Amazon Web Services is pushing deeper into on-premises AI infrastructure with the launch of AWS AI Factories, a new model that brings dedicated AWS AI stacks directly into customer data centers. The offering targets enterprises and public sector organizations that want the performance, ecosystem, and operational model of AWS, but with the physical control, data locality, and regulatory alignment of their own facilities.
The concept responds to a growing reality: building a high-performance “AI factory” from scratch is far beyond what most organizations want to take on. Standing up large-scale AI infrastructure demands not just GPUs and racks, but a tightly integrated stack of management, networking, storage, databases, observability, and security tooling. On top of that, customers must navigate model licensing, framework choices, and compliance requirements across multiple jurisdictions. The result is often multi-year timelines, ballooning capital expenditure, and significant architectural risk.
AWS AI Factories aim to compress those timelines by delivering a pre-engineered, full-stack environment on customer premises, operated exclusively for that customer. Functionally, each deployment behaves like a private AWS Region: it exposes familiar AWS APIs and services for compute, storage, databases, and AI, but the hardware sits inside the customer’s data center and runs within their power, cooling, and physical security envelope. This design lets organizations reuse existing space and power contracts while still tapping into AWS’s latest AI chips and managed services.
Under the hood, AI Factories combine several building blocks. At the silicon layer, they support both NVIDIA’s latest accelerated computing platforms and AWS’s own Trainium family of AI chips, giving customers options for training and inference depending on performance and cost profiles. These are tied together with AWS high-speed, low-latency networking technologies and backed by high-performance storage and database systems designed to feed data-hungry workloads. On top of that sits the AI services layer, including Amazon Bedrock for managed foundation models and Amazon SageMaker for model building, training, and deployment.
One of the selling points for regulated industries and governments is the ability to access leading foundation models and AI tools without having to negotiate separate contracts with each model provider. Because AI Factories can expose Bedrock and other managed services internally, customers get a catalog of third-party and proprietary models within their own environment while keeping data processing and storage confined to the boundaries required by security and sovereignty rules. This is particularly relevant for organizations constrained by data residency laws, sector-specific regulations, or national security classifications.
AWS is positioning AI Factories as the product of nearly two decades of cloud operations experience and large-scale AI system design, arguing that it can deploy secure and reliable AI infrastructure faster than most organizations could architect and build themselves. That pitch is reinforced by its long-standing partnership with NVIDIA, which AWS credits as foundational to its GPU-based offerings since the first GPU cloud instance launched 15 years ago.
In the AI Factory context, the AWS–NVIDIA collaboration means customers can access the full NVIDIA accelerated computing platform, including the latest Grace Blackwell and future Vera Rubin architectures, alongside NVIDIA’s AI software stack and ecosystem of GPU-accelerated applications. These capabilities are integrated with AWS infrastructure technologies such as the Nitro System, Elastic Fabric Adapter for petabit-scale networking, and Amazon EC2 UltraClusters. Looking ahead, AWS plans to support NVIDIA NVLink Fusion high-speed chip interconnect in next-generation Trainium4 and Graviton processors and within Nitro, with the goal of further improving performance for tightly coupled AI workloads.
NVIDIA frames the initiative as meeting the need for a full-stack approach to large-scale AI, where GPUs, networking, system software, and cloud services are jointly optimized. From a customer perspective, this integration is meant to reduce the time spent on low-level integration and cluster engineering, allowing internal teams to focus more on data, models, and applications.
The public sector is a major target. AWS says AI Factories are being engineered to meet its highest security standards, including support for workloads across classification levels from Unclassified through to Top Secret. By deploying AWS-operated infrastructure on national soil, governments can pursue AI modernization programs with stronger assurances around control, availability, and compliance, while still benefiting from commercial innovation in chips, networking, and AI services.
One of the most visible early deployments will be in Saudi Arabia, where AWS and NVIDIA are collaborating with HUMAIN, a locally based company building full-stack AI capabilities. AWS plans to build a dedicated “AI Zone” within a HUMAIN data center, with up to 150,000 AI chips, including GB300 GPUs, along with AWS AI infrastructure and services. HUMAIN describes the project as the start of a “multi-gigawatt journey” focused on serving both domestic and global AI compute demand. The partnership is pitched as combining AWS’s experience in building infrastructure at scale with HUMAIN’s regional focus and ambition to create an AI ecosystem that can support innovation well beyond its home market.
For B2B technology leaders, AWS AI Factories highlight a broader trend: hyperscale cloud is no longer an exclusively off-premises story. As AI becomes more central to national strategies and core industry processes, the boundary between cloud and data center is blurring. AWS’s bet is that many organizations will want the convenience of its managed AI stack and hardware roadmap, but delivered in a form that respects local control and sovereignty constraints.
Executive Insights FAQ
How does an AWS AI Factory differ from a traditional on-premises AI cluster?
An AWS AI Factory is delivered as a fully managed, private AWS environment inside the customer’s data center, exposing AWS APIs, services, and AI tools rather than a collection of standalone servers and GPUs that the customer must integrate and operate themselves.
What role does NVIDIA play in AWS AI Factories?
NVIDIA provides the accelerated computing platforms, AI software stack, and ecosystem of GPU-accelerated applications that underpin much of the compute layer, integrated with AWS networking and infrastructure technologies to support large-scale training and inference.
How do AI Factories help with data sovereignty and regulatory requirements?
Because the infrastructure runs in the customer’s own facility and can be configured so data is processed and stored locally, organizations can align AI workloads with national or sector-specific rules on residency, classification, and access, while still using managed AWS services.
Can organizations use multiple AI models and providers within an AI Factory?
Yes. Through services such as Amazon Bedrock, customers can access a variety of foundation models and tools from different providers within the AI Factory, without negotiating separate contracts for each model, while keeping interactions within their controlled environment.
Who is the primary audience for AWS AI Factories?
The offering targets large enterprises, regulated industries, and government entities that need high-performance AI infrastructure, want to leverage AWS’s ecosystem and operational model, but must retain physical control, strong security assurances, and compliance with local or national requirements.
