Nutanix Unveils ‘Sovereign Clouds’ For Distributed Infrastructures

Sovereign cloud architectures are ‘becoming a defining priority for enterprises,’ Nutanix said.

Nutanix, a provider of hybrid multi-cloud computing solutions, announced Monday expanded capabilities in its Nutanix Cloud Platform (NCP) offering.

The new features will allow its customers to manage sovereign cloud infrastructure – ones that must adhere to stringent data privacy and other mandates—across distributed environments running “traditional, modern and AI applications, including fully disconnected environments,” Nutanix said in a news release.

Sovereign cloud architectures, according to Nutanix, are “becoming a defining priority for enterprises.”

“We’re introducing several enhancements to the Nutanix Cloud Platform that help customers meet these needs without giving up the advantages of a distributed cloud infrastructure,” said Thomas Cornely, executive vice president of product management, Nutanix, in a news release. “These new capabilities give customers the clarity and control needed to draw their own sovereign boundaries across distributed environments and leverage the resiliency and flexibility that distributed clouds provide,” he added.

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Details on the new features include:

• Orchestrated lifecycle management of multiple dark-site environments
• Nutanix Central can now run in customer-controlled on-prem environments as soon will Nutanix Data Lens
• Federal U.S. agencies can build and operate a distributed sovereign cloud on Amazon Web Services using the Nutanix Government Cloud Clusters (GC2 solution)
• Tiered disaster recovery options
• Nutanix Infrastructure Manager, a new automation tool designed to streamline the deployment of distributed environments.

Last month, Nutanix announced its Nutanix Enterprise AI (NAI) solution. It also announced that it had expanded its partnership with AWS. The collaboration will allow Nutanix customers to “leverage Nutanix Cloud Clusters (NC2) on AWS,” Nutanix said in a news release.

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NAI is a hybrid multicloud environment for AI workloads. It can be deployed in several scenarios: on Kubernetes, at the edge of a network, in the datacenter, and in public clouds including Amazon Web Services’ Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS), and Google Kubernetes Engine (GKS).