Large cloud operators may be becoming a more attractive option for organizations seeking new infrastructure, according to Nutanix CEO Rajiv Ramaswami, who argues that hyperscale providers can often secure servers and components faster than traditional enterprise buyers.
Speaking about current market conditions, Ramaswami said cloud providers benefit from purchasing hardware in enormous volumes. Their buying scale allows them to negotiate directly with manufacturers and secure priority access to components such as memory and solid-state drives. As a result, some enterprises evaluating new infrastructure projects are finding that cloud-hosted bare-metal servers can be available sooner, and in certain cases at lower cost, than purchasing and deploying equipment in their own data centers.
The comments come at a time when organizations continue to face elevated hardware expenses. Memory modules and flash storage remain among the most expensive components in modern server deployments, contributing to overall infrastructure costs. According to Ramaswami, these pricing pressures are unlikely to ease in the near term, meaning enterprises may need to factor longer-term budget impacts into future technology investments.
For infrastructure teams, procurement decisions are increasingly shaped by two practical considerations: acquisition cost and deployment timelines. If a cloud provider can supply computing resources immediately while physical server orders require extended delivery periods, organizations may choose cloud deployment even when they have traditionally preferred on-premises environments.
However, Nutanix is observing a different pattern when artificial intelligence projects are involved. While some conventional workloads are moving toward cloud infrastructure, many businesses continue to deploy AI systems inside their own facilities. Ramaswami said predictable operating costs remain one of the primary reasons for this approach.
Many organizations are still attempting to determine whether AI initiatives generate measurable financial returns. While interest in AI remains high across industries, businesses are increasingly scrutinizing infrastructure spending associated with model training, inference workloads, and data processing. Operating AI infrastructure internally can provide greater visibility into hardware utilization and long-term costs.
According to Nutanix, practical AI applications currently dominate enterprise deployments. Document retrieval systems, knowledge search tools, automated summaries, and internal productivity assistants remain among the most common implementations. Ramaswami said Nutanix has recorded approximately a 10 percent improvement in service response times through AI-assisted operations, while software development teams have accelerated feature delivery by roughly 50 percent after incorporating AI-supported workflows.
The discussion also touched on evolving server architectures. Enterprise customers are increasingly evaluating smaller hardware footprints as they seek to reduce power consumption, rack space requirements, and operational expenses. Some organizations are also exploring Arm-based processors, which have attracted attention because of their energy-efficiency characteristics.
Despite growing industry interest in Arm, Nutanix does not currently see sufficient customer demand to justify a full migration of its software platform. Ramaswami noted that many open-source technologies used throughout the Nutanix ecosystem, including Kubernetes and the KVM hypervisor, already support Arm processors, potentially simplifying future development efforts if adoption accelerates.
The CEO’s comments coincided with Nutanix’s third-quarter fiscal 2026 earnings announceme
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