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AWS AI Factories put dedicated AI infrastructure inside a customer-owned or leased data center, with AWS deploying and operating the integrated environment. The model gives enterprises a defined place to run AI training and inference, but it does not make every part of a deployment automatically sovereign: the boundary depends on selected AWS Region integrations, operating controls, and the customer’s legal and governance requirements.
What are AWS AI Factories?
AWS AI Factories are managed, dedicated AI environments built in a customer’s data center, including a leased colocation facility. They combine AI accelerators, compute, networking, storage, and AWS services in a deployment for one customer or its designated trusted community. AWS describes the arrangement as using infrastructure capacity the customer has already acquired while AWS deploys and manages the service. AWS announced the offering on December 2, 2025.
This is not a standard server kit that an organization buys and installs itself. The customer provides a suitable facility and power capacity; AWS works with the customer on readiness, configuration, deployment, and operation. That division of responsibility is central to the offer: the equipment is dedicated and located at the customer’s site, while AWS supplies the managed infrastructure and AI services.
How are AWS AI Factories sovereign by design?
AWS says the Factory’s data plane—including model training and inference—stays inside the Factory perimeter unless the customer chooses to integrate with AWS Region services. In the AWS AI Factories FAQ, the company states: “The AWS AI Factory data plane—including model training and inference workloads—remains within the AWS AI Factory perimeter unless you explicitly choose to integrate with AWS Region services such as Amazon S3.”
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That describes a technical data boundary, not a blanket guarantee that every legal, personnel, or governance requirement is met. For example, connecting to a regional service can introduce data movement or additional dependencies that the customer must include in its governance design. AWS also describes controls and options including Nitro-based infrastructure, IAM access management, Control Tower, encryption, external key options, and auditing. Organizations should validate which controls apply to their proposed configuration rather than assuming every option is included in every deployment.
Who operates the infrastructure?
AWS says only its personnel are authorized to operate Factory infrastructure and services. Customer administrators manage access through AWS accounts and permissions, granting selected accounts or organizations access through the standard AWS Management Console and APIs for the parent Region. Those access controls are distinct from authority to operate the underlying infrastructure.
For customers with local-personnel requirements, AWS says it can work with them on operational controls such as nationality or security-clearance rules. Those requirements should be discussed as part of the deployment assessment. Data residency alone does not establish that a Factory satisfies a particular jurisdiction’s full sovereignty, regulatory, or staffing requirements.
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What infrastructure and services can a Factory include?
The current AWS FAQ lists the following options. AWS says the exact models and components are validated for each deployment and depend on configuration and availability; the catalog may change.
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| Layer | Examples listed by AWS |
|---|---|
| AI accelerators | Trainium Trn2 and Trn3; NVIDIA P6-B200, P6-B300, P6e-GB200, and P6e-GB300 UltraServer options. AWS says multiple accelerator types can be combined within a Factory. |
| AI and machine-learning services | Amazon Bedrock and Amazon SageMaker AI. AWS says inference, including Bedrock endpoints, can run inside the Factory perimeter. Bedrock model availability is validated with providers and remains subject to workload and regulatory requirements. |
| Compute and orchestration | Amazon EC2, Amazon ECS, Amazon EKS, and AWS Batch. |
| Storage | Amazon EBS, Amazon FSx for Lustre, and Amazon S3 Express One Zone. |
| Networking and security services | Amazon VPC, AWS Direct Connect, Elastic Load Balancing, and AWS Shield. |
| Additional software | AWS AI Enterprise, using a customer-provided license or a license purchased through AWS Marketplace. |
AWS says a Factory can connect to a selected AWS Region over the AWS Global Network and describes private Direct Connect connectivity at the facility. Because regional integrations are optional, customers should map each service dependency and data flow against their boundary requirements before settling the configuration. The current service and component list is in the AWS FAQ.
What must a customer provide?
The customer needs data-center space and sufficient power capacity, and the site must be ready before deployment. AWS’s process starts with the customer’s AWS account team, followed by work on site readiness, facility preparation, and configuration. A leased colocation site can qualify, but the customer still needs to ensure the facility is suitable for the planned deployment.
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- Facility and power: Confirm available space, power capacity, and readiness for the agreed configuration.
- Workload and scale: Define training, fine-tuning, or inference needs and the scale required, then validate which accelerator and service combinations are available.
- Connectivity and data boundary: Identify any planned links to AWS Regions or other services and account for their data flows in governance decisions.
- Access and operations: Decide which customer accounts and organizations need access, and raise any personnel, nationality, or clearance requirements during planning.
- Commercial and service terms: Request a deployment-specific assessment and review the service-level agreements (SLAs) applicable to the actual configuration.
How long does it take to deploy an AWS AI Factory?
AWS estimates approximately 3–6 months from when the data center is ready and handed over. This is an AWS estimate, not a guaranteed or independently measured schedule; the FAQ says configuration complexity and component availability affect timing. The clock therefore should not be treated as starting before facility readiness and handover. Hardware timing can also depend on general availability and the selected configuration.
What is the pricing for AWS AI Factories?
AWS does not publish a standard price in its current FAQ. Pricing depends on deployment location, size, accelerator and service choices, and the customer’s infrastructure. AWS makes pricing available after a joint assessment, so enterprises need a deployment-specific quote to compare the Factory with other options. The same assessment is the appropriate point to clarify the actual service terms and SLAs for the proposed configuration.
How should an enterprise decide whether the model fits?
The Factory is worth evaluating when an organization needs dedicated AI infrastructure at a particular site and wants AWS to manage the integrated environment. It is not a shortcut around facility readiness or a substitute for defining how sovereignty will be demonstrated for a particular jurisdiction.
- Start with the data boundary: Document where training data, prompts, outputs, models, logs, and backups must remain, then identify whether any proposed regional integrations affect those requirements.
- Match hardware to workload: Specify training, fine-tuning, or inference demands and ask AWS to confirm exact accelerator, model, and service availability. Do not assume every listed option or combination is available in every deployment.
- Validate operational controls: Review who can access accounts, who can operate infrastructure, what audit and key-management choices apply, and whether any local-personnel conditions can be supported.
- Check site and economics together: Include power, space, connectivity, configuration, and bespoke pricing in the assessment rather than comparing accelerator costs alone.
- Review commitments for the actual build: Confirm delivery assumptions and service-specific SLAs for the selected design before relying on them for a production plan.
AWS says its approach may accelerate AI infrastructure buildouts “by months or years” compared with independent buildouts, but that is the company’s stated potential benefit, not a published universal result or an independently verified performance benchmark. AWS’s launch announcement does not establish a standard deployment outcome or performance figure.
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