Modern software companies no longer run on a single cloud; they're spread across dozens of specialized providers, from AI platforms to data warehouses to edge networks. This fragmentation creates a problem: today's developers grew up building apps in the cloud, but most lack the network engineering skills needed to securely connect all these different services together. The capabilities that tech giants spent years building - private network connections, global infrastructure, and sophisticated traffic routing - remain out of reach for smaller teams.
According to a release from OpenAI, the dollars heading to AWS from the organization, whose annual revenues are projected to be a mere $13 billion, will be used to "run and scale" its AI workloads, beginning immediately. The release went on to say that AWS will provide the company with "Amazon EC2 UltraServers, featuring hundreds of thousands of chips, and the ability to scale to tens of millions of CPUs for its advanced generative AI workloads."
Today, enterprises need a robust digital infrastructure for everything from customer engagement to operational continuity, and multi-cloud technology has become a fundamental enabler of enterprise success. However, with these increased complexities, organisations face increasing challenges in managing security risks, maintaining operational uptime, and above all, to maximise value from their cloud investments. Emerging technologies and innovative approaches are reshaping the way enterprises navigate these challenges, and at the same time service level agreements (SLAs) too are evolving to align with these developments.
The real value in multi-cloud operations lies in providing consistent operations across AWS, Azure, and Google Cloud Platform. "If I want to run something in Google and I want to run something in Amazon, if I have to learn new ways of doing things... it becomes more complex for the enterprise," he explained. Nutanix enables organizations to maintain the same operational model across all cloud environments, reducing training requirements and operational complexity.
