AI Factories: What Are They and Who Needs Them?

Businesses now view AI environments as critical to competitive advantage. According to a forecast by Gartner, AI infrastructure investment is expected to reach$202 billion by 2025. As organizations shift from pilot projects to real-world deployments, they’re learning that legacy infrastructure can quickly become a bottleneck. AI workloads need expensive GPUs to operate, as they are required to: 

• Run with predictable latency,  often within distributed and/or self-scaling architectures for load tolerance and high availability

• Consume and produce business-critical data and insights, meaning security and data (and often model) sovereignty is essential

In turn, this means AI-oriented datacenters aren’t like regular datacenters or commodity clouds. They’re specialized high-performance computing environments with features like Remote Direct Memory Access (RDMA), high-speed networking (enabling GPUs on different machines to share memory), and stacks of complex middleware to enable GPU sharing with processes on the host, inside VMs, and running in containers. 

Building such environments is a tall order, and orchestrating services and applications on top of them is even more complicated. Platforms like k0rdent Enterprise and k0rdent AI from Mirantis (see below) are purpose-matched to the challenge of creating and operating stacks ‘from metal to model’, dealing with the specialized infrastructure across many environments (clouds, bare metal datacenters) and abstracting it upward so that apps can use the capacity seamlessly.

Above ‘AI PaaS’ platforms, like k0rdent, is where the work of building infrastructure happens. AI factories are emerging as a way of using the capacity of optimized and abstracted cloud frameworks and so-called ‘Neoclouds’ to treat intelligence as an industrial product. Factories integrate every stage of the lifecycle, transforming data into real-time predictions and insights at scale. 

Let’s explore what AI factories are, how they work, and why enterprises invest in them to power the next wave of intelligence.

What Are AI Factories?

NIVIDIA CEO Jensen Huang recently described AI factories as treating AI development like an industrial process: raw data is the input, advanced computation the assembly line, and trained models or real-time AI services the output.

AI factories integrate all stages of AI production under one roof:

• Ingesting and storing massive datasets

• Training machine learning models

• Fine-tuning models

• Deploying models for high-volume inference

Crucially, they enable a continuous loop: production AI models generate new data from user interactions or sensors, which is fed back in, improving the next generation of models.

Key Characteristics of AI Factories

AI factories are not general-purpose IT platforms. They are built to run machine learning pipelines at production scale. That means handling large datasets, saturating high-performance compute hardware, and supporting continuous deployment and retraining. Design priorities for managing latency, throughput, fault isolation, and traceability are different from traditional enterprise systems.

AI factories support:

• An integrated notion of the AI lifecycle: Data ingestion, model training, validation, deployment, and inference are handled in a single environment. Artifacts and metrics persist across stages.

• Hardware and software specialization: GPU clusters, high-throughput storage, and scheduling frameworks are tuned for AI workloads, not generic computing.

• Intelligence as output: The goal is not to serve static applications, but to continuously produce and refine models that improve with use.

How Does an AI Factory Work?

An AI factory runs AI as a pipeline. Data comes in. Models are trained or refined. Outputs are served in production and generate new data, which loops back into training. Each stage must be automated, observable, and scalable.

Typical stages include:

• Data Ingestion and Preprocessing: Raw input from users, devices, or systems is collected and prepared using ETL tools, data lakes, and filtering logic. Quality and labeling matter.

• Model Training and Tuning: Deep learning models are trained on large datasets using distributed compute. This includes initial training and fine-tuning on task-specific data.

• Inference and Serving: Models are deployed behind APIs or service endpoints. Infrastructure supports scaling, load balancing, and low-latency response.

• Feedback and Retraining: Inference outputs are logged and analyzed. Edge cases and performance issues feed back into retraining pipelines.

• Automation and Orchestration: Tools like Kubernetes and MLflow manage jobs, schedule retries, monitor health, and enforce rollback policies.

Key Components of AI Factory Operations

AI factory infrastructure comprises a stack of software layers over complex hardware. The purpose of the stack (looking down) is to abstract orchestration layers, services, and organizational requirements and standards, and hardware capacity so that workload builders can focus on creating value. 

The business, meanwhile, gains assurance that all AI workloads are resilient, scalable, run within appropriate security guardrails, and are compliant with regulations, wherever they and the data they operate on reside. 

The AI Factory stack includes the following layers:

For implementation details, see the Mirantis AI Factory Reference Architecture.

The Compounding Benefits of AI Factories

AI factories deliver exponential value as usage grows. This is because inference workloads scale with demand, and feedback from those workloads feeds into continuous model improvement. Over time, every component of the system gets better, creating a flywheel of accelerating returns. 

