Colocation Enters a New Phase Due to AI and Cloud Pressure

Data center colocation has moved beyond a tactical solution for companies that didn’t want to build their own data center (DC). With the growth of hybrid cloud, Artificial Intelligence, edge computing, and high-density workloads, the market is becoming a critical layer of the digital infrastructure. Companies no longer seek just rack space; they look for power, connectivity, security, advanced cooling, regulatory compliance, and scalable capacity without bearing the full cost of setting up an own data center.

This shift is evident in industry conversations. For years, colocation was described as renting space in shared facilities. While that remains accurate, it’s no longer sufficient. A modern colocation provider acts as an interconnection hub among enterprises, telecom operators, public clouds, content platforms, private networks, managed services, and emerging AI workloads. The technical space matters, but the network of connections surrounding it is becoming increasingly important.

Demand isn’t driven by just one factor. The migration to hybrid models, the need to reduce capital expenditure, regulatory pressures on data, increasing traffic volumes, the surge in real-time applications, and the arrival of AI servers with much higher power requirements per rack all combine. Many organizations are reevaluating their strategies: building, leasing, outsourcing partially, or blending multiple options.

What Sets Colocation Apart from Other Types of Data Centers

Not all data centers follow the same model. Hyperscale centers are designed for large cloud or tech platforms. Enterprise data centers are typically managed by a single organization. Edge data centers bring capacity closer to end-users to reduce latency. Colocation offers a compelling middle ground: shared facilities with dedicated spaces, power, and connectivity tailored to each client’s needs.

Colocation stands out because it allows a company to maintain control over its hardware without managing all the physical complexity. Clients can host their servers, cabinets, security appliances, networking equipment, or backup infrastructure in a professional environment with redundant power, cooling, physical security, monitoring, and multiple operator connectivity.

This balance explains its growth. Many companies don’t want to move everything to the public cloud but also prefer not to operate outdated, inefficient, or non-redundant technical rooms. Colocation offers a third way: own infrastructure within a specialized facility, connected to clouds and external networks.

AI Is Changing Colocation Requirements

Artificial Intelligence is transforming data center design. Servers with GPUs and accelerators require more power, enhanced cooling, and improved electrical design. Where a 5 or 10 kW rack sufficed for many enterprise workloads, increasingly dense configurations are emerging—especially for training, inferencing, HPC, and data analytics.

For colocation providers, this creates both opportunity and pressure. The opportunity lies in attracting clients needing high-density capacity without building a dedicated campus. The challenge is to adapt buildings, rooms, power feeds, electrical distribution, and cooling systems to meet demands that often exceed traditional infrastructures.

Preparing colocation for AI involves more than placing expensive servers in a room. It requires studying rack weight, power distribution, cooling capacity, network latency, security, specialized maintenance, and phased expansion capabilities. Often, the difference between a ready and a non-ready provider is in subtle details: inlet temperatures, airflow, electrical redundancy, circuit availability, or support for liquid cooling.

Edge, Sustainability, and Connectivity: Three Key Trends

The growth of the colocation market relies on several simultaneous trends. The first is edge computing. Not all workloads can be processed far from the end-user. Industrial applications, video, gaming, IoT, connected vehicles, digital health, and low-latency financial services require closer processing proximity.

The second trend is sustainability. Data centers consume significant energy and face increased public scrutiny. Clients inquire about renewable sources, PUE, cooling efficiency, carbon footprint, water use, and operational efficiency. It’s no longer enough to claim “green” buildings; companies need verifiable data for ESG reporting and regulatory compliance.

The third is interconnection. In hybrid environments, a data center’s value extends beyond its square footage to who it connects with. Operators, clouds, private networks, CDNs, neutral points, SaaS platforms, and security providers all contribute to its appeal.

Providers that only offer space and power are increasingly less competitive. Clients now expect a platform: neutral connectivity, remote support, remote hands services, monitoring, backup options, cloud integration, physical security, and the ability to adapt infrastructure to evolving needs.

