State of the Data Center 2024: Hybrid IT Adoption Accelerates

More than ever, IT executives have options for strategically locating computing resources across multiple environments, with an eye toward interconnected digital ecosystems that deliver value, performance and flexibility. These specialized digital ecosystems are being strategically designed via combinations of colocation, cloud and on-premises resources aligned with business objectives.

The 2024 State of the Data Center Report from CoreSite shows that although C-suite confidence in the economy remains high, a VUCA (volatile, uncertain, complex, ambiguous) environment has many business leaders proceeding with caution when it comes to their IT and data ecosystems, with an emphasis on cost control and predictability, flexibility and risk management.

However, this cautious approach also must accommodate a growing volume of resource-intensive artificial intelligence (AI) and other high-density workloads critical to organizational growth and innovation. The result of this dichotomy is an accelerated embrace of hybrid IT ecosystems to support varying types of data and workload needs.

Specifically, 98% of organizations say they have currently adopted or plan to adopt a hybrid model using colocation, private cloud and public cloud to manage their workloads.


Source: CoreSite

"The 2024 data demonstrates that IT leaders are increasingly relying on hybrid IT environments to support business objectives, including better cost control and predictability, and to efficiently deploy specific workloads to maximize benefits," said Juan Font, CoreSite President and CEO and American Tower Senior Vice President. "Underscored by the evolving needs of AI and other high-density workloads, modern hybrid IT strategies allow for the type of flexibility that can reduce infrastructure footprints and focus IT resources and talent on growth, while delivering the performance organizations need to remain competitive."

Key insights from this year's report include:

Connection Reigns Supreme

Companies need to directly connect to the cloud and interconnect systems and locations to transfer large-scale amounts of data, while keeping latency, cost, security and quality in mind. In fact, cloud interconnection was the No. 1 reason for using colocation for nearly half of the 22 workloads included in the survey. However, only 31% of respondents say their current colocation provider offers interconnection to a variety of cloud providers.

Additionally, 95% of respondents said the ability of colocation providers to offer native, direct connections to the major cloud providers is important, with 69% citing it as very important.

A Public Cloud Exodus

The public cloud has historically been seen as an essential platform to replace legacy technology or quickly add new capabilities to improve agility and flexibility. However, "cloud smart" hybrid IT infrastructure environments are increasingly valued over an "all in" cloud approach for their ability to effectively and efficiently address cost concerns while meeting performance and compliance requirements.

Most participants in the survey say they have considered a move from public cloud to colocation across 22 different workloads, led by generative AI (GenAI) applications, BI/analytics, and IoT connectivity and management. Compared with the 2023 study, the use of public cloud is trending down across all workloads.

AI Is Hybrid IT Accelerant

Heightened use of AI — which requires more computing resources and high data volumes — is forcing IT leaders to re-evaluate options for hosting these and other high-density workloads within current budget constraints. The 2024 results show a shift of AI-specific workloads from on-prem environments, primarily to colocation data centers.

Additionally, at least three-quarters of respondents in this year's survey said they are considering moving AI-related workloads from the public cloud to a colocation data center, including GenAI applications (91%), chatbots (81%), predictive analytics (79%) and augmented AI applications (76%).

"IT executives have more options than ever for locating computing resources, and the CoreSite 2024 State of the Data Center Report demonstrates how highly customized hybrid environments that include colocation are becoming the option of choice for organizations that must remain highly competitive while continually managing cost predictably," said John Gallant, Enterprise Consulting Director at CIO. "These often-competing pressures only will become more salient with AI's explosive growth in the coming years. Adopting an ecosystem — and regularly optimizing that ecosystem — with a mix of colocation, private cloud and public cloud capabilities is a trend that likely will continue to remain dominant in the coming years."

Methodology: The report is based on a quantitative survey of 300 CIOs, CTOs and other IT decision-makers, plus in-depth interviews with seven senior technology executives from financial services, healthcare, retail and SaaS organizations. Foundry, an IDG, Inc. company, conducted the research.

Related Links

Hot Topics

The Latest

The Performance Bottleneck in Enterprise AI Has Been Exposed – Now We Must Fix It

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

What Capital Markets Can Teach Enterprises About Integrated Data Infrastructure

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

Why AI in IT Is Producing High Productivity but Heavy Workloads

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Implementing AI Isn't the Hard Part Anymore - Adoption Is

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

The Incident Load Is Rising, but Recovery Times Aren't Moving

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

Your AI Pipeline May Have a Document Observability Problem

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

The SIEM Isn't the Problem. Your Telemetry Architecture Is

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

As AI Scales Across Enterprises, Breaking Points Emerge

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

Preparing Network Infrastructure for AI Starts with the Basics

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Agentic Operations Begin with Governed Middleware Access

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

State of the Data Center 2024: Hybrid IT Adoption Accelerates

More than ever, IT executives have options for strategically locating computing resources across multiple environments, with an eye toward interconnected digital ecosystems that deliver value, performance and flexibility. These specialized digital ecosystems are being strategically designed via combinations of colocation, cloud and on-premises resources aligned with business objectives.

