Nutanix Extends Reach to Multiple Kubernetes Distributions
Nutanix today at the KubeCon + CloudNativeCon 2024 conference updated its hyperconverged computing platform for artificial intelligence (AI) applications, dubbed Nutanix Enterprise AI, to add support for any distribution of Kubernetes, including instances running on public clouds.
Additionally, Nutanix has allied with Amazon Web Services (AWS) to enable IT teams to deploy Nutanix Cloud Clusters (NC2) on a public cloud.
Thomas Cornely, senior vice president for product management for Nutanix, said these additional deployment options will make it simpler for application development teams to experiment with building applications in a virtual private cloud computing environment that might later be deployed in an on-premises IT environment because of compliance requirements or security concerns.
Many organizations are also now opting to deploy artificial intelligence (AI) applications in on-premises IT environments where much of their data already resides, noted Cornely. It’s too early to definitively say whether IT teams or AI specialists will manage the AI inference engines deployed in these environments, however, regardless of who is responsible, they will be increasingly relying on platform engineering principles to help compensate for a general lack of AI infrastructure expertise, added Cornely.
Nutanix has been making a case for an integrated IT platform as an alternative to requiring IT teams to integrate multiple components themselves. Nutanix Enterprise AI extends that core platform to run inference engines. Nutanix Enterprise AI is a component of Nutanix GPT-in-a-Box 2.0, which adds Nutanix Cloud Infrastructure and Nutanix Unified Storage to an instance of a Kubernetes cluster to create a single platform that is priced based on the infrastructure resources consumed, rather than metering and usage-based models that can result in less predictable costs.
Regardless of the type of application deployed, the hyperconverged architecture enables IT teams to devote more resources to building applications versus managing IT infrastructure, said Cornely. Nutanix is also planning to further reduce costs by providing IT teams with a copilot tool that makes use of generative AI to automate IT management tasks. The Nutanix Kubernetes Platform (NKP) that Nutanix gained by acquiring the assets of D2iQ already has a copilot capability enabled by OpenAI, but over time the company will be employing a mix of large language models (LLMs) to automate a wider range of tasks across its portfolio.
It’s not clear to what degree organizations are favoring one infrastructure platform over another for building and deploying cloud-native applications. However, as it becomes more apparent that applications will need to be deployed across highly distributed computing environments the need for a platform that can be run anywhere is becoming more apparent. Each additional platform added tends to reduce the total cost of IT by requiring organizations to hire additional IT staff to manage that specific platform.
As more organizations embrace platform engineering principles to centralize the management of IT, there will undoubtedly be a more concerted effort to rationalize the number of platforms that already overwhelm understaffed IT teams. The only issue left to determine now is what exactly is the lowest number of platforms that IT organizations can effectively standardize on.
