Equinix and Digital Realty Report Enterprise Shift to High-Density Hybrid Deployments

Executive Summary

• Enterprises scale hybrid colocation with 30–60kW racks for AI, according to recent landlord and analyst updates.
• AWS, Microsoft, and Google release new high-density AI infrastructure options that reframe enterprise deployment strategies...
• Analyst notes indicate a growing share of enterprise workloads favoring colocation and sovereign cloud controls in Europe..
• Enterprises pilot liquid cooling at 15–25% of AI estates to manage heat and efficiency, industry sources suggest.

Enterprise Momentum to Hybrid Colocation Enterprise infrastructure leaders report a decisive pivot to hybrid deployments pairing colocation with public cloud, citing density, cost control, and proximity to cloud on-ramps. Data center landlords highlight AI-ready designs and interconnection strategies as core to enterprise adoption in Q4 2025–early Q1 2026.. Analysts estimate enterprises are allocating larger shares of new compute and storage spending toward facilities optimized for GPUs and specialized accelerators, particularly for inference at scale. Equinix and Digital Realty emphasize cross connects to major clouds and high-density power availability as differentiators for enterprise workload placement. Equinix’s interconnection data and regional expansion updates point to increased bandwidth utilization among enterprise tenants in late 2025, driven by AI pipelines and data-intensive applications. Digital Realty underscores purpose-built AI data halls and guidance for liquid cooling deployment, reflecting ongoing adoption pilots among large customers. Deployment Strategies Shaped by Hyperscaler Offerings Product updates from major clouds in December 2025 are reshaping enterprise plans for where and how AI compute is deployed. AWS re:Invent sessions and announcements stressed new instance families and networking features geared for high-throughput model training and inference, influencing hybrid placement decisions for performance and cost. Microsoft’s Azure updates spotlight expanded GPU SKUs and workload orchestration improvements, which enterprises leverage alongside colocation for data locality and regulatory compliance. Google Cloud’s recent posts on AI infrastructure tooling and secured controls bolster enterprise confidence in multi-region deployments with sovereign features in the EU, aligning with rising regulatory expectations for data residency and operational controls.. Together, these launches are driving a strategy where enterprises split training to cloud while anchoring inference and data processing in colocation for predictable cost and governance. For more on broader Data Centers trends. Density, Cooling, and Power Availability Enterprises targeting 30–60kW per rack are piloting liquid cooling across high-density rows to mitigate thermal limits and improve efficiency. Industry research indicates a measurable uptick in liquid cooling pilots among enterprise data center estates, particularly where GPU utilization and power constraints converge. Landlords detail deployment playbooks covering direct-to-chip cooling readiness, containment, and modular power upgrades to support rapid AI capacity growth. Vendors and integrators are standardizing reference designs to reduce time-to-deploy. NVIDIA ecosystem partners and server OEMs like NVIDIA, Supermicro, HPE, and Dell Technologies are promoting dense, liquid-ready racks and validated configurations that accelerate enterprise pilots and migrations.... This aligns with landlords’ guidance on airflow, floor loading, and power redundancy to ensure operational reliability as adoption scales. Governance, Sovereign Controls, and Interconnection European energy-efficiency and reporting requirements are increasingly informing deployment choices, pushing enterprises toward facilities with transparent efficiency metrics and sovereign data controls. Hyperscalers are responding with enhanced regional controls that enterprises combine with colocation strategies for compliance and performance.. Interconnection growth among enterprise tenants points to a shift toward data gravity-aware architectures—placing inference close to data stores and cloud egress points. Equinix’s interconnection reports and ecosystem data suggest steady enterprise bandwidth growth for multi-cloud integration through late 2025. This builds on related Data Centers developments where enterprises weigh latency, regulatory, and cost outcomes across hybrid footprints. Key Enterprise Deployment Metrics