The 2024 State of the Data Center Report from CoreSite shows that although C-suite confidence in the economy remains high, a VUCA (volatile, uncertain, complex, ambiguous) environment has many business leaders proceeding with caution when it comes to their IT and data ecosystems, with an emphasis on cost control and predictability, flexibility and risk management.

However, this cautious approach also must accommodate a growing volume of resource-intensive artificial intelligence (AI) and other high-density workloads critical to organizational growth and innovation. The result of this dichotomy is an accelerated embrace of hybrid IT ecosystems to support varying types of data and workload needs.

Specifically, 98% of organizations say they have currently adopted or plan to adopt a hybrid model using colocation, private cloud and public cloud to manage their workloads.


Source: CoreSite

"The 2024 data demonstrates that IT leaders are increasingly relying on hybrid IT environments to support business objectives, including better cost control and predictability, and to efficiently deploy specific workloads to maximize benefits," said Juan Font, CoreSite President and CEO and American Tower Senior Vice President. "Underscored by the evolving needs of AI and other high-density workloads, modern hybrid IT strategies allow for the type of flexibility that can reduce infrastructure footprints and focus IT resources and talent on growth, while delivering the performance organizations need to remain competitive."

Key insights from this year's report include:

Connection Reigns Supreme

Companies need to directly connect to the cloud and interconnect systems and locations to transfer large-scale amounts of data, while keeping latency, cost, security and quality in mind. In fact, cloud interconnection was the No. 1 reason for using colocation for nearly half of the 22 workloads included in the survey. However, only 31% of respondents say their current colocation provider offers interconnection to a variety of cloud providers.

Additionally, 95% of respondents said the ability of colocation providers to offer native, direct connections to the major cloud providers is important, with 69% citing it as very important.

A Public Cloud Exodus

The public cloud has historically been seen as an essential platform to replace legacy technology or quickly add new capabilities to improve agility and flexibility. However, "cloud smart" hybrid IT infrastructure environments are increasingly valued over an "all in" cloud approach for their ability to effectively and efficiently address cost concerns while meeting performance and compliance requirements.

Most participants in the survey say they have considered a move from public cloud to colocation across 22 different workloads, led by generative AI (GenAI) applications, BI/analytics, and IoT connectivity and management. Compared with the 2023 study, the use of public cloud is trending down across all workloads.

AI Is Hybrid IT Accelerant

Heightened use of AI — which requires more computing resources and high data volumes — is forcing IT leaders to re-evaluate options for hosting these and other high-density workloads within current budget constraints. The 2024 results show a shift of AI-specific workloads from on-prem environments, primarily to colocation data centers.

Additionally, at least three-quarters of respondents in this year's survey said they are considering moving AI-related workloads from the public cloud to a colocation data center, including GenAI applications (91%), chatbots (81%), predictive analytics (79%) and augmented AI applications (76%).

"IT executives have more options than ever for locating computing resources, and the CoreSite 2024 State of the Data Center Report demonstrates how highly customized hybrid environments that include colocation are becoming the option of choice for organizations that must remain highly competitive while continually managing cost predictably," said John Gallant, Enterprise Consulting Director at CIO. "These often-competing pressures only will become more salient with AI's explosive growth in the coming years. Adopting an ecosystem — and regularly optimizing that ecosystem — with a mix of colocation, private cloud and public cloud capabilities is a trend that likely will continue to remain dominant in the coming years."

Methodology: The report is based on a quantitative survey of 300 CIOs, CTOs and other IT decision-makers, plus in-depth interviews with seven senior technology executives from financial services, healthcare, retail and SaaS organizations. Foundry, an IDG, Inc. company, conducted the research.

Related Links

Hot Topics

The Latest

The Performance Bottleneck in Enterprise AI Has Been Exposed – Now We Must Fix It

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

What Capital Markets Can Teach Enterprises About Integrated Data Infrastructure

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

Why AI in IT Is Producing High Productivity but Heavy Workloads

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Implementing AI Isn't the Hard Part Anymore - Adoption Is

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

The Incident Load Is Rising, but Recovery Times Aren't Moving

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

Your AI Pipeline May Have a Document Observability Problem

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

The SIEM Isn't the Problem. Your Telemetry Architecture Is

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

As AI Scales Across Enterprises, Breaking Points Emerge

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

Preparing Network Infrastructure for AI Starts with the Basics

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Agentic Operations Begin with Governed Middleware Access

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...