AWS is best for companies with high-volume or elastic inference workloads that prioritize cost efficiency but still need access to GPUs for peak performance scenarios.”

Getting started

Whether you’re scaling inference workloads or building foundation models, AWS provides the AI infrastructure to support your journey:

Explore AWS AI infrastructure to learn about our purpose-built chips and accelerated compute options.
Get started with Amazon Bedrock to build, customize, and deploy generative AI applications faster with fully managed foundation models (FMs).
Read the full report to see how Forrester evaluated all 13 providers across detailed criteria.

Required Objectivity Statement: Forrester doesn’t endorse any company, product, brand, or service included in its research publications and doesn’t advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. This report is part of a broader collection of Forrester resources, including interactive models, frameworks, tools, data, and access to analyst guidance. For more information, read about Forrester’s objectivity.

Attribution: The Forrester Wave: AI Infrastructure Solutions, Q4 2025, Forrester Research, Inc., December 16, 2025.

About the author

Kim Gibbons

Kim is a Senior Manager for Infrastructure Services in AWS Analyst Relations.

Related articles