Hybrid cloud computing transforms enterprise technology

Hybrid cloud computing is redefining the enterprise technology landscape, evolving from a focus on scalability and cost efficiency to becoming the driving force behind transformative applications.

Innovations in generative artificial intelligence, hybrid application development and infrastructure modernization are enabling organizations to integrate traditional systems with advanced technologies, paving the way for greater adaptability and resilience in an ever-changing digital ecosystem, acc0ording to Sarbjeet Johal (pictured), technology analyst and member of theCUBE Collective.

Sarbjeet Johal, technology analyst and theCUBE Collective member, talks about hybrid cloud computing.

“There’s no magic in computing; there’s logic there,” he said. “There are zeros and ones, and they get flipped based upon what we tell computers to do. In this case, large language models are telling them based upon our prompts. The fact is that when we view the next-generation applications, they will be hybrid applications. We will use the old constructs, data coming from the actual databases where it sits there right now and then also the gen AI agents will be called in.”

Johal spoke with theCUBE Research’s John Furrier for theCUBE’s “Cloud AWS re:Invent Coverage,” during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how hybrid cloud computing, generative AI and infrastructure modernization are driving innovation and adaptability in enterprise technology.

Hybrid cloud computing powers innovation and resilience

AWS’ announcements during this week’s re:Invent emphasized three pillars: compute, storage and networking. These “primitives” form the foundation of its infrastructure strategy, ensuring seamless integration between legacy systems and modern technologies, according to Johal.

“You take any little strand, the three core pillars like compute, storage and network, under that, there’s so many pillars, like wireless … on-prem, cloud, different flavors of network, different flavors of storage, different flavors of compute. Now there’s GPUs and all that,” he said. “They are innovating at all levels. That is the key.”

One standout revelation was Amazon’s Nova frontier models, designed to compete with industry giants such as Google LLC and OpenAI in generative AI. These models offer four specialized flavors, enabling diverse use cases, such as video, graphics and enterprise applications. This move not only positions AWS among the leaders in AI, but also demonstrates its ability to integrate these tools into existing enterprise frameworks effectively.

“I was surprised when they announced their frontier model in four flavors,” Johal said. “I think with the frontier model, Amazon has joined the main league of gen AI as Google, including Anthropic, that’s Facebook, that’s OpenAI.”

A key theme in AWS’ vision is the hybrid application model. By combining traditional databases with small and large language models via APIs, businesses can create adaptive workflows. This approach bridges the gap between the old and the new, ensuring compliance with regulations while harnessing the power of AI-driven insights, Johal explained.

“They spend a lot of time on the analytics and how they’re merging,” he said. “They’re saying that the analytics world and the gen AI world will merge and how data from relational databases or all different types of databases, relational, non-relational, can be brought into the model training.”

The growing importance of redundancy and adaptability was underscored by insights from leading organizations, such as JPMorgan Chase, which operates on a scale comparable to hyperscalers. This focus on resilience aligns with AWS’ innovation across every layer of its platform, from compute to networking, according to Johal.

“B2B companies have started to use their large-language model, and they are making that model smaller,” he said. “They condensed that model to make it a smaller model, and then they infused their own data into it to make a small language model, SLM. The beauty of SLM is that during the inference stage it’s cheaper to operate and you pay a lot less bill.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s “Cloud AWS re:Invent Coverage”:

Photo: SiliconANGLE

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