# Nutanix built $20m AI cluster to reduce use of Copilot and Claude, expects ROI in a year > Source: > Published: 2026-08-27 05:21:19+00:00 # Nutanix built $20m AI cluster to reduce use of Copilot and Claude, expects ROI in a year Source: [The Register](https://www.theregister.com)Also thinks Arm can give it a hand on the edge during RAMpocalypse Nutanix is closer to porting its stack to the Arm architecture, spurred by the high price of hardware and a desire to ensure customers can run its wares wherever they want to. CEO Rajiv Ramaswami today told The Register Nutanix customers are currently acutely aware of hardware costs and availability, issues that are slowing some purchases of its software. Nutanix is trying to avoid such delays by expanding its hardware compatibility list and supporting external storage devices – moves Ramaswami said mean users can migrate away from VMware without needing to replace hardware. He said Nutanix also continues to optimize the footprint of its stack and pointed to its decision to allow bare metal installations of its Kubernetes Platform as another hardware-minimization effort. The CEO today told The Register Nutanix now sees Arm support as another way to ensure its software platform can run wherever users need it, and believes that adopting the architecture will mean it can run on lower-cost hardware that users won’t balk at buying in the current climate. That’s an advance on Nutanix’s 2024 position on an Arm port as a worthy future consideration, but not an item on its development to-do list. AI support is, understandably, higher on that list and yesterday Nutanix delivered an update to its Enterprise AI suite that added a Model Context Protocol ( [MCP](/glossary/mcp)) gateway – a tool that sits between agents and MCP servers to provide identity management and security services to control the data and other services agents can access. MCP Gateways are quickly becoming standard issue in packaged AI infrastructure stacks, so Nutanix has made sure it’s on par with competitors. Another cost Ramaswami thinks his customers want to control is spending on tokens used in and produced by AI apps, and the company has dogfooded in this field by spending $20 million on its own AI infrastructure – a sum that he expects to recoup in a year “Our software teams have been using AI for coding across the lifecycle,” he told The Register, and initially used tools including[Copilot](/compare/github-copilot-vs-cursor)and[Claude](/glossary/claude). “Usage exploded and so did costs,” the CEO admitted. The company has therefore moved to using open[weight](/glossary/weight)models and an on-prem cluster. “We are no longer paying on a per-[token](/glossary/token)basis,” Ramaswami said. Some users occasionally use frontier models or external clusters, when necessary, an arrangement the CEO suggested is increasingly common as organizations match models and infrastructure providers to workloads rather than using LLMs for all AI apps. Ramaswami made his remarks about Arm and Nutanix’s on-prem cluster during a media briefing about the company’s Q4 results, which saw it win $757 million revenue, a 16 percent year-over-year jump. Full year revenue grew 12 percent to $2.85 billion. Net income remains modest – just $1.5 million for the full year. “Our fiscal 2026 results demonstrated a good balance of top and bottom line performance with,” said CFO Rukmini Sivaraman. “We remain focused on delivering sustainable growth and improving profitability.” Ramaswami pointed to the fact that Nutanix won 3,000 new customers in the financial year as evidence of growth and said many chose the company’s platform as an alternative to VMware. Nutanix will continue to win customers from VMware for another five years, he predicted. ®Get AI news in your inbox Daily digest of what matters in AI. ## Key Terms Explained Claude Anthropic's family of AI assistants, including Claude Haiku, Sonnet, and Opus. MCP Model Context Protocol (MCP) is an open standard created by Anthropic that lets AI models connect to external tools, data sources, and APIs through a unified interface. Token The basic unit of text that language models work with. Weight A numerical value in a neural network that determines the strength of the connection between neurons.