Nutanix Enterprise AI

Research Note: Nutanix Enterprise AI

Nutanix recently introduced Nutanix Enterprise AI, its new cloud-native infrastructure platform that streamlines the deployment and operation of AI workloads across various environments, including edge locations, private data centers, and public cloud services like AWS, Azure, and Google Cloud.

Building on its GPT-in-a-Box toolkit, Nutanix Enterprise AI separates the inferencing components, creating a leaner solution for data scientists and enterprises seeking a consistent multi-cloud experience with AI.

Nutanix Enterprise AI

Nutanix Enterprise AI provides a consistent multi-cloud operating model, dramatically reducing generative AI application deployment. Unlike the original GPT-in-a-Box, Nutanix Enterprise AI excludes Nutanix’s infrastructure, Kubernetes platform, and storage components, leaving customers to supply their own.

Key Features and Innovations

  • Broad Kubernetes Compatibility: The Nutanix Enterprise AI platform works with any Kubernetes installation, providing flexibility to operate in multi-cloud and hybrid environments. It is also compatible with Amazon Elastic Kubernetes Service, Azure Kubernetes Service, and Google Kubernetes Engine.
  • Rapid Deployment: The new solution accelerates the deployment of generative AI applications, reducing setup time and simplifying operational workflows.
  • Simplified Infrastructure Management: Nutanix Enterprise AI provides automation to set up inference endpoints, download models, and manage secure deployments, allowing customers to use their preferred cloud or on-premises infrastructure.
  • AI Microservices and Open-Source Support: The platform includes built-in support for Nvidia’s AI Microservices, which enhances AI model deployment across various environments. Additionally, it supports open-source foundation models from Hugging Face, offering customers the flexibility to deploy a variety of LLMs.

Pricing Strategy

Nutanix has adopted a resource-based, subscription pricing model in which customers pay per CPU core and GPU used. This contrasts with traditional usage-based pricing and reflects an approach aimed at predictability and transparency, a move likely to appeal to enterprises wary of fluctuating cloud costs.

Analysis

Nutanix Enterprise AI is a versatile, cloud-native solution for organizations seeking a simplified, consistent infrastructure for generative AI deployment across their hybrid cloud infrastructure.

Decoupling the AI inferencing components from its traditional GPT-in-a-Box toolkit, Nutanix targets a broader audience of data scientists and enterprises looking to deploy generative AI applications with minimal infrastructure setup.

Enterprise AI’s resource-based pricing model—charging per CPU core and GPU rather than usage—reinforces Nutanix’s focus on cost predictability and transparency and should resonate with companies wary of variable cloud expenses.

The success of Nutanix Enterprise AI will hinge on its ability to deliver seamless, cost-effective performance in real-world scenarios. Competing against established cloud providers, Nutanix must continue emphasizing simplicity and reliability to differentiate itself. If it can maintain these benefits while scaling, Nutanix Enterprise AI has strong potential to become a cornerstone solution for AI deployments across industries.

As it does for more general-purpose workloads, Nutanix supports enterprises in harnessing the power of AI in a scalable, multi-cloud fashion. The new offering strengthens Nutanix’s footprint in the AI infrastructure market, responding to the needs of data scientists and enterprises by removing infrastructure-related complexities and fostering operational consistency. It’s a compelling and much-needed offering.

Competitive Positioning & Advice to IT Buyers

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Disclosure: The author is an industry analyst, and NAND Research an industry analyst firm, that engages in, or has engaged in, research, analysis, and advisory services with many technology companies, which may include those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.

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