Aggregate spending by global enterprises on GPUs is expected to grow at 35%/year (CAGR) through 2030, according to data from Grandview Research. Thus, a new focus has emerged on maximizing ROI per GPU. Organizations now track this metric closely to justify AI infrastructure investments and drive efficiency at scale.

Real-Time Inference at Enterprise Scale

A late prediction is a failure, even if it’s accurate. In production, inference happens inside user sessions, transaction flows, and systems that can’t wait. AI factories reduce latency by keeping models close to where they run. They use dynamic scaling to absorb unpredictable load, but that only works if cold starts are rare and queues stay short. Some workloads will still need reserved capacity. Others fail fast under load and need circuit breakers. None of this works without real observability.

To support this, production systems typically implement:

• Regional model endpoints to minimize network round-trip times

• Autoscaling of inference nodes in response to real-time usage patterns

• Internal APIs with access controls and audit logging

• Load-aware routing to balance traffic and avoid cold starts

Continuous Model Improvement Through Feedback Loops

Production input always shifts. Sometimes gradually, sometimes overnight. Maybe it's a holiday, a broken upstream filter, or users doing something the training set never covered. If the model doesn’t adjust, it decays. AI factories collect live signals and feed them back. Not just for retraining, but to expose what's breaking. The goal isn’t to perfect the model. It’s to keep it in sync with the mess.

To support that, systems usually provide:

• Structured logging of inputs, predictions, and outcomes

• Retention of edge cases that fail confidence or performance thresholds

• Retraining pipelines that trigger on drift, not just on a calendar

Faster Time to Insight and Business Value

When training blocks deployment or testing takes weeks, projects stall. Teams delay decisions. Bugs go unseen. AI factories break that deadlock. They don’t remove risk, but they remove the waiting. You can train a model, test it, and ship it without queuing for another team’s slot or waiting for someone to write a deployment script. What matters is speed with visibility.

Systems that support this typically include:

• CI/CD pipelines that package models with their data and config

• Deployment jobs with guardrails that roll back on degraded performance

• Artifact tracking that ties each model version to the code and training run that produced it

Expanding Automation Across Critical Workflows

Most internal processes repeat. Sometimes they follow rules. Sometimes not. If the signal is strong enough, a model can step in. AI factories let teams drop models into live systems—approval queues, classification jobs, filters—and replace what used to be manual judgment. It’s not about scale first. It’s about not needing a human for every low-risk step.

What makes this work:

• Interfaces that let models run inside existing systems, not off to the side

• Logs that show what the model decided and why

• Fallbacks for when the output needs review or just isn’t trusted yet

Shared Infrastructure for Diverse AI Use Cases

Training runs and inference jobs need compute that can move fast and scale without notice. If every team builds its own stack, most of it sits idle or breaks under pressure. AI factories fix this by centralizing GPU access and tooling. Teams still work independently, but under one roof. What matters is keeping workloads isolated without wasting hardware.

This usually involves:

• Shared GPU pools with job queues and quotas

• Role and access boundaries that stop teams from interfering with each other

• System-wide logs and usage metrics for tracking cost and behavior

Improved Predictive Accuracy Over Time

Models drift. Slowly in some systems, suddenly in others. You don’t always see it until something fails—conversion rates drop, detection misses increase, or the model starts misclassifying edge inputs it used to get right. AI factories catch this early. They track what the model sees and how it performs, and use that data to keep models aligned with reality.

To do this well, systems often include:

• Online evaluation that pulls from real usage or synthetic tests

• Drift checks that trigger when inputs start to shift

• Retraining pipelines that use the most recent usable data

Greater Operational Efficiency With System Maturity

Early on, GPU jobs overrun, logs don’t line up, and some nodes idle while others throttle. As factories scale, those gaps turn into real costs. Fixing them isn’t just optimization—it’s survival under load. Teams tune queues, rework schedules, and cut dead weight from pipelines. The result isn’t perfect efficiency. It’s a system that holds together when traffic spikes or models get heavier.

What this usually looks like in practice:

• Job schedulers tuned to pack GPUs without starving critical workloads

• Cost tracking that attributes usage to teams or services, not just a global pool

• Tracing that spans model training, serving, and the data in between

How Is an AI Factory Different from a Traditional Data Center?

AI factories aren’t built for general IT. They don’t run office software, internal websites, or business apps that sit idle half the time. They exist to push hardware hard—saturating GPUs, moving massive volumes of data, and keeping models in memory as long as the power holds. 

That shifts the design. Uptime matters, but so does throughput under pressure. Scheduling isn’t just about fairness; it’s about not wasting $100,000 worth of compute on stalled jobs. You need fast networking, observability that catches failures early, and automation that can reset a cluster without needing to page someone at 3 a.m. 

