Nutanix schedules .NEXT 2026 in Chicago
Nutanix released the program for .NEXT 2026 and scheduled the event for April 7–9, 2026, in Chicago. The company presented a lineup that combined executive keynotes, customer presentations, hands-on labs and educational sessions aimed at addressing current operational requirements and planning for future capacity needs.
The agenda framed session topics around enterprise Artificial Intelligence (AI), distributed data, modern IT and cloud native approaches, and said attendees would examine trends, practices and strategies related to running hybrid cloud environments, modernizing application platforms and accelerating AI adoption.
The program described specific technical themes including a consistent sovereign operating model across multicloud endpoints, a unified environment for containers and virtual machines, and a scalable compute and storage operating environment for AI factories, along with sessions on the adoption of recent AI hardware and software.
Nutanix listed keynote speakers Mark Rober and Kelsey Hightower and customer presentations from JetBlue Airways, Lockton and Purdue University, and it included hands-on labs, certification opportunities and expert tracks on AI in the enterprise; databases and business-critical applications; end user computing; Kubernetes and cloud native; management and orchestration at scale; migration to Nutanix; modern infrastructure for datacenter, edge and hybrid cloud; networking and security; and public and service provider cloud. The company also identified more than 100 sponsors, naming AMD, AWS, Cisco, Dell, Everpure, HPE, Lenovo, Microsoft, Omnissa and Supermicro as Platinum sponsors and Accenture and Thermal Control Subsystem (TCS) as Platinum system integrators, and said CEO Rajiv Ramaswami and other executives would present on cloud native applications, enterprise AI, the Cloud Platform and partnerships.
The press release included a single forward-looking statement noting that such statements speak only as of the date provided and involve risks and uncertainties that could cause actual results to differ.