Why Enterprises Choose Colocation Data Centers to Host AI and Hybrid Cloud
Enterprises are increasingly placing AI inference computing in colocation data centers, because modern colocation facilities support 35 kW air-cooled racks and 70-150 kW liquid-cooled racks, at higher densities than most traditional enterprise data centers. The Foundry 2026 Cloud Survey shows that 74% of enterprises accelerated cloud migration last year, while VMware reports that 83% of enterprises have completed or plan to migrate workloads back from public cloud, shifting to hybrid cloud plus colocation or on-premises deployment models.
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Enterprises are strategically choosing colocation data centers to host AI applications, deploy a hybrid cloud model, add incremental capacity, improve connectivity, and reduce costs.
September 21, 2026
Enterprises are increasingly choosing colocation data centers to host AI inference computing, which performs best using higher cabinet densities than are supported in most legacy corporate data centers. Modern colocation facilities support 35 kW cabinets with air cooling and 70-150 kW cabinets using optional liquid-cooling solutions. Because liquid cooling can be added once higher-density cabinets are needed, enterprise users with uncertain densification timelines can avoid stranding capital expenditures on infrastructure, making this “pay-as-you-go” model attractive.
AI inference computing is highly iterative, requiring rapid, continuous interaction with large volumes of corporate financial, operational, and research data. Enterprises optimize AI inference computing performance by placing it near their other data centers to enjoy ultra-low round-trip telecom latencies. Enterprises can also install AI applications in edge colocation data centers near specialized data sources and use cases, such as factories, financial market feeds, and research labs. Colocation is available near almost every major city, but most public cloud regions operate from only a few major data center markets in each country.
Colocation also offers cybersecurity advantages for AI applications. Because AI inference computing accesses valuable, confidential corporate data, many enterprises in highly regulated industries like financial services and healthcare prefer to perform inference internally in private colocation suites with dedicated network circuits and self-operated firewalls rather than sending that data off-site to third-party AI startups.
Hybrid Cloud Adoption and Rebalancing
Public cloud adoption remains strong: 74% of enterprises accelerated cloud migrations last year, according to Foundry’s 2026 Cloud Computing Study, seeking the flexibility, agility, and low capital investment offered by the cloud.
However, prompted by rapidly escalating cloud costs and several highly publicized cloud outages, many enterprises are rethinking “cloud only” strategies by shifting to a hybrid cloud plus colocation or on-premises model. VMware’s Private Cloud Outlook 2026 indicates that 83% of enterprises have completed or are planning to repatriate workloads back from the public cloud, driven by security, cost, compliance, and performance concerns.
In the push toward hybrid cloud, many enterprises now tailor workload placement to whichever delivery path works best for each specific application and data set. Enterprises often choose public cloud for dynamic capacity and cloud-native applications, but colocation and on-premises for predictable workloads and those that underperform technically or are cost-prohibitive in public cloud. Colocation also effectively hosts private clouds seeking the advantages of public cloud in a more controlled computing and security environment.
Enterprises seek scalable data center capacity for both AI and traditional corporate computing (research, accounting, finance, sales, and legal), which is vital to all businesses. AFCOM’s 2026 State of the Data Center Report indicates that 75% of enterprises expect AI workloads to increase overall capacity requirements.
Colocation offers incremental capacity expansion, allowing enterprises to increase or decrease capacity as needs change. Enterprises often prefer colocation to expanding on-premises capacity because on-premises data center construction can be expensive, time-consuming, and, in some cases, impossible due to the age of legacy facilities or physical limitations.
Cost Predictability and Governance
Colocation typically offers lower occupancy costs than public cloud for stable, predictable workloads. Most new colocation campuses are massive – often 50+ MW – delivering economies of scale in both upfront construction costs and operating expenses unavailable at smaller on-premises data centers. An enterprise seeking 2 MW of new capacity can occupy it at much lower cost in colocation than in a new on-premises data center.
Related:Wholesale vs. Retail Colocation: How to Choose a Data Center Lease
Colocation also avoids unpredictable, unbudgeted cost overages in public cloud deployments, often driven by unforeseen variable usage, opaque fees, and weak monitoring tools or practices. Enterprise users in Flexera’s 2026 State of the Cloud Report estimate that 29% of their cloud spending is wasted.
Application Performance and Networking Enhancement
Colocation supports legacy applications not written to be cloud-native, which often perform poorly in public cloud environments, while other corporate applications require lower telecom latency than is available from public cloud placements when corporate data centers aren’t located near cloud campuses.
Some enterprises enhance connectivity for their computing portfolio using dedicated networking cabinets in interconnection-focused colocation facilities near their primary data centers. In this topology, enterprises secure competitively priced metro-area circuits from 20+ telecom carriers and cloud direct-connect circuits available in urban carrier hotels.
International Operations and Data Sovereignty
Many US-based multinational corporations operate their domestic data centers in-house but turn to international colocation suites for worldwide operations where data sovereignty, low-latency access to foreign operations and customers, or audit and compliance requirements necessitate local computing. International colocation providers also usually offer à la carte managed services that are valuable to enterprises without local IT staff.
Test and Development Labs
Colocation supports internal test/dev labs by offering technologies unavailable at legacy on-premises data centers. Many colocation facilities offer liquid coolants for direct liquid-to-chip testing, large telecom network connections for data-intensive application development, and AI-driven data center management tools and digital twin software. Some cutting-edge colocation facilities offer 800-volt DC electrical distribution and immersion cooling. These technology-forward features are available for “try it before you buy it” enterprise projects.
Audit and Compliance Controls
Colocation provides a secure, non-shared hardware placement, enabling audit and compliance procedures favored by corporate users. Many colocation data centers also offer extensive facility and provider certifications, enhancing regulatory approvals.
Meeting Multiple Objectives with Colocation
Enterprises can pursue various goals simultaneously using colocation – delivery paths are not “either/or” between cloud and colocation. For example, a large bank might use an affordable private colocation suite to supplement other computing capacity and reduce public cloud costs; multiple international colocation facilities to support worldwide operations; several interconnection nodes to improve telecom networking; and a density-focused colocation cabinet pod for AI inference rollouts.
About the Authors
Kirk Killian
Partners National Mission Critical Facilities.
Kirk Killian is president at Partners National Mission Critical Facilities.
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