Hybrid IT Dominates Enterprise Architecture, with Colocation Anchoring Critical Workloads

Uptime, Security, Connectivity and Performance Overtake Cost as Primary Drivers of Enterprise IT Decisions Across Cloud, Colocation and On-Prem Environments

Hybrid IT has become the standard operating model for enterprises — but that companies are still looking for the right hybrid IT mix, according to the 2026 State of the Data Center Report from CoreSite.

After years of cloud migration and hybrid adoption, organizations are shifting their focus from deciding whether to use cloud, colocation or on-premises infrastructure to determining which workloads belong in each environment.

As workloads become more distributed and AI drives greater infrastructure demands, enterprises are taking a more deliberate approach to workload placement, making decisions based on performance, security, data control and connectivity requirements rather than cost alone.

"An organization's success will be determined by its ability to deploy hybrid workloads at scale, and by future-proofing with higher power densities and faster connectivity," said Juan Font, President and CEO of CoreSite and SVP of American Tower. "The future belongs to organizations that align the right infrastructure with the right workloads."

High-level insights and key data points from this year's report include:

Workload Placement Is Becoming More Intentional

Across roughly two dozen workload categories, enterprises are actively rebalancing where applications run. The research shows increased cloud deployment of chatbots, mobile apps, websites, content delivery and AI-based recommender systems, while colocation is gaining share in workloads such as web applications, HR systems and cybersecurity — signaling a more precise, workload-by-workload optimization approach rather than broad migration strategies.

CIOs Are Choosing Colocation for AI Production Workloads

Nearly 70% of CIOs now favor colocation and hybrid environments for AI/ML production workloads such as chatbots, virtual assistants and web hosting. This marks one of the clearest signals yet that AI production infrastructure is migrating to highly connected, power-dense environments.

Cloud Connectivity Is Now a Core Requirement for Colocation Providers

Nearly eight in 10 respondents rate native, direct connectivity to major cloud providers as "very important" for colocation providers — the highest level of urgency recorded in the last five years, showing that seamless public cloud interconnection has become a foundation for modern hybrid IT architectures.

Cost Is No Longer the Primary Driver

While cost remains a factor, uptime, security and performance have overtaken cost as the primary drivers of workload placement decisions, particularly for AI and high-density compute workloads, marking a shift from cost-optimized infrastructure planning to resilience- and performance-driven architecture decisions.

As hybrid environments become more complex, seamless integration and direct, low-latency connectivity across cloud, colocation and on-prem systems have become essential. Security and data control remain foundational drivers of workload placement decisions, particularly for sensitive and regulated workloads. As a result, colocation is evolving beyond a hosting environment and increasingly serving as an orchestration point that enables performance, security and ecosystem connectivity across distributed enterprise environments.

"The State of the Data Center findings reinforce broader trends we're seeing across the industry: hybrid IT is now the standard operating model for enterprises," said John Gallant, Enterprise Consulting Director at CIO. "What stands out in this year's research is that enterprises have moved beyond hybrid adoption and the ones preparing for the future are now focused on optimizing workload placement across cloud, colocation and on-premises environments based on performance, security and control requirements."

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Hybrid IT Dominates Enterprise Architecture, with Colocation Anchoring Critical Workloads

Uptime, Security, Connectivity and Performance Overtake Cost as Primary Drivers of Enterprise IT Decisions Across Cloud, Colocation and On-Prem Environments

Hybrid IT has become the standard operating model for enterprises — but that companies are still looking for the right hybrid IT mix, according to the 2026 State of the Data Center Report from CoreSite.

After years of cloud migration and hybrid adoption, organizations are shifting their focus from deciding whether to use cloud, colocation or on-premises infrastructure to determining which workloads belong in each environment.

As workloads become more distributed and AI drives greater infrastructure demands, enterprises are taking a more deliberate approach to workload placement, making decisions based on performance, security, data control and connectivity requirements rather than cost alone.

"An organization's success will be determined by its ability to deploy hybrid workloads at scale, and by future-proofing with higher power densities and faster connectivity," said Juan Font, President and CEO of CoreSite and SVP of American Tower. "The future belongs to organizations that align the right infrastructure with the right workloads."

High-level insights and key data points from this year's report include:

Workload Placement Is Becoming More Intentional

Across roughly two dozen workload categories, enterprises are actively rebalancing where applications run. The research shows increased cloud deployment of chatbots, mobile apps, websites, content delivery and AI-based recommender systems, while colocation is gaining share in workloads such as web applications, HR systems and cybersecurity — signaling a more precise, workload-by-workload optimization approach rather than broad migration strategies.

CIOs Are Choosing Colocation for AI Production Workloads

Nearly 70% of CIOs now favor colocation and hybrid environments for AI/ML production workloads such as chatbots, virtual assistants and web hosting. This marks one of the clearest signals yet that AI production infrastructure is migrating to highly connected, power-dense environments.

Cloud Connectivity Is Now a Core Requirement for Colocation Providers

Nearly eight in 10 respondents rate native, direct connectivity to major cloud providers as "very important" for colocation providers — the highest level of urgency recorded in the last five years, showing that seamless public cloud interconnection has become a foundation for modern hybrid IT architectures.

Cost Is No Longer the Primary Driver

While cost remains a factor, uptime, security and performance have overtaken cost as the primary drivers of workload placement decisions, particularly for AI and high-density compute workloads, marking a shift from cost-optimized infrastructure planning to resilience- and performance-driven architecture decisions.

As hybrid environments become more complex, seamless integration and direct, low-latency connectivity across cloud, colocation and on-prem systems have become essential. Security and data control remain foundational drivers of workload placement decisions, particularly for sensitive and regulated workloads. As a result, colocation is evolving beyond a hosting environment and increasingly serving as an orchestration point that enables performance, security and ecosystem connectivity across distributed enterprise environments.

"The State of the Data Center findings reinforce broader trends we're seeing across the industry: hybrid IT is now the standard operating model for enterprises," said John Gallant, Enterprise Consulting Director at CIO. "What stands out in this year's research is that enterprises have moved beyond hybrid adoption and the ones preparing for the future are now focused on optimizing workload placement across cloud, colocation and on-premises environments based on performance, security and control requirements."

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Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

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Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

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Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...