For over 20 years, virtual machines (VMs) have enabled IT teams to standardize and scale their systems. In the past decade, containers have become the de facto way to build and run applications. Today, many find themselves at a crossroads: build a whole new team to manage their growing number of cloud native applications while their existing team manages efficient VMs or do they find a way to unify these two worlds?
The rise of AI may be driving a wedge between VMs and containers or bringing them together like never before. It’s clearly supercharging container adoption and inspiring some to avoid running certain cloud native workloads on a hypervisor. They want to plug directly into bare metal GPU performance.
“We're moving from cloud native to AI native, and the companies that understand this shift will define the next decade of enterprise IT,” Rob Enderle, principal analyst at Enderle Group, told The Forecast in a story exploring The Arc of Cloud Native Transformation.
There’s a growing need for managing AI performance and efficiency. It’s forcing IT teams to move beyond juggling separate management systems for their vast VM base and growing containerized applications, pushing them toward a unified, single control plane for both environments, according to Steve Carter, director of product marketing at Nutanix.
“While the industry spent a decade or more moving workloads into VMs, the massive compute requirements of AI has many eager to tap into raw power of bare metal for certain needs.”
This is giving rise to what Carter calls “dual native.”
“It’s the ability to run Kubernetes on virtual machines and on bare metal systems, and manage them as part of a single platform,” Carter said.
He said what sets Nutanix apart is its dual native ability to unify both environments, giving IT teams ultimate flexibility without the usual infrastructure trade-offs. He described a dual native-capable IT platform like a shipping hub that sends packages by ground or air depending on urgency and cost. IT departments can route containerized workloads to VMs or bare-metal servers, where those workloads run without a virtualization layer.
Carter said that having a single platform that can switch between both environments offers several benefits.
"With dual native architecture, you can be intentional about the infrastructure that you use,” he said.
"You get the operational efficiency of virtualization for most standard deployments. Or you can support AI, ML, and other resource-intensive workflows that require constant access to GPU hardware by running containers on bare-metal servers rather than on hypervisors.
“It's the best of both worlds."
AI Infrastructure and Application Expansion
Like they’ve done through other transformative times, Carter said organizations want to maximize their investment while simplifying growing IT complexity and security requirements. Many will prioritize investment in AI capabilities that are flexible and can evolve over time.
McKinsey projects that the IT capacity needed for AI and non-AI workloads could almost triple by 2030, with AI capacity increasing 3.5 times and making up roughly 70% of the total.
By default, most investments in IT resources will flow into cloud native technologies, since about 85% of global IT executives in this year's Enterprise Cloud Index (ECI) survey said they expect app containerization to increase, largely driven by AI.
“Containers in general and Kubernetes in particular are the de facto standard for developing and deploying new applications,” said Dan Ciruli, vice president and general manager for cloud native with Nutanix.
"As a developer, it's so much easier to put your app in a container,” Ciruli said. “It makes testing and deployment really, really simple.”
Up to 95% of respondents to the ECI survey said their Kubernetes clusters run on VMs. Even cloud service providers such as AWS, Azure, and Google Cloud run Kubernetes on virtual machines by default because it delivers strong isolation and operational consistency at scale.
“Using Kubernetes in the cloud is solved,” Ciruli said. “But using Kubernetes on-prem can still be difficult.”
This led Ciruli and his team to build a single control plane that runs Kubernetes anywhere. The Nutanix Kubernetes Platform (NKP) simplifies microservices management in on-prem data centers, in the cloud and on edge devices.
“NKP Full Stack lets enterprises run any combination of virtualized and containerized applications,” Ciruli said.
For most enterprises, running cloud native applications in VMs, containers or bare metal is about choice and flexibility, Carter explained.
“Virtualization is the standard used by cloud providers,” he said. “That’s what powers their Kubernetes offerings. People increasingly want to run some of their specialized containers on bare metal. They want direct GPU access when lower latency matters most.”
The ability to manage across different IT infrastructures is becoming more critical as AI infrastructure and applications scale and evolve alongside existing systems.
"You don't have to compromise," Carter said. "You don't have to choose to virtualize containers or not to. You can do both depending on the scenario."
Editor's note: In this video with HyperFRAME Research, Steve Carter describes Why Nutanix is betting on Bare Metal for AI Workloads. Learn more about Nutanix Enterprise AI and Nutanix Kubernetes Platform solutions.
David Rand is a business and technology reporter whose work has appeared in major publications around the world. He specializes in spotting and digging into what’s coming next–and helping executives in organizations of all sizes know what to do about it.
Ken Kaplan contributed to this story. He is Editor in Chief for The Forecast by Nutanix. Find him on X @kenekaplan and LinkedIn.
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