AI factories are environments specifically designed to train, deploy and operate AI models at scale. They differ from traditional data centers in that they’re optimized for continuous AI workloads by combining accelerated compute, data pipelines, orchestration and security in a unified system. 

Fortanix Armet AI provides the security layer for AI factories by protecting data and models while they’re actively in use. It enables confidential AI workflows backed by hardware isolation and cryptographic controls, allowing organizations to securely run sensitive AI workloads.

AI factories typically separate training and inference pipelines to optimize performance and cost. Training workloads focus on large-scale, batch compute, while inference should be optimized for low-latency, continuous execution.

Not all AI factories require confidential computing, but it’s essential when models work with sensitive, regulated, or proprietary data. Confidential computing ensures that the data and the models themselves are protected even during processing, not just when the data is at rest or in transit.

AI factories support a range of workloads, including data preparation, model training, fine-tuning, inference, model evaluation, and monitoring. They often also host supporting services such as MLOps, observability, and governance tools. 

No. Large language models are certainly a common use case, but AI factories can also support things like computer vision, recommendation systems, predictive analytics and domain-specific models for various industries. 

AI factories provide the infrastructure needed to train, fine-tune, and run GenAI models at scale. This helps organizations operate GenAI continuously while maintaining performance, governance, and security controls.

The main industries currently benefiting most include government, financial services, healthcare, telecommunications, manufacturing and research. Organizations in these sectors are often looking for scalable AI performance along with strict data governance and compliance. 

AI factories protect proprietary models through isolation, encryption and controlled access to model weights. Techniques such as confidential computing help ensure that models can’t be inspected, copied or tampered with, even during execution. 

In traditional environments, cloud operators might have privileged access to infrastructure. But the beauty of AI factories using confidential computing is that data and models can be cryptographically isolated so that even infrastructure operators cannot access them. 

Confidential computing is the technology that enables AI workloads to run within hardware-enforced, trusted execution environments—literally a physical component on modern CPUs and GPUs. This allows data and models to remain encrypted and protected as they are processed within an AI factory. 

Encryption keys are typically managed with a centralized key management system and strict policy controls. In secure AI factories, keys are released only for verified workloads, typically via cryptographic attestation. 

AI factories can be deployed on-premises, in the cloud or across hybrid environments. The choice depends on the organization's specific needs when it comes to performance, data sensitivity and regulatory requirements. 

Yes. Many organizations today deploy AI factories across hybrid environments so they can combine on-prem or sovereign infrastructure with cloud-based resources, all while maintaining consistent security and governance. 

A sovereign AI factory is an environment in which data, models and workloads remain under the control of a specific organization or nation. The idea is to enforce data residency, governance and legal jurisdiction requirements. 

Governments use AI factories to support national AI initiatives, public services, defense, healthcare and research. These environments are attractive to governments because they allow them to adopt and roll out AI while maintaining control over sensitive national data. 

Yes. AI factories can be designed to ensure that data and models never leave specific geographic or legal boundaries, meaning organizations can meet data residency and sovereignty regulations.

As AI production ramps up, enterprises need infrastructure that delivers predictable performance, scalability and top-class governance. AI factories are designed to soften the infrastructure burden associated with adoption and support long-term AI strategies.

AI factories aren’t a “must” for every use case, but they’re becoming a key component for organizations that run AI at scale. As AI becomes a core business operation, infrastructure that’s purpose-built to handle it will become increasingly important. 

AI factories enable organizations to iterate on models, lower operational friction and achieve more reliable AI performance. Over time, this translates into better decision-making, automation and competitive advantages. 

Not necessarily, but it’s important to understand that AI factories and ML platforms serve different purposes. ML platforms are all about tools and workflows, while AI factories serve as the underlying infrastructure to reliably operate AI at scale. 

Since AI factories centralize and optimize AI workloads, they reduce inefficiencies, improve the utilization of accelerated compute, and, ultimately, lower the cost of AI output over time compared to stitched-together infrastructure. 

Yes, but only when implemented correctly. AI factories are meant to improve consistency, performance and reliability, which directly impacts model quality and business results.

Not really. While they may physically resemble data centers, AI factories are architected specifically for AI workloads, with different assumptions about compute, data flow and security. 

Organizations running continuous, large-scale or sensitive workloads benefit most from AI factories, which include enterprises, governments and research institutions where AI is mission-critical. 

Risks include the centralization of sensitive data, new and expanded attack surfaces and increased operational complexity. But these risks can be mitigated with strong governance, isolation and security controls. 

Building a sovereign AI factory means selecting trusted infrastructure, enforcing data residency, implementing strong encryption and access controls, and using tech like confidential computing to protect data while it’s in use. 

No. A supercomputer focuses on raw compute performance, while an AI factory includes orchestration, data pipelines, security, governance and the tooling needed for production-grade AI. 

Core components include accelerated compute, high-speed networking, data pipelines, AI platforms, observability tools and security layers such as encryption, key management and confidential computing.