Building Scalable AI Infrastructure with Mirantis AI Factory

/ AI, Cloud-Native, Infrastructure, Kubernetes
Building Scalable AI Infrastructure with Mirantis AI Factory

The News

Mirantis has introduced the industry’s first Kubernetes-native AI infrastructure reference architecture to simplify and accelerate enterprise AI and ML operations. The Mirantis AI Factory Reference Architecture, built on k0rdent AI, is designed to deploy secure, scalable, and sovereign AI workloads across cloud, hybrid, and edge environments.  

To read more, visit the original press release here

Analysis

AI development cycles are being compressed, yet developers are still burdened with fragmented infrastructure that’s not optimized for high-performance or data-sensitive AI workloads. As theCUBE Research highlights, the convergence of cloud-native paradigms and AI demand is accelerating the need for composable, declarative infrastructure stacks. Mirantis’ move endeavors to address this by enabling organizations to quickly provision and manage large-scale AI systems using templates, curated integrations, and GPU-aware orchestration. This comes at a time when demand for hybrid, sovereign, and performant infrastructure is surging across regulated industries and developer-led innovation teams.

The Mirantis AI Factory Reference Architecture could provide a foundational toolkit for developers and MLOps teams to deploy AI workloads within days instead of months. Built on Kubernetes-native tooling like k0rdent AI, the platform supports GPU slicing, RDMA networking, multi-tenant security, and high-throughput data access for workloads such as model training, fine-tuning, and inference. For developers, the goal is to access an end-to-end, infrastructure-as-code environment that removes the need to master complex hardware or specialized IT workflows. By abstracting infrastructure setup and aligning with open standards, Mirantis seeks to empower teams to focus on accelerating AI iteration cycles and improving production velocity.

Overcoming Traditional Infrastructure Barriers to AI Adoption

Historically, developers building AI workloads have faced friction from rigid infrastructure stacks lacking GPU orchestration, secure multitenancy, and efficient data flow. Many projects stalled due to the complexity of integrating storage, networking, and compute for large AI models, especially under data sovereignty or compliance constraints. Mirantis responds with a modular and declarative approach, simplifying tasks like configuring RDMA networks or scaling Kubernetes clusters for training supercomputers. This may be especially valuable in edge and hybrid environments where infrastructure sprawl and latency issues have historically hindered AI deployment.

A New Reference Model for AI-Native Workload Enablement

By introducing this composable reference architecture, Mirantis could set a baseline for how AI platforms should be built: secure, modular, developer-friendly, and optimized for rapid scale. Developers can reuse infrastructure templates to meet specific workload needs, whether deploying inference at the edge, running training jobs in hybrid clusters, or managing sensitive models under strict governance. Built-in integrations with NVIDIA AI Enterprise, Gcore, and others can allow rapid validation of production scenarios without having to rebuild the wheel. This reflects an evolution toward infrastructure as a code-based service for AI enablement, which is an essential shift in how developers interact with complex systems.

Looking Ahead

As AI adoption scales, the need for cloud-native, sovereign-ready infrastructure will likely continue to rise. According to industry research, by 2026 over 75% of enterprises will require sovereign AI capabilities to meet regulatory, privacy, and performance requirements. We hope to see Mirantis strategically positioned to address this shift by aligning Kubernetes-native infrastructure with AI workload patterns, especially in multi-cloud, edge, and regulated environments.

Going forward, we could expect to see Mirantis expand this architecture with dynamic workload placement, tighter CI/CD integrations for ML pipelines, and broader accelerator support. For developers building the next generation of AI applications, Mirantis’ AI Factory may present a tangible and repeatable path to operationalizing AI infrastructure, without sacrificing agility, control, or compliance.

Authors

  • Paul Nashawaty

    Paul Nashawaty, Practice Leader and Lead Principal Analyst, specializes in application modernization across build, release and operations. With a wealth of expertise in digital transformation initiatives spanning front-end and back-end systems, he also possesses comprehensive knowledge of the underlying infrastructure ecosystem crucial for supporting modernization endeavors. With over 25 years of experience, Paul has a proven track record in implementing effective go-to-market strategies, including the identification of new market channels, the growth and cultivation of partner ecosystems, and the successful execution of strategic plans resulting in positive business outcomes for his clients.

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  • With over 15 years of hands-on experience in operations roles across legal, financial, and technology sectors, Sam Weston brings deep expertise in the systems that power modern enterprises such as ERP, CRM, HCM, CX, and beyond. Her career has spanned the full spectrum of enterprise applications, from optimizing business processes and managing platforms to leading digital transformation initiatives.

    Sam has transitioned her expertise into the analyst arena, focusing on enterprise applications and the evolving role they play in business productivity and transformation. She provides independent insights that bridge technology capabilities with business outcomes, helping organizations and vendors alike navigate a changing enterprise software landscape.

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