The power of genAI plus multicloud architecture

This post was originally published on Info World

The rapid evolution of generative AI is poised to influence the significant adoption and expansion of multicloud architecture. What is most interesting is that multicloud is occurring mainly behind the scenes, without much fanfare, taking a backseat to the hype around generative AI. I believe it’s just as important, and enterprises need to pay attention.

Everyone saw this coming

Generative AI models, especially large-scale neural networks, require immense computational power and scalable infrastructure. Multicloud architecture is essentially a complex distributed architecture that spreads workloads across multiple cloud service providers, on-premises systems, edge, and anything that can store or process stuff. Multicloud offers the requisite scalability and flexibility whether you’re hosting generative AI systems or not.

By leveraging different cloud environments, enterprises can dynamically allocate resources, ensuring that AI workloads are efficiently managed without bottlenecks. This flexibility is particularly crucial for generative AI models, which often necessitate bursts of high-performance computing and vast amounts of storage.

One of the primary benefits of a multicloud strategy is cost optimization. Generative AI workloads can be expensive to run continuously. Using a multicloud approach, organizations can optimize costs by selecting the most cost-effective cloud provider for specific tasks.

This has been a big bugaboo for me, considering that enterprises are

Read the rest of this post, which was originally published on Info World.

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