This isn’t general-purpose infrastructure. It’s a system built to run unstable, high-intensity workflows at full tilt. The table below breaks down the key differences.

Why Enterprises Need to Adopt the AI Factory Model

As artificial intelligence becomes central to digital transformation strategies, organizations face growing pressure to operationalize it at scale. AI factories provide the architectural and operational foundation to meet this demand, helping enterprises gain a competitive advantage, unify infrastructure across environments, and future-proof their operations for continuous innovation and growth. 

Below are key reasons why factories are becoming critical to enterprise success.

Recent research confirms that a large majority of businesses have already embraced artificial intelligence. A 2024 McKinsey global survey found more than three-quarters of companies worldwide use AI in at least one business function. 

• Competitive Advantage Through Faster AI Delivery: Near-ubiquitous use of AI underscores why establishing “AI factories” can help enterprises rapidly deploy models and maintain competitive velocity, as organizations seek to leverage AI at scale for efficiency and innovation.

• Infrastructure Preparedness for AI-First Strategy: Building AI infrastructure industrializes AI development—standardizing environments for model training, inference, and reuse to support enterprise-wide deployment at scale.

• Flexibility Across Cloud, Edge, and On-Prem Environments:The AI factory model supports hybrid and multi-environment strategies, using unified management to span cloud, data center, and edge—meeting latency, cost, and sovereignty requirements.

• Centralized Control Over AI Governance and Compliance: Centralized infrastructure enables consistent security, compliance, and policy enforcement while maximizing GPU efficiency across teams.

• Future-Proof Foundation for Scaling AI Operations: Companies delaying AI integration may never catch up, according to analysts at PwC. AI factories offer the scale and adaptability now vital in sectors such as finance, automotive, and pharma.

Who’s Building AI Factories Today — And Why?

Rapid adoption of AI is changing how infrastructure gets built. Organizations are building specialized environments with dense GPU clusters, high-power racks, and software to manage the full AI lifecycle.

Different groups are doing this for different reasons. In Europe and Asia, some want to keep compute and data within national borders. Industrial firms are redesigning data centers to support AI-heavy operations. Telecoms are offering local AI capacity to enterprise customers. Hyperscalers are scaling up to train massive models and serve global workloads. 

The examples below show how and why these factories are taking shape.

• Nebul: A sovereign European AI cloud provider, Nebul, deployed Mirantis k0rdent AI to unify and simplify management of its complex, multi-tenant AI infrastructure—eliminating Kubernetes cluster sprawl, streamlining operations, and accelerating its shift to Inference-as-a-Service.

• Schneider Electric (Global): In partnership with NVIDIA, Schneider Electric is deploying AI-ready data center architectures capable of supporting up to 132 kW per server rack. Their designs aim to cut cooling energy use by 20% and reduce deployment time by nearly 30%—a significant boost for enterprises running large-scale AI workloads.

• Yotta Data Services (India): Yotta’s Shakti Cloud platform—built with NVIDIA DGX systems—offers sovereign, on-prem GPU infrastructure across multiple Tier IV-certified data centers. With over 9,000 NVIDIA GPUs committed to the India AI Mission, Yotta enables enterprises and government entities to train and deploy LLMs locally.

• Telenor (Norway): Telenor has launched Norway’s first AI factory—a sovereign, secure AI cloud service powered by NVIDIA GPUs. It supports enterprises like Hive Autonomy and Capgemini, offers sustainable compute, and enhances regional AI adoption.

• Hyperscalers & U.S. Tech Firms: Major U.S. tech firms—including Amazon, Google, and Meta—are building AI-optimized data centers alongside hyperscalers. Meta alone plans multi-gigawatt AI clusters as part of its Prometheus and Hyperion projects, while Amazon’s “Project Rainier” spans over 1,200 acres and is intended to support advanced AI model training for Anthropic.

Build Your AI Factory with k0rdent AI

Organizations looking to operationalize artificial intelligence at scale need a platform that unifies infrastructure, orchestration, and lifecycle management. k0rdent AI delivers exactly that: a Kubernetes-native solution for deploying, managing, and scaling AI workloads—whether on premises, in the cloud, or at the edge.

With k0rdent AI, teams can implement AI inference best practices, automate model deployment pipelines, and enable inference-as-a-service across departments. The platform supports hybrid environments with robust MLOps tooling and tight integration with leading accelerators like NVIDIA, making it ideal for enterprises investing in artificial intelligence and machine learning at scale.

Whether you’re modernizing an existing stack or building AI infrastructure from the ground up, k0rdent AI gives you the tools to launch and grow AI factories—securely, efficiently, and without vendor lock-in. 

Book a demo today and see how Mirantis can help your enterprise scale AI workloads, simplify orchestration, and quickly deliver real-time intelligence.