Production AI is pushing private cloud back into the center of enterprise infrastructure.

The shift is not just about where workloads run. It is about cost control, security, governance and the need to bring AI closer to enterprise data. Broadcom Inc.’s VMware Cloud Foundation strategy reflects that reality, as organizations weigh cloud economics against the demands of AI inference at scale.

“AI is driving a couple of factors,” said Paul Turner, chief product officer of the VMware Cloud Foundation Division at Broadcom. “It’s driving one, great opportunity. There’s compelling reasons of why people are adopting AI. One of the things that they need is that the platform that runs AI must be better, and that’s what’s driving VCF adoption today. The second thing that we’re really seeing happen is AI is actually a cost multiplier, because it’s increasing the cost of infrastructure. You’ve got to deal with the risks that AI can expose as well.”

Turner; Prashanth Shenoy, chief marketing officer and vice president of marketing of the VMware Cloud Foundation Division at Broadcom; and others, spoke with John Furrier and Gemma Allen at the Broadcom “Modern Private Cloud: A Secure Foundation for Production AI” event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how Broadcom and its partners and customers see private cloud becoming a practical foundation for secure production AI. (* Disclosure below.)

Production AI changes the private cloud equation

AI has made the infrastructure conversation more practical. Enterprises are not only asking how to adopt models; they are asking how to run inference, secure data and manage rising infrastructure costs without losing control of their operating model. That is where VCF is being positioned as a private cloud foundation, according to Turner.

“AI is also a risk and cost multiplier,” he said. “Just think about a few stats: 73% of enterprises see AI-related attacks. That is almost every industry out there … actually seeing these new attacks that are driven by AI-enabled software.”

That risk profile is changing how enterprises think about public cloud. Training and experimentation may still lean on cloud services, but day-to-day production AI has different economics. Once inference becomes part of operations, token costs, data gravity and compliance become boardroom issues instead of technical footnotes, Shenoy explained.

“Last year, when we did the private cloud outlook study, there was a definitive cloud reset happening in the market, where private cloud and the operating model of private cloud to run your mission-critical workload on-premises or in a hybrid environment was on par with public cloud,” he said. “Fast-forward to this year, when we did the same survey with 1,800 IT leaders and decision-makers around the globe. A lot of organizations are now moving their AI applications from a pilot phase of trying, to production, doing it at scale.”

Cost pressure is also making virtualization feel newly relevant. Memory tiering, GPU utilization and shared platforms for virtual machines and containers all matter more when AI workloads move from isolated pilots into everyday business systems. Broadcom’s argument is that infrastructure efficiency becomes a direct lever for AI adoption, Turner noted.

“It makes a huge difference to our customers when we save the money,” he said. “At the end of the day, we do a lot to make a platform. You’re going to hear more about how we make a platform powerful for AI. But it’s also very important that we make it cost-effective, that we virtualize, that we provide the best platform possible.”

Here’s theCUBE’s complete video interview with Paul Turner and Prashanth Shenoy:

AI sovereignty turns into an infrastructure priority

Private AI has also matured beyond a single architecture. The enterprise need is becoming more distributed, with local models, frontier models and AI gateways working together depending on sensitivity, cost and performance. That is especially important for organizations trying to keep data, control planes and audit trails under their own governance model, according to Chris Wolf, global head of AI and advanced services for the VMware Cloud Foundation Division at Broadcom.

“For a lot of our customers today, their definition means that it’s not just about the data plane being sovereign; it’s about the control plane being sovereign,” he said. “It’s that, ‘I can disconnect from the internet and I can continue to run. I can continue to operate.’ That’s a difference, and that’s been really driven over the last couple of years, far more so than we’ve seen previously.”

Cloud services provider ThinkOn’s work in Canada shows how this plays out in the field. For regulated environments, AI sovereignty is not an abstract policy debate. It becomes a deployment requirement that covers data classification, access control, model choice and the ability to operate in a trusted private cloud environment, according to Craig McLellan, founder and chief executive officer of ThinkOn.

“I’d even go a step further and say that it’s also about model sovereignty,” he added. “Many countries want to have their own sovereign models. For instance, in Canada, Cohere is a vibrant participant in the market, and we actually took the opportunity to work closely with Broadcom to add the Cohere model to the environment as a private cloud. We are able to provide the public sector with a combination of model sovereignty, certainly economic and data sovereignty, as well as control plan and data plane sovereignty.”

Execution is now the harder test. Many organizations have AI strategies, but production use depends on whether infrastructure teams can make the experience safe, repeatable and easy enough for users. That means the operational stack around AI is becoming just as important as the GPU capacity underneath it, McLellan pointed out.

“The clients are always looking for the easy buttons despite the complexity the client wants to bring to the table,” he said. “What I love about this first project that we worked on over the last few months was it was a three-way collaboration. There’s a collaboration between Broadcom and ThinkOn because this is a complex environment that we need to present the easy button with. On top of that, we had to work with the client that was bringing workload to us that wasn’t necessarily entirely thought through either. To be able to turn it into a tool that internal members of the public sector community can actually use safely and securely and, most importantly, easily was no small task.”

Here’s theCUBE’s complete video interview with Craig McLellan and Chris Wolf:

Manufacturing use case shows the infrastructure stakes

Charlotte Pipe and Foundry Co. brings the private cloud discussion into a more grounded setting. The 125-year-old manufacturer is not chasing AI for novelty. Its VCF journey started with practical needs around workload mobility, security and the ability to modernize without refactoring core applications, noted Rodney Barnhardt, server administration at Charlotte Pipe and Foundry.

“Originally when we moved to VCF, it was prior to being able to do brownfield imports,” he said. “While we’ve been VMware customers for a long time, prior to moving to VCF, we were on three tiers: Cisco, BladeCenter, Unity all-flash storage array. In looking at VMware by Broadcom and the VCF platform using HCX to be able to do those migrations, as well as vDefend to put microsegmentation around products, made VMware Cloud Foundation an ideal product to look at deploying within our environment.”

Security became a central driver. For Charlotte Pipe, vDefend and microsegmentation offered a way to limit lateral movement and reduce exposure if an attacker breached the environment, Barnhardt explained. That kind of control becomes more important as AI connects more systems and expands the number of workflows touching sensitive operational data.

“If you use vDefend and create microsegmentation, for those that are not familiar, that limits applications and services to only the ports that they need access to,” he said. “You can say that these servers cannot do a remote desktop protocol, or RDP, to these servers, and that can help lower the threat landscape. If an attacker gets in, they cannot just be jumping from server to server to server. It also adds a layer of protection to the overall environment.”

The operational benefits are also tied to patching, upgrades and day-to-day management. A more automated platform can reduce manual effort and help IT teams respond faster when vulnerabilities emerge. Barnhardt’s advice is practical: Get the fundamentals right before starting the migration.

“I think the key is to plan, is look at your current environment, make sure all of your hardware meets the HCL list, make sure you have all of the appropriate things in place,” he said. “Look at the requirements and the HCL list, and make sure that what you have is already in place or that you may have to go out and make some changes or acquire some different hardware to make that upgrade. You really want to look into that before you start down the upgrade process or the move process, or you may have a delay in getting this transformation done.”

Here’s theCUBE’s complete video interview with Rodney Barnhardt:

(* Disclosure: TheCUBE is a paid media partner for the Broadcom “Modern Private Cloud: A Secure Foundation for Production AI” event. Neither Broadcom, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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