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AWS AI Factories - AI Infrastructure in Your DC

AWS AI Factories: dedicated AWS AI infrastructure with Trainium and NVIDIA GPUs deployed in your own data center, run like a private AWS Region.

Machine Learning
Pricing Model Custom engagement via the AWS account team (no public pricing)
Availability Deployed in the customer data center, incl. EU
Data Sovereignty Data residency in your own data center, suitable for EU requirements
Reliability N/A (custom agreement, no published SLA) SLA

What is AWS AI Factories?

AWS AI Factories is dedicated, high-performance AWS AI infrastructure that AWS builds and operates directly inside the customer’s data center. Instead of running AI workloads in a public cloud region, the customer provides data center space and power capacity it has already acquired, while AWS handles deployment and operation of the hardware and services. The environment is built exclusively for one customer or a designated trusted community and is fully separated from other environments.

AWS AI Factories addresses the challenge of building large AI capacity without violating strict data residency and sovereignty requirements. Building comparable AI infrastructure independently is slow and complex in procurement, setup, and optimization. AWS AI Factories instead delivers a pre-integrated stack of the latest AWS Trainium accelerators and NVIDIA GPUs, plus specialized networking and storage.

The service was announced in December 2025 and targets enterprises and governments that need a secure, isolated environment with strict data residency requirements.

Core Features

  • Dedicated infrastructure in your own DC: AWS builds and operates a physically isolated AI infrastructure inside the customer’s data center, which provides the space and power.
  • Hardware stack of Trainium and NVIDIA GPUs: A combination of the latest AWS Trainium accelerators and NVIDIA GPUs, complemented by specialized low-latency networking and high-performance storage.
  • Integrated AWS AI and ML services: Core services such as Amazon Bedrock and Amazon SageMaker provide direct access to foundation models without negotiating separate contracts with individual model providers.
  • Sovereignty and isolation: A fully separated environment operated exclusively for one customer or a trusted community, with clear operational separation from other AWS environments.

Common Use Cases

AI workloads with strict data residency requirements: Governments and regulated industries run training and inference inside their own data center, so sensitive data does not leave the site and sovereignty requirements are met.

Fast buildout of large AI capacity: Organizations that need significant AI compute quickly receive a pre-integrated stack instead of handling procurement, setup, and optimization themselves.

Dedicated AI zones for trusted communities: An exclusively operated, physically isolated environment serves as a shared AI platform for one customer or a designated trusted group, with clear separation and independent operation.

Benefits

  • Data and AI workloads stay inside your own data center, supporting data residency and sovereignty.
  • A pre-integrated stack of Trainium and NVIDIA GPUs reduces the effort and time compared with building comparable infrastructure yourself.
  • Direct access to AWS AI services such as Amazon Bedrock and Amazon SageMaker without separate negotiations with model providers.

Integration with innFactory

As an AWS Reseller, innFactory supports you with the adoption and operation of this service.

Typical Use Cases

AI workloads with strict data residency and sovereignty requirements
Training and inference for governments and regulated industries
Building a dedicated, physically isolated AI environment in your own DC
Fast AI buildout instead of building the infrastructure yourself

Frequently Asked Questions

What is AWS AI Factories?

AWS AI Factories is dedicated, rapidly deployable AWS AI infrastructure that AWS builds and operates inside the customer's own data center. The customer provides data center space and power capacity it has already acquired, while AWS handles deployment and management of the infrastructure. The environment combines AWS Trainium and NVIDIA GPUs with specialized networking, storage, and AWS AI services such as Amazon Bedrock and Amazon SageMaker.

When should I use AWS AI Factories?

AWS AI Factories is a good fit when AI workloads must not leave your own data center for regulatory or sovereignty reasons. Typical scenarios are governments and enterprises with strict data residency requirements, and organizations that want to build large, dedicated, isolated AI capacity without procuring and operating the hardware themselves.

How much does AWS AI Factories cost?

There is no public price list for AWS AI Factories. It is deployed under a custom engagement with the AWS account team, depending on scope, hardware stack, and operating model. In addition, the customer bears the cost of the data center space and power it already has in place. Clarify binding terms with AWS or your reseller.

How does AWS AI Factories support data sovereignty in the EU?

The infrastructure is deployed physically inside the customer's data center and forms a dedicated, isolated environment, so data does not need to leave your own site. AWS positions AWS AI Factories as a building block for governments and enterprises with strict data residency requirements, alongside other AWS sovereignty offerings. Specific compliance commitments should be verified with AWS on a case-by-case basis.

Note: All product information on this page has been compiled with care, but is provided without guarantee and may be outdated or incomplete. Cloud services evolve rapidly — features, pricing, SLAs, and availability change frequently. Authoritative and up-to-date information can only be found on the official product page of AWS (official documentation). This page does not represent an offer by AWS.

AWS Cloud Expertise

innFactory is an AWS Reseller with certified cloud architects. We provide consulting, implementation, and managed services for AWS.

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