The shift toward AI has intensified the challenge of managing both virtual machines and containers within the same enterprise environments, creating operational friction for organizations seeking to modernize their infrastructure.
Industry observers note that the traditional "single-stack" approach to enterprise computing — where standardized systems offered simplicity at the cost of flexibility — has largely been replaced by heterogeneous architectures. This evolution, driven by multi-cloud adoption, specialized hardware, and AI workloads, has left many IT departments navigating a complex mix of legacy applications, cloud services, and emerging AI infrastructure.
A growing number of enterprises now operate across multiple environments, including private clouds, public clouds, and edge locations. These environments support diverse workloads, from traditional VM-based applications to containerized services and AI-specific components such as GPU-accelerated inference and agentic workflows. The result is a fragmented operational landscape where resource allocation, governance, and management tools often fail to scale uniformly.
One persistent issue is the coexistence of VMs and containers, sometimes referred to as the "VM-container divide." Critical business applications continue to run on virtual machines, while new AI initiatives increasingly deploy via containers, particularly within Kubernetes orchestration platforms. This split can lead to duplicated planning efforts, fragmented support contracts, and inconsistent governance models across infrastructure layers.
Resource inefficiencies are also common. In some cases, VM clusters operate below capacity, while container pools are overprovisioned, leading to stranded computing resources. Without mechanisms to dynamically share workloads between environments, utilization remains suboptimal, complicating efforts to scale AI operations.
Experts warn that infrastructure fragmentation may hinder the deployment and governance of AI agents — autonomous systems designed to perform complex tasks across environments. When agents cannot seamlessly access data or communicate across VM and container boundaries, their functionality may degrade from autonomous problem-solvers to isolated, limited utilities.
According to a recent survey by Nutanix, 85% of IT executives believe AI is accelerating container adoption, with 29% reporting significant acceleration. Harsha Kotikela, Senior Director of Product & Solutions Marketing at Nutanix, stated that AI workloads require tightly managed dependencies, while existing enterprise applications and data often remain tied to VM environments.
"AI is accelerating the shift to containers because machine learning models require strict dependency packing, while critical applications and data largely remain in VM environments," Kotikela said. "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."
Kotikela noted that modern AI architectures often involve multiple tiers — GPU-intensive inference workloads in containers, CPU-heavy agent processes, and backend applications on VMs — each requiring distinct management approaches. This complexity has led some organizations to adopt multiple operational stacks, which can increase rather than resolve operational overhead.
Rather than viewing heterogeneity as an insurmountable obstacle, Kotikela advocates for a unified operating model that enables consistent management across diverse environments. He emphasized the need for platforms that treat VMs and containers as services of equal importance, reducing the need for separate pipelines and governance frameworks.
Nutanix has responded with its dual-native architecture, designed to run both VMs and containerized AI workloads on a single, consistent infrastructure. The Nutanix Cloud Platform (NCP) supports hybrid and multi-cloud deployments, integrating compute, storage, and management capabilities across on-premises systems, public clouds, and edge locations.
The platform incorporates Nutanix Enterprise AI (NAI), which provides tools for deploying, governing, and scaling AI workloads. According to Kotikela, NAI enables organizations to run AI across VMs and containers, bridge public-cloud large language models with private self-hosted models, and deploy AI workloads where data resides.
Technology partnerships play a key role in this strategy. Nutanix collaborates with NVIDIA to integrate accelerated computing and AI software into its platform, including support for NVIDIA NIM and NeMo microservices. These integrations aim to streamline AI inference and agentic workflows while maintaining control over data and models.
Nutanix Kubernetes Platform and Unified Storage are also part of the stack, designed to support high-performance data access and Kubernetes management for AI workloads. Together, these components are intended to help enterprises deploy and scale AI while preserving infrastructure control.
Kotikela noted that most enterprises are not starting from scratch with containers. Many are extending existing VM-based applications with new containerized services, not replacing legacy systems but extending their lifespan. This incremental approach reflects broader trends in enterprise modernization, where risk mitigation and operational continuity often take precedence over disruptive overhauls.
The company positions its dual-native approach as a solution to the operational challenges of agentic AI — systems that require coordination across multiple layers of infrastructure. Nutanix argues that by unifying management under a single control plane, organizations can reduce the time and effort required to integrate and operate complex AI stacks.
According to Kotikela, the next phase of agentic AI will be defined by the ability to operate with governance, visibility, and control. The latest release of NAI (version 2.8) introduces enhanced capabilities in Nutanix Agent Gateway and Nutanix Private Inference, enabling organizations to manage AI workloads across environments while maintaining sovereignty over data.
He added that customers increasingly seek flexibility in choosing between open-source and proprietary models based on performance and cost, as well as the ability to shift workloads between cloud and on-premises environments based on data governance needs.
The broader challenge, Kotikela said, lies in operationalizing agentic AI at scale. Integrating GPUs, inference engines, Kubernetes, and AI models across cloud, on-premises, and edge environments demands significant coordination across development, architecture, and infrastructure teams — often diverting resources from core business objectives.
Nutanix claims its platform reduces this burden by pre-integrating the necessary components within a unified operating model. The company argues this allows IT teams to focus less on infrastructure integration and more on deploying AI solutions that deliver measurable business value.
For organizations exploring AI infrastructure strategies, the path forward appears to depend less on choosing between VMs and containers and more on building cohesive management frameworks capable of operating across both. As AI workloads continue to evolve, the ability to manage complexity without sacrificing agility may prove decisive.