Seasoned techies recall with nostalgia a simpler time when the “single-stack” model of enterprise computing held as the default approach to solving interoperability headaches. Back then IT decision-makers were prepared to forfeit some choice and flexibility in how they specified IT systems in return for the availability and manageability that came with a homogenous operating environment. With the subsequent rise of multi-clouds, application-specific hardware, ever-changing business requirements, and the race for AI, the old “one-size-fits-all” playbook has been largely abandoned.

Most IT estates have embraced technological diversity, some by design, some through standardization, some by inertia. Modern enterprise environments have become innately heterogeneous – and innately complex. They run a diverse combo of system software, hardware devices, cloud services, legacy applications, et al. The advance of AI has brought standard CPUs, specialized GPUs, fast networking fabrics, distributed data pipelines, and new deployment models that span private clouds, public clouds, edge – and ever-pressing ROI expectations.

But that doesn’t mean that companies’ existing applications and data simply disappear. One trying but inevitable outcome of rapid AI deployment is the so-called “VM-container divide”. This is where critical business applications tipped to transition to containers continue to run on virtual machines, because cloud-native services are taking precedence, nabbing container and Kubernetes availability. So companies face the challenge of operationalizing AI, without being forced into complex migrations or creating new infrastructure silos.

Dealing with this technological schism poses acute challenges for IT management. Many new AI deployments are going straight to containers, while existing data and applications – which the AI needs to access – continue to run on VMs.

Running separate stacks for VMs and containers creates a number of issues, such as duplicate capacity plans, separate support contracts, and fragmented governance. And after years of trying to get beyond the pitfalls of siloed data, exasperated enterprises are now having to deal with siloed infrastructure. Separately managed VM and container environments often mean inefficient resource utilization. This in turn can lead to “stranded capacity” – installed computing resources that cannot be used to support live workloads.

VM clusters are frequently underutilized while container pools (like Kubernetes) are overprovisioned, with no easy way to share resources without manual intervention. Furthermore, VM/container infrastructure fragmentation can create an operations bottleneck for agentic AI. Infrastructure fragmentation can complicate the deployment, integration, governance, and scaling of agents thus making it harder to operationalize and scale agentic AI securely and consistently.

When AI agents cannot autonomously communicate across environments, their utility droops from autonomous problem-solvers to plain localised macros. Containers are gaining because of the widespread embrace of AI-enabled applications. Eighty-five percent of IT executives polled by Nutanix’s most recent Enterprise Cloud Index survey think that AI is accelerating their organization’s adoption of containers in a meaningful way; 29 percent think it is speeding-up adoption “to a great extent”. “AI is accelerating the shift to containers because machine learning models require strict dependency packing, while critical applications and data remain spread across both virtualised and containerised environments,” says Harsha Kotikela, Senior Director of Product & Solutions Marketing at Nutanix. Agents are multi-tier by nature he explains.

“Inference is typically GPU-intensive and container-based, while agents are often CPU-intensive and container-based, and existing enterprise applications and data largely remain in VM environments. The challenge is to bring these tiers together so agents can work with enterprise applications and data, without forcing migration or creating additional infrastructure silos.”

The application landscape, meanwhile, has become just as diverse. Many organisations simultaneously operate traditional enterprise applications running on VMs, containerised apps, distributed AI applications and intelligent agents, with workloads deployed across private cloud, public cloud, and edge. Each environment has its own management tools, operational processes, security rules and governance models. “Management capability has struggled to scale up commensurately,” says Kotikela, but adding more consoles or building separate operational teams for every technology stack does not necessarily solve this complexity: “In many cases, it increases it.”

However, Kotikela challenges the notion that the predicament enterprises face in straddling the VM-container divide is intractable. Rather, the challenge is to move towards a unified operating model that allows them to operationalize agentic AI while retaining flexibility across their existing and new environments. “Rather than trying to work around heterogeneity, the objective now should be to make heterogeneous environments operate consistently with purposeful policies,” Kotikela says. “This is where Nutanix's dual-native architecture comes in, enabling enterprises to operationalize agentic AI across container-based inference and agent workloads alongside existing VM-based applications and data, through a consistent operating model.”

