Enterprise AI is moving out of the pilot phase and into production. As it moves, one question keeps coming up: where should that AI live?
Public cloud is the default answer for many businesses. It’s familiar, available immediately, and it gets a project off the ground fast. However, production AI behaves differently to a pilot, and housing it in space you don’t own or control carries a cost that only shows up later.
Public cloud suited AI experimentation the way a hotel suits a short stay: check in, use the facilities, check out. Production AI has a different requirement.
AI needs a home – a space where you control what happens inside its walls and know exactly who holds the keys to the building and your data. This is where private AI comes in.
The market is already moving towards private cloud. Broadcom’s Private Cloud Outlook 2026 found that 56% of enterprises are now running or planning to run production AI inferencing in private cloud, while public cloud usage for the same workloads fell from 56% to 41% in a single year. Production AI is pulling infrastructure decisions back towards environments that businesses control directly.
The three Cs of AI infrastructure
1. Control: who holds the keys?
Control is usually the first consideration for organisations once AI moves into production.
AI systems run on data, and that data often includes intellectual property, customer information and competitive advantage. As AI embeds itself into business processes, control over that information stops being optional.
Oliver Rowell, Solution Architect at Xtravirt, puts it simply: “Sovereignty comes down to one thing: who has the keys to your data? Under the US CLOUD Act, any company headquartered in the United States can be compelled to hand over access to data held by a public cloud provider it owns, wherever that data physically sits. We have clients with sensitive legal matters who simply don’t want the US government able to reach their data through a subpoena to the hosting company.”
Control and compliance sit close together here. Data protection law, sector regulation and client contracts increasingly determine not just how information is secured, but where it can legally sit and who is entitled to access it.
In a hotel, you occupy the room, but someone else owns the building and sets the rules on access. Public cloud works the same way. It introduces dependencies on a provider’s policies and jurisdiction, whether or not that provider is actively involved in your day-to-day operations.
Private cloud puts the organisation back in charge of where data sits, how it’s protected, who can reach it and how governance is enforced. For regulated industries and any business handling sensitive information, that level of ownership is now a requirement.
If AI is going to sit inside the fabric of the business, it needs to live somewhere the business controls outright.
2. Cost: avoiding the expensive hotel bill
Hotels are convenient for a night. Stay for a year and the bill adds up fast.
AI in public cloud follows the same pattern. AI isn’t a temporary workload. Successful AI systems get more valuable, and more expensive, over time. They process more data, support more users and reach further into the business. Consumption-based pricing makes that hard to forecast, with compute, storage, data transfer and GPU costs all moving independently.
Will Rodbard, Master Architect at Broadcom, puts the economics plainly: “A single GPU can cost anywhere from seven thousand to sixty thousand dollars. But if you are on-prem, you have the ability to control cost.”
Private cloud reverses that equation. Own the infrastructure and you gain direct visibility into what you’re consuming and why, which turns AI spend from a monthly surprise into a predictable line item.
3. Complexity: building for the long term
A hotel room works when you’re travelling light. Moving your whole life needs a permanent address.
Enterprise AI carries real complexity. Models connect to core business systems. Data pipelines need securing and governing. Compliance requirements apply. Performance has to hold steady while the underlying technology keeps changing.
Decisions made early are hard to reverse later. Data residency choices get harder to unwind. Migration costs climb. Dependencies multiply the longer AI stays where it started.
Private cloud lets organisations design the environment around their own requirements from day one. Platforms such as VMware Cloud Foundation (VCF) deliver the automation, self-service provisioning and policy-driven governance that give public cloud-like agility, while keeping the organisation in control of the infrastructure beneath.
That control matters because AI itself won’t stay still. Models, use cases and business requirements will keep changing, and a private cloud foundation gives the room to adapt as they do.
Two places to start
The strongest AI deployments start with a specific problem, not a broad ambition. As Rowell puts it: “The organisations succeeding with AI are the ones that pick a real use case and commit to it”.
Documentation and knowledge search is one clear starting point. Most organisations hold vast amounts of data scattered across systems and formats, much of it hard to access. Retrieval Augmented Generation (RAG) turns that stored knowledge into contextual insight without the data leaving the environment. Xtravirt has delivered rapid value through RAG deployments for clients running on VCF.
Secure coding environments are another starting point. Development teams working in regulated or air-gapped settings need AI-assisted coding support that never leaves the perimeter. Private AI delivers that: the productivity gains of AI-assisted development without routing proprietary code through a public endpoint.
The gap widens quickly
Much of the pressure around AI comes from the fear of falling behind, and that fear has a basis in fact. Rodbard puts it this way: “Two businesses doing the same work, one running orchestrated, automated processes and the other still relying on manual labour, won’t stay competitive at the same pace. The automated business moves faster and reacts sooner”.
For years, IT teams have been asked to do more with less. Private AI delivers on that brief. It frees people from repetitive, high-volume work so they can focus on the work that moves the business forward.
None of this requires an in-house data science team to get started. An AI application is still just an application – an interface that needs infrastructure to run and data to draw on, whether the person behind it is a data scientist or not. Xtravirt’s role is making that infrastructure straightforward to deploy, so the barrier to starting is lower than most organisations assume.
How Xtravirt can help
Enterprise AI demands proximity to data, predictable performance, strong governance, and secure access to valuable intellectual property. The organisations getting the most from AI aren’t just adopting the newest models, they’re building the right foundations.
Xtravirt supports enterprises at every stage of that journey, from AI readiness assessments and cloud strategy through to deployment, governance and ongoing optimisation.
Businesses no longer question whether they need AI. They’re asking where it should live, and whether that location delivers the value, security and compliance they need.
Explore the Own Your Cloud hub or get in touch to discuss where your AI programme should call home.
This piece draws on a recorded conversation between Will Rodbard, Master Architect at Broadcom, and Oliver Rowell, Solution Architect at Xtravirt. Watch the full discussion here.
