Enterprises Shift to Colocation for AI Density and Cost Control
Companies are moving AI workloads to third-party data centers to access higher power densities and reduce reliance on public cloud costs.
Key points
- Colocation facilities support cabinet power densities of 70 to 150 kW, far exceeding the limits of most legacy corporate data centers.
- 83% of enterprises are planning to repatriate workloads from public cloud to hybrid models due to cost and security concerns.
- 75% of companies expect AI workloads to drive significant increases in their overall data center capacity requirements.
Major corporations are increasingly choosing third-party data center facilities, known as colocation, to host artificial intelligence applications. This shift is driven by the need for higher power densities that traditional on-site servers cannot support, as well as a desire to stabilize costs and improve network performance.
According to Data Center Knowledge, this move allows businesses to deploy hybrid cloud models more effectively. By placing AI inference tasks near their core data, companies can significantly reduce latency while avoiding the high capital expenditure associated with building new on-premises infrastructure from scratch.
Higher Power Densities for AI Workloads
AI inference computing requires significantly more power per server cabinet than legacy corporate applications. Modern colocation facilities support cabinet densities of 35 kW with air cooling, and up to 150 kW when liquid cooling is added. This capacity is critical because most older corporate data centers lack the physical infrastructure to handle such high loads without extensive and costly retrofits.
The flexibility of colocation allows companies to adopt a pay-as-you-go approach to power and cooling. If a business's AI needs grow, they can upgrade to liquid-cooled cabinets only when necessary. This prevents the risk of stranded capital expenditure on infrastructure that may become obsolete or insufficient as technology evolves.
Rebalancing Hybrid Cloud Strategies
While 74% of enterprises accelerated cloud migrations last year, many are now reconsidering a cloud-only strategy. Data from VMware’s Private Cloud Outlook 2026 shows that 83% of companies have completed or are planning to bring workloads back from public cloud providers. This repatriation is driven by concerns over rising costs, security risks, and compliance requirements.
In this hybrid model, companies assign workloads based on efficiency. Public cloud remains useful for dynamic, cloud-native applications, while colocation is preferred for predictable, high-volume workloads. This approach allows businesses to maintain the agility of the cloud while retaining the control and cost predictability of private infrastructure.
Scalable Capacity and Governance
Building new on-premises data centers is often slow, expensive, and physically constrained by legacy facility limitations. Colocation offers a faster path to capacity expansion, allowing enterprises to scale up or down as AI workloads fluctuate. AFCOM’s 2026 report indicates that 75% of enterprises expect AI to increase their overall capacity requirements, making this scalability a key decision factor.
Regulated industries like finance and healthcare also favor colocation for data governance. By hosting AI inference in private suites with dedicated network circuits, companies can keep sensitive data within their own security perimeter. This avoids sending confidential information to third-party AI startups, addressing both privacy concerns and regulatory compliance mandates.
