Artificial intelligence (AI) is reshaping life as we know it. So, it comes as no surprise that the technology to support AI is also recalibrating. As a result of exponential growth in AI, hybrid IT architectures are experiencing increased requirements for power, low-latency connectivity, secure, scalable infrastructure and cost predictability. To support these demands, IT decision-makers are reassessing their infrastructure strategies, specifically where their AI workloads should run across colocation, cloud and on-premises environments.
Every IT environment has its strengths and limitations. On-premises environments offer tight control over critical systems. Cloud offers rapid scalability, on-demand computing resources and agility. Colocation offers high-density power, diverse connectivity options, physical security and operational resiliency. These factors directly impact the performance of AI inference workloads. The bottom line is that every application does not belong in the same environment.
With hybrid IT as the “go-to” approach for infrastructure architecture, organizations are placing workloads in the environment that best meets their specific requirements to deploy enterprise AI while balancing agility, performance and control. Results from the 2026 State of the Data Center Report show the heightened reliance on hybrid IT, with nearly 70% of IT leaders favoring colocation and hybrid environments for their AI workloads.
Colocation has moved beyond rented data center space to become a trusted environment for AI inference workloads. According to research of 300+ IT leaders, increased security, scalability, performance and uptime are among the leading technical reasons organizations move workloads into colocation environments.
With colocation, organizations have access to high-density infrastructure, scalable network and data center capacity, public clouds and technology partners in one location. This local access can help reduce AI complexity and accelerate the exchange of data to ensure the applications that individuals and businesses use every day are available when needed.
AI workload placement is the process of determining the most strategic location for an AI application to run — on premises, in a colocation data center, in the cloud or across multiple environments — based on the specific needs of a workload.
The greater rack-level power requirements of AI infrastructure have necessitated that businesses expand beyond on-premises environments, with survey results showing a drop from 46% to 18% for on-premises AI deployments in one year.
Purpose-built colocation data centers can provide the high-density power, advanced cooling — including liquid cooling technology — and scale to support AI inferencing infrastructure. Colocation facilities are also designed to use power and cooling resources more efficiently than on-premises facilities for more sustainably focused operations.
Latency is an important consideration and a key differentiator for optimizing AI application performance. While AI data centers that train large language models (LLMs) can be built anywhere with available land and power, colocation data centers need to be close to where networks come together and data is created and consumed. AI inference workloads are generally latency-sensitive, requiring rapid, if not real-time, responses. For example, in an emergency response, real-time AI data maps the fastest route for an ambulance driver to quickly reach a hospital.
In latency-sensitive use cases, network design also matters. Each network hop can introduce delays and operational risk. Think of it as the difference between a nonstop flight and a flight with multiple stops and layovers. Colocation allows organizations to place infrastructure closer to users, data sources, networks and public cloud providers to minimize latency.
Colocation data centers like CoreSite that offer direct connections to public clouds from within their buildings can further reduce latency. These on-site cloud on-ramps eliminate multiple network hops to improve speed. They also bypass the public internet to enhance performance and reduce risk.
AI inferencing is the process of applying existing AI model data to new data to enhance the capabilities of an application. Examples include credit card fraud detection, virtual assistants, smart security cameras and medical imaging analysis.
AI workloads benefit from robust interconnection and a broad digital ecosystem. A colocation facility with an extensive ecosystem of enterprises, networks, clouds, service providers and technology partners can serve as an AI inference and interconnection hub. Organizations can leverage this infrastructure to position their AI workloads close to public clouds, data sources, inference zones, users and partners to provide lowest-latency data exchange, while retaining control over how data and workloads move between environments. Network-rich colocation facilities with diverse ecosystems can also minimize infrastructure complexity, distance and network hops by offering access to critical resources within its walls.
Direct connections to public clouds simplify and increase data delivery speed, reduce egress fees and provide more consistent connectivity to cloud platforms.
Operating a hybrid IT architecture entails careful integration for seamless data transfer between deployed environments and across the necessary networks to achieve the reliability customers expect. The scale of the colocation data center ecosystem helps ease and streamline integration by offering connections from within the facility, reducing unnecessary network hops and enabling access across multiple environments.
Successful integration also requires experienced people. Colocation data centers offer access to an experienced team of solution architects, engineers, cloud specialists, security professionals and operations personnel. These experts can serve as an extension of your internal team and assist with infrastructure design, implementation, integration, monitoring and ongoing management.
As you evaluate and redistribute your IT workloads, partnering with the right IT infrastructure providers can help drive your business success. The strongest hybrid IT strategy places each workload in the environment that best meets its unique needs. Colocation can provide the foundation that supports specific workload requirements, helping your organization reduce complexity and effectively connect AI workloads across multiple environments.
Download the 2026 State of the Data Center Report for additional insights from IT leaders on how they are fine-tuning their hybrid IT strategies to manage AI-related complexity.