In an effort to transform how businesses are managing and securing artificial intelligence workloads, companies are turning to gen AI private cloud technology. By offering a dedicated environment for AI development, these private clouds provide enhanced security, control and scalability, making them an ideal choice for organizations seeking to leverage AI without compromising on privacy or compliance.

Driven by the need for strong data protection and regulatory adherence, companies are striving to achieve a balance between innovation and security, ensuring AI initiatives are both cutting-edge and compliant with industry standards. During the recent HPE Discovery in Las Vegas, David Linthicum, principal analyst at theCUBE Research, learned about some of Hewlett Packard Enterprise Co.’s innovations.

“What really kind of struck me is the fact that they’re coming up with bundles of technology that are, in essence, on-premises technology, hardware, that are going to provide very similar value to public cloud providers in the context of solving the AI problem,” he said.

Linthicum examines the prepackaged solutions hardware providers are offering for gen AI private cloud and what enterprises need to consider when evaluating options.

The pros and cons of private versus public clouds

The resurgence of private cloud is partly driven by the need to manage the complex array of services required for effective AI deployment, Linthicum explained. However, he noted that using gen AI private cloud effectively requires a vast ecosystem that organizations must develop, deploy and operate, which is costly and time-consuming.

“Public cloud computing is the most convenient way to build and deploy generative AI systems today because the ecosystem is there, on demand,” he said. With everything available in the portal, Linthicum added, all that is needed is to gather the information, decide what systems to build and assemble the services.

While public cloud computing offers ease and convenience, it can also be the most expensive cloud option, according to Linthicum. Moreover, he identified other compelling reasons organizations might want to use a private cloud environment. He explained that compliance might be an issue or regulations or policies — or that some organizations may wish to run generative AI in their own data center rather than outsource it to another data center in the public cloud.

A new gen AI private cloud alternative that supports long-term success

What alternatives do organizations have to public or private cloud providers? In recent years, a new choice has emerged.

“Well, the private cloud providers — in this case, HPE, Dell Technologies and Lenovo … — are coming together with bundles of services and bundles of hardware, which they’re marketing as private cloud AI or AI systems that run on private cloud,” Linthicum said. He added that these solutions are “really getting to answer the value issue with leveraging private cloud computing as related to and contrasted with public cloud computing.”

These hardware providers are competing with cloud providers and realize the need to provide cost-effective solutions that deliver greater value in deploying generative AI systems, Linthicum noted. That’s why companies provide customers with the ecosystem —the products customers need to build and deploy a generative AI system — preconfigured for them to leverage almost like a public cloud service, he added.

“The reason we’re having lots of cloud repatriation that’s occurring now is because people didn’t do the planning I think they needed and ultimately ended up misplatforming some of the core applications,” Linthicum said. He cautioned that companies could make the same types of mistakes with generative AI, with some things ending up in the cloud when they should be on-premises and vice-versa.

It’s also essential to evaluate the costs of each solution being considered, which may need to be estimated, he advised. Linthicum suggests that when thinking about a DIY or hardware-based private cloud option,  internal resources are also needed to maintain any systems and to have a business continuity and disaster recovery plan in place.

In addition, consider what kind of systems need to be built and the types of use cases found within an enterprise. “That really dictates the requirements that you’re going to have,” he noted. “Security, governance, performance, business capabilities — all those sorts of things. And then you back those into the appropriate solution.”

To provide more perspective and help inform your cloud decisions, Linthicum suggests reading an article by Dave Vellante, co-CEO of SiliconAngle Media. “… It talks about the race right now with the hardware providers and the cloud providers,” he said.

Here is the complete discussion with David Linthicum, part of the AI and Innovation podcast series on theCUBE:

Image: SiliconANGLE/Bing