Disaggregated infrastructure is displacing the all-in-one data center stack — and the economics of AI are making that reality impossible to ignore. Most enterprise data remains on-premises, creating a natural anchor for AI deployments. As companies push to operationalize agentic AI, the rigid costs and complexity of legacy hyperconverged infrastructure are increasingly hard to justify, according to Travis Vigil, senior vice president of product management at Dell Technologies Inc.
Vigil argues that separating compute from storage allows enterprises to scale each independently, avoiding the one-size-fits-all constraints of older systems. “Being able to bring compute and data management to where the data lives is cheaper and more efficient,” he said. “Customers need to turn data into results — that’s the oil. Everything else is just piping to get value from it.”
Dell’s pitch centers on escaping hyperconverged lock-in. At Dell Technologies World 2026, the company announced expanded support for VMware Cloud Foundation 9.1, Microsoft Azure Local, and Nutanix on PowerStore — all built on a disaggregated model. This approach claims up to 65% cost savings compared to hyperconverged infrastructure, according to Caitlin Gordon, senior vice president of product management for private cloud and AI solutions at Dell.
“Dell Private Cloud with disaggregated infrastructure now offers a six-to-one data reduction,” Gordon said. “That’s 65% acquisition cost savings. The private cloud software also delivers operational savings. It’s a cost benefit analysis that’s undisputed for every customer.”
PowerStore Elite, the centerpiece of Dell’s storage refresh, delivers three times the performance and density of earlier versions. Throughput reaches 40 gigabytes per second, keeping GPU clusters continuously fed, Vigil noted. A new AI ISV ecosystem program allows partners to self-validate integrations at scale, Gordon added.
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“The AI ISV ecosystem program lets us bring providers into context of their use cases,” Gordon said. “Dell validation ensures scalability for enterprise customers. We can help them onboard and deploy solutions automatically through the Dell Automation Platform.”
The shift to disaggregated infrastructure reflects broader industry trends. Legacy systems struggle to meet the demands of AI workloads, which require flexible, scalable resources. Dell’s approach separates compute and storage, enabling tailored scaling without vendor lock-in.
Some experts caution that disaggregated models require careful orchestration. “It’s not just about splitting hardware,” said one analyst unaffiliated with the company. “Operational complexity increases. Customers must ensure their software layers can manage the separation effectively.”
Despite challenges, the economics of AI are driving adoption. Legacy hyperconverged systems often force companies to overprovision resources, leading to wasted capacity. Disaggregated models, by contrast, align hardware with specific workloads, reducing costs.
The company’s PowerStore Elite highlights the potential of disaggregated infrastructure. With 40 gigabytes per second of throughput, it supports high-demand AI applications that require continuous data flow. The AI ISV ecosystem program further extends this by enabling partners to validate integrations at scale.
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“Having ecosystem providers in context of their use cases is critical,” Gordon said. “Dell validation ensures our enterprise customers can deploy solutions automatically. This reduces friction in onboarding and speeds up time to value.”
The push toward disaggregated infrastructure is part of a larger movement. Legacy systems struggle to meet the demands of AI workloads, which require flexible, scalable resources. The company’s approach separates compute and storage, enabling tailored scaling without vendor lock-in.
Some experts caution that disaggregated models require careful orchestration. “It’s not just about splitting hardware,” said one analyst unaffiliated with the company. “Operational complexity increases. Customers must ensure their software layers can manage the separation effectively.”
Despite challenges, the economics of AI are driving adoption. Legacy hyperconverged systems often force companies to overprovision resources, leading to wasted capacity. Disaggregated models, by contrast, align hardware with specific workloads, reducing costs.