Spain Gains Attention in the European Market

Spain is gaining prominence in the data center landscape due to several factors: available land in specific regions, strong international connectivity, fiber deployment, renewable energy, favorable geographic location, and growing interest in European digital sovereignty. Madrid remains the primary node, but expansion is now extending to Aragón, Catalonia, Castilla-La Mancha, Valencia, Extremadura, Andalusia, and other areas.

Installed capacity in commercial data centers in Spain reached 439 MW by the end of 2025, according to SpainDC, representing strong growth from the previous year. The association projects a significant leap by 2030 if the planned pipeline is executed. However, that potential heavily depends on a crucial factor: actual available power capacity.

Colocation can play a vital role in this growth, as not all companies need or can afford a hyperscale campus. Many seek locations with good connectivity, European regulatory compliance, local support, and phased expansion options. There is room for global operators, regional providers, and specialized vendors.

From Cost Savings to Flexibility

A traditional argument for colocation has been cost savings compared to building an own data center. While still valid, it’s no longer the sole reason. Building a data center requires significant investment, permits, design, civil works, electrical supply, redundancy, cooling, security, specialized staff, and ongoing maintenance. For many companies, this effort makes little sense if their core business isn’t infrastructure management.

Colocation transforms some of these capital expenses into operational costs, enables phased growth, and grants access to facilities with certifications, connectivity, and security that would be hard to replicate internally. Additionally, it reduces the risk of being stuck with an aging or inflexible owned data center that cannot keep up with new densities.

The decision isn’t universal. Critical sectors like government, banking, or industries with highly sensitive data may prefer or require owned or hybrid facilities. Still, even in those cases, colocation is valuable for disaster recovery, international expansion, interconnection, backups, or specific workloads.

Risks: Energy, Costs, and Concentration

Colocation growth isn’t without challenges. Energy is a primary concern. Power availability, electricity costs, and grid connection timelines shape project feasibility. A provider might have land, financing, and interested clients, but without granted power and a realistic schedule, the project stalls.

Concentration risk also exists. Certain European markets are highly constrained by land scarcity, electrical limitations, or social opposition. Expansion into secondary regions can alleviate some issues but requires reliable connectivity, permits, skilled talent, and solid energy agreements.

Price is another factor. AI workloads tend to push capacity costs upward, especially in high-density-ready centers. Companies must carefully compare options: hosting standard racks versus capacity tailored for GPUs, liquid cooling, and low-latency connectivity.

Colocation Becomes a Strategic Infrastructure Element

Colocation is no longer just an real estate category or a technical service for companies avoiding own DCs. It’s becoming a strategic enabler in the digital economy. Hybrid clouds, AI, private networks, edge, business continuity, and compliance increasingly rely on shared, interconnected, and specialized facilities.

Market growth will be uneven. Providers offering true capacity, interconnection, efficiency, security, managed services, and customization for high-density workloads will thrive. Those still perceiving colocation as mere square meters with power will struggle.

For clients, choosing a provider no longer reduces to price per rack. It requires evaluating power availability, connectivity options, location, redundancy, certifications, sustainability, support, scalability, and future AI workload capacity.

The next decade won’t be defined solely by the number of data centers built but by the kind of capacity delivered. Here, colocation could become one of the most critical pieces of the technological infrastructure—less visible than AI models or public clouds, but indispensable to enable all other functions.

Frequently Asked Questions

What is data center colocation?

It’s a model where a company hosts its servers and equipment in a third-party data center, leveraging their power, cooling, physical security, and connectivity.

How does colocation differ from cloud?

In colocation, the client maintains control over their hardware. In cloud services, resources are virtualized or managed by the provider on shared infrastructure.

Why is AI driving colocation?

Because AI workloads demand more power, cooling, connectivity, and deployment capacity. Many clients prefer ready-made facilities over building their own data centers.

What should a company consider before choosing colocation?

They should evaluate available power, connectivity, location, redundancy, security, certifications, remote support, sustainability, costs, and scalability for AI workloads.