WWT reports the rise of specialized private clouds for AI and high-performance computing—for example, neocloud providers that offer GPU-as-a-service. “These on-premises environments can be optimized for performance characteristics and cost management, whereas public cloud offerings, while often a quick entry point to start AI/ML experimentation, can become prohibitively expensive at scale for certain workloads,” WWT stated.
There is also a move to build up network and compute abilities at the edge, Anderson noted. “Customers are not going to be able to home run all that AI data to their data center and in real time get the answers they need. They will have to have edge compute, and to make that happen, it’s going to be agents sitting out there that are talking to other agents in your central cluster. It’s going to be a very, distributed hybrid architecture, and that will require a very high speed network,” Anderson said.
Real-time AI traffic going from agent to agent is also going to require a high level of access control and security, Anderson said. “You need policy control in the middle of that AI agent environment to say ‘is that agent authorized to be talking to that other agent? And are they entitled to access these applications?’”
That’s a big problem on the horizon, Anderson said. “If a company has 100,000 employees, they have 100,000 identities and 100,000 policies about what those people can do and not do. There’s going to be 10x or 100x AI agents out there, each one is going to have to have an identity. Each one is going to have an entitlement in a policy about what data they are allowed to access. That’s going to take upgrades that don’t exist today. The AI agent issue is growing rapidly,” Anderson said.
In addition, the imperative to run AI workloads on-premises, often dubbed “private AI,” continues to grow, fueled by the need for greater control over data, enhanced performance, predictable costs and compliance with increasingly strict regulatory requirements, WWT stated. It cited IDC data projecting that by 2028, 75% of enterprise AI workloads are expected to run on fit-for-purpose hybrid infrastructure, which includes on-premises components.
“This reflects a shift toward balancing performance, cost and compliance, especially for private AI deployments,” WWW wrote, noting that Grand View Research is predicting the global AI infrastructure market will reach $223.45 billion by 2030, growing at a 30.4% CAGR, “with on-premises deployments expected to remain a significant portion of this growth, particularly in regulated industries like healthcare, finance, and defense.”
