How Fast Is the Data Centre IT Market Growing?

The global data center IT market was valued at approximately USD 78.34 billion in 2025 and is projected to reach USD 288.76 billion by 2035, growing at a CAGR of 13.9% from 2026 to 2035.

This expansion reflects rising demand for AI computing, cloud platforms, enterprise applications and high-performance data processing. However, market growth does not translate into equal demand across every technology category. AI-focused facilities may prioritize accelerators and high-speed networking, while enterprise environments may focus on virtualization, storage efficiency, cybersecurity and infrastructure modernization.

For technology buyers, this distinction matters: the best investment depends on the workload being supported, not simply on the overall growth of the market.

1. Why Is AI Changing Data Center IT Investment?

AI is increasing the need for computing capacity while changing the technical requirements of data center infrastructure.

Gartner forecasts that worldwide spending on data center systems will reach USD 822 billion in 2026, up from USD 506 billion in 2025. This reflects a sharp increase in investment as technology providers and enterprises expand AI infrastructure.

Three purchasing priorities are becoming more important:

• Servers and accelerators: AI training and inference can require specialized processors, greater memory capacity and higher-density computing.
• Networking equipment: Faster connections between servers help prevent data movement from limiting overall system performance.
• Storage systems: AI applications need infrastructure capable of feeding large datasets to computing resources and managing the output generated by models.

The business implication is that these components should be planned as an integrated system. Purchasing powerful servers without evaluating network bandwidth, storage throughput and memory requirements can leave expensive computing resources underutilized.

2. Are Hardware Costs Becoming a Bigger Concern?

Performance is only one part of the investment decision. Hardware availability, memory prices, energy consumption and replacement cycles can significantly affect the total cost of ownership.

Gartner has identified rising hardware and memory costs, supply constraints and competing AI investment priorities as pressures on technology budgets. This creates a difficult balance: delaying an upgrade may limit performance, but buying equipment too early can lock an organization into expensive capacity before its workloads are fully established.

Before committing to a large procurement, businesses should assess:

• Workload utilization: Are existing servers operating near capacity, or could consolidation and optimization meet demand?
• Memory requirements: Will planned AI applications require higher-memory configurations that may have longer lead times or higher costs?
• Upgrade flexibility: Can the architecture support additional computing capacity without replacing the entire infrastructure?
• Lifecycle costs: Do expected performance gains justify equipment, maintenance, power and cooling expenses over the planned operating period?

A phased deployment can help organizations match infrastructure spending to verified demand, particularly when AI adoption and workload requirements are still evolving.

3. Why Are Power and Efficiency Now IT Procurement Issues?

Data center IT decisions increasingly affect electricity consumption and operating costs. The International Energy Agency projects global data center electricity use will rise from approximately 485 TWh in 2025 to 950 TWh by 2030. Electricity consumption at AI-focused data centers is expected to grow even faster.

This makes energy efficiency a purchasing consideration, not just a facility-management concern. Servers with higher computing performance per unit of electricity, efficient workload scheduling and better utilization can help reduce the cost of delivering digital services.

IT teams should evaluate equipment using more than purchase price or peak performance. Useful measures include:

• Performance per watt for the intended workload.
• Energy consumption under typical operating conditions.
• Rack density and compatibility with existing cooling systems.
• Utilization rates and the potential to consolidate workloads.
• Software capabilities for monitoring, automation and capacity planning.

The lowest-priced server is not necessarily the least expensive option over its operating life. Equally, the most powerful configuration may be unnecessary if the workload does not use its additional capacity.

4. How Should Businesses Balance Servers, Storage and Networking?

Servers often attract the largest share of attention in AI infrastructure plans, but system performance depends on how effectively all components work together.

Servers: Assess whether workloads require general-purpose CPUs, accelerated computing or a combination of both. Different applications have different processing and memory requirements.

Storage: Consider data access speeds, capacity growth, backup requirements and the cost of retaining large datasets. AI projects may need high-throughput storage, while other applications prioritize reliability and long-term retention.

Networking: Evaluate bandwidth, latency and connectivity between servers, storage systems and external services. Network constraints can undermine the benefits of faster processors.

Management software and security: Centralized monitoring, automation, access controls and workload orchestration can help operators manage increasingly complex environments.

The right mix varies by deployment. A hyperscale AI facility, a colocation provider and a corporate data center may have very different priorities even when all three are increasing capacity.

5. Should Companies Expand Existing Infrastructure or Move to Cloud and Colocation?

There is no single model that works for every organization. The choice depends on workload predictability, control requirements, capital availability and the speed at which capacity is needed.

• Expanding an existing data center may suit organizations that need direct control over infrastructure and already have sufficient power, cooling and operational expertise.
• Colocation can provide access to established facilities and connectivity without requiring the business to build and operate an entire site.
• Cloud infrastructure can offer flexibility for variable workloads and reduce the need for upfront hardware purchases, although recurring costs require careful monitoring.
• Hybrid infrastructure can help organizations retain control over selected workloads while using cloud resources for variable demand or specific applications.

The comparison should include more than the initial investment. Data transfer, licensing, security, availability, staffing and long-term operating expenses can change which option offers the best value.

6. What Should Buyers Check Before Committing to an Upgrade?

Before approving a data center IT investment, decision-makers should establish a clear connection between business requirements and technical specifications.

A practical evaluation should cover five questions:

1. What problem must the investment solve? Define the expected workload, performance improvement or capacity shortfall.
2. Which components are limiting performance today? Measure server utilization, storage throughput, network latency and memory pressure before replacing equipment.
3. Can the supply chain meet the required schedule? Confirm availability, delivery timelines, support commitments and compatibility.
4. What will the infrastructure cost to operate? Include electricity, cooling implications, maintenance, software and staffing.
5. How easily can capacity be expanded or reconfigured? Favor architectures that can adapt as workloads, AI models and business requirements change.

These checks help distinguish investments that solve a genuine operational problem from upgrades driven mainly by expectations about future technology demand.

What Does This Mean for the Data Center IT Market?

The expansion of AI and cloud computing is creating opportunities across servers, storage, networking and infrastructure-management software. But demand alone does not guarantee that every investment will deliver an acceptable return.

Businesses that connect hardware decisions to workload requirements, energy efficiency, procurement timelines and lifecycle costs will be better positioned to manage the next phase of infrastructure expansion.

For suppliers, the same shift places greater importance on demonstrating measurable performance, interoperability, reliable delivery and operational value—not just higher specifications.

The central question for the industry is therefore not simply how much data center IT capacity will be deployed, but which technologies can deliver the right balance of computing performance, scalability and cost as AI workloads expand.

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