The reality is that the VM-to-container migration trend is sluggish, Kotikela suggests. Few enterprises are starting from scratch with containers.

Many have long-established business applications on legacy architectures, on top of which they are building container-based applications, but mostly not to replace what they already have running on VMs. Rather, they are prolonging their VM investment. It’s customer interest that’s informed how Nutanix provisions a governed, dual-native architecture through its Nutanix Cloud Platform – NCP – which runs traditional VMs and containerised/Kubernetes-based AI workloads concurrently on a single, consistent infrastructure.

NCP’s hybrid-cloud infrastructure foundation unifies compute, storage, virtualisation and management across on-premises resources, public clouds and edge. It works in concert with Nutanix Enterprise AI (NAI), which provides the software capabilities to deploy, govern, and scale AI workloads.

Technology partnerships are vital to building AI support functionality into Nutanix's AI portfolio, explains Kotikela. “Nutanix and NVIDIA work together to provide an integrated platform for enterprise AI, combining Nutanix’s hybrid-cloud infrastructure and software with NVIDIA’s accelerated computing and AI software. Nutanix Enterprise AI simplifies the deployment, management, and secure operation of AI models, integrating with NVIDIA NIM and NeMo microservices to support enterprise AI inference and agentic AI workflows.”

Meanwhile, “Nutanix Kubernetes Platform provides the foundation for managing Kubernetes environments, while Nutanix Unified Storage delivers the high-performance data access required by AI workloads. Together, these capabilities help enterprises deploy and scale AI while maintaining control over their infrastructure, models, and data.”

Stacks of opportunity “In an ideal IT world you do not want to have to manage two different operational stacks – but the world of IT is rarely ‘ideal’,” Kotikela points out. “So you have to approach the challenge from a realistic perspective. You cannot bend your current stacks to a different reality without the risk of something breaking, and there’s no sense in that.”

He adds: “A platform that unifies VMs and containers improves developer time-to-value by getting rid of those infrastructure silos, and providing a single control plane.”Rather than development teams having to manage separate pipelines for existing VMs and containerised applications, a unified platform “treats both as services of equal status and importance”.

NAI delivers a unified and secure platform to deploy, manage and scale AI workloads, Kotikela says. It gives enterprises governance over their agents and models, full visibility over token usage, and streamlines AI development with a straightforward interface that integrates with existing infrastructure.”Giving agents agency According to Kotikela the next phase of agentic AI will be measured by the ability to operate agents with governance, visibility and control: “That’s what we set out to solve with the newest NAI release. Release 2.8 continues to evolve NAI with new control and governance capabilities across Nutanix Agent Gateway and Nutanix Private Inference. This means organisations can run AI across VMs and containers, bridge public-cloud LLMs with private self-hosted models, and deploy AI where data already is.”

Kotikela adds: “When I speak to customers, they tell me they want the freedom to choose between frontier and open-source models based on performance and cost, as well as the flexibility to run AI workloads in the cloud for experimentation and move them on-premises when they need greater control over their data or have sovereignty requirements.”Stitch in time For Kotikela the biggest challenge going forward is “operationalizing agentic AI - bringing together all the components required to make it work - from GPUs and underlying infrastructure to Kubernetes, AI models, inference engines, and gateways. These components must work together seamlessly across cloud, on-premises, and edge environments. Integrating and operating this multi-tier architecture requires significant time and effort from developers, IT architects, and infrastructure teams, diverting resources from delivering business outcomes.”

This is where Nutanix delivers value, says Kotikela. “We’ve done the heavy lifting of bringing together the infrastructure and software components required to run Agentic AI - within a unified operating model across hybrid environments. This allows IT teams to focus less on integrating and managing the underlying stack and more on operationalizing AI to deliver business value.”Find out more about from Nutanix at NVIDIA GTC Berlin at booth #7042 Sponsored by Nutanix