Metric	Estimated Range	Context	Source
Enterprise racks at 30–60kW	20–35% of new deployments	AI-focused expansions in colocation	Digital Realty AI-ready guidance
Liquid cooling pilots	15–25% of AI racks	Thermal and efficiency management	Uptime Institute research
Hybrid colocation share	40–55% of net-new workloads	Training in cloud, inference in colo	IDC infrastructure insights
Interconnection bandwidth growth	10–20% YoY	Enterprise multi-cloud integration	Equinix Global Interconnection Index
Sovereign cloud-enabled deployments	20–30% in EU enterprises	Residency and compliance drivers	Google Cloud sovereign controls
GPU-optimized colocation footprints	25–40% of expansion capex	High-density power and cooling	AWS re:Invent infrastructure themes

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Procurement, Financing, and Time-to-Deploy Enterprises are compressing build timelines by adopting standardized high-density blocks and pre-validated reference designs, while negotiating power commitments and interconnection SLAs upfront with landlords. Recent updates from major providers point to modular build approaches that help reduce time-to-live for AI workloads from months to weeks in select regions.. Financially, landlords and hyperscalers signal continued capex allocation toward GPU-optimized halls, structured for elasticity as enterprise demand ramps. Analyst commentary highlights a measured but firm expansion trajectory for enterprise AI estates across 2026, balancing power availability, thermal constraints, and regulatory controls.. OEM ecosystems from NVIDIA and Supermicro to HPE and Dell Technologies continue to publish dense, liquid-ready designs to support these strategies.... FAQs { "question": "How are enterprises balancing cloud and colocation for AI workloads?", "answer": "Enterprises increasingly split training and inference across environments. Training is often placed in public cloud to use elastic GPU capacity and specialized networking, while inference and data processing move to colocation for predictable costs, data locality, and compliance. This hybrid approach also leverages interconnection to major clouds and data sources. Analyst notes in late 2025 and provider updates from AWS, Microsoft, and Google indicate this trend is solidifying across large enterprises." } { "question": "What rack densities are enterprises targeting for new AI deployments?", "answer": "Many enterprises are planning for 30–60kW per rack in AI-focused expansions, enabled by liquid cooling readiness and upgraded power distribution. Landlords such as Digital Realty and Equinix outline designs for high-density rows and validated cooling strategies. These configurations support GPU clusters and low-latency interconnects for inference pipelines and model serving, contributing to faster deployment timelines and improved efficiency in Q4 2025–Q1 2026." } { "question": "What role do sovereign cloud controls play in deployment strategies?", "answer": "Sovereign cloud controls, particularly in the EU, are shaping workload placement decisions to meet residency and regulatory requirements. Google Cloud and Microsoft have enhanced regional controls and compliance features, which enterprises combine with colocation for data gravity and cost. As EU energy and reporting frameworks evolve, enterprises favor facilities offering transparent metrics and governance, ensuring compliant operations without sacrificing performance." } { "question": "Are enterprises adopting liquid cooling widely or selectively?", "answer": "Adoption is growing but remains selective, typically focused on the densest AI racks where thermal limits are most acute. Industry research suggests liquid cooling pilots span roughly a fifth of AI estates, with direct-to-chip approaches gaining traction. Landlords provide deployment playbooks and modular retrofits to accelerate adoption. OEM ecosystems from NVIDIA and server vendors support these pilots with liquid-ready designs to streamline integration and operations." } { "question": "Which vendors are influencing enterprise deployment timelines the most?", "answer": "Hyperscalers and OEMs are central to deployment speed. AWS, Microsoft, and Google provide elastic GPU capacity and orchestration tooling that guide hybrid strategies. NVIDIA’s platform ecosystem, along with Supermicro, HPE, and Dell Technologies, delivers validated, dense, and liquid-ready server configurations. Landlords like Equinix and Digital Realty complement these with power, cooling, and interconnection readiness, compressing time-to-live for AI workloads across regions." } References

• Global Interconnection Index - Equinix, December 2025
• AI-Ready Data Centers - Digital Realty, December 2025
• AWS re:Invent 2025 Announcements - Amazon Web Services, December 2025
• Azure Blog Updates - Microsoft, December 2025–January 2026
• Google Cloud Blog - Google, December 2025–January 2026
• IDC Infrastructure Market Update - IDC, December 2025
• Data Center Research Library - Uptime Institute, December 2025
• Energy Efficiency Directive Recast - European Commission, December 2025
• NVIDIA Data Center Platform - NVIDIA, December 2025
• AI Solutions Overview - Supermicro, December 2025