Data Center Components Market: 6 Decisions Companies Should Make to Improve Power Efficiency, Cooling, and Infrastructure Reliability

The global data center components market was valued at USD 260 billion in 2025 and is projected to reach USD 1,009 billion by 2035, growing at a compound annual growth rate (CAGR) of 14.5% from 2026 to 2035. Growth is being driven by expanding AI workloads, hyperscale data center construction, cloud adoption, and rising demand for high-capacity power systems, advanced cooling, networking equipment, and high-density racks. As operators scale infrastructure to accommodate increasingly demanding computing workloads, decisions about component selection, energy efficiency, equipment availability, and lifecycle costs are becoming critical.

Data center components span IT infrastructure, power infrastructure, cooling systems, racks and enclosures, monitoring and management systems, and physical security equipment. These systems must work together to deliver reliable computing capacity. A mismatch between server density, electrical capacity, and cooling capability can delay deployment, increase operating costs, and limit the performance of an entire facility.

For data center operators, cloud providers, colocation companies, and infrastructure suppliers, the priority is to ensure that component investments support current workloads while allowing facilities to adapt to future computing requirements.

1. Match Component Selection to Actual Workload Requirements

The first decision is to determine which components are required for the workloads a facility will support. Traditional enterprise computing, cloud infrastructure, AI model training, and high-volume inference place different demands on servers, networking, power distribution, and cooling.

For example, AI training clusters may require high-performance accelerators, high-bandwidth networking, and specialized thermal management. General-purpose enterprise workloads may place greater emphasis on storage capacity, virtualization, availability, and predictable operating costs.

Before procurement, companies should assess:

• Computing requirements: Determine the mix of CPUs, GPUs, AI accelerators, memory, and storage needed for planned workloads.
• Network performance: Evaluate bandwidth, latency, interconnect requirements, and the volume of data exchanged between computing systems.
• Rack density: Estimate the power and cooling requirements of individual racks rather than relying only on facility-wide capacity.
• Availability targets: Identify redundancy and recovery requirements for critical workloads.
• Expansion requirements: Ensure that the selected components can accommodate future servers, storage, and networking without extensive redesign.

Operators should avoid selecting components solely on the basis of maximum performance or purchase price. A high-performance server can become an inefficient investment if the network cannot feed it data or the facility cannot supply sufficient power and cooling.

The most effective approach is to design the infrastructure around representative workloads and evaluate components as part of an integrated system.

2. Prioritize Power Infrastructure Before Expanding Computing Capacity

Power availability is becoming a major constraint on data center development. AI accelerators and high-density computing systems increase electrical demand, requiring operators to reassess transformers, switchgear, uninterruptible power supply (UPS) systems, power distribution units (PDUs), busways, and backup generators.

The International Energy Agency estimated that global data center electricity consumption reached approximately 415 terawatt-hours (TWh) in 2024 and could rise to around 945 TWh by 2030 in its 2025 base-case outlook. AI is an important driver of this growth, increasing the importance of reliable and efficient electrical infrastructure.

Companies should evaluate:

• Grid availability: Confirm the capacity and timing of the site's electrical connection before committing to major equipment purchases.
• Power distribution: Select transformers, switchgear, PDUs, and busways that can support planned loads and future expansion.
• Backup systems: Determine the appropriate UPS, battery, generator, and redundancy configuration for critical operations.
• Power efficiency: Measure conversion losses and the efficiency of electrical distribution across different operating loads.
• Monitoring and control: Deploy power monitoring to identify abnormal consumption, emerging capacity constraints, and equipment faults.

Operators should also examine how power architectures may evolve as rack densities increase. Equipment that meets today's requirements may not support future AI systems without costly upgrades.

Power planning should therefore be completed alongside IT procurement, not treated as a separate facilities task after servers have been selected.

3. Invest in Cooling That Can Support Higher Rack Densities

Cooling is becoming one of the most important component decisions as AI workloads increase heat generation within individual racks. Conventional air cooling remains suitable for many enterprise and general-purpose environments, but high-density AI deployments can require direct-to-chip liquid cooling, coolant distribution units (CDUs), cold plates, pumps, heat exchangers, or other advanced thermal-management systems.

The appropriate solution depends on heat load, rack configuration, facility design, operating conditions, and the equipment manufacturer's requirements.

Before selecting cooling infrastructure, companies should consider:

• Thermal density: Determine the expected heat load per rack and how it will change as computing capacity expands.
• Cooling technology: Compare air cooling, rear-door heat exchangers, direct-to-chip liquid cooling, and immersion cooling where relevant.
• Retrofit feasibility: Assess whether existing facilities can accommodate liquid distribution, additional heat rejection, and associated maintenance requirements.
• Water and energy use: Evaluate cooling efficiency alongside water consumption and the site's environmental constraints.
• Monitoring: Use temperature, flow, pressure, and equipment-status data to identify potential thermal problems early.

Recent industry data reinforces the importance of this decision. In October 2026, Dell'Oro Group reported that the global data center physical infrastructure market grew 27% year over year to USD 14 billion in the second quarter of 2026, with thermal management remaining its fastest-growing segment. Direct liquid cooling and heat-rejection equipment were among the strongest areas of demand.

For operators, the implication is straightforward: cooling systems must be selected according to actual and projected heat loads. Installing advanced cooling without sufficient workload demand can raise capital costs unnecessarily, while underestimating future heat density can limit a facility's usable computing capacity.

4. Design Racks, Networking, and Infrastructure for Scalability

Racks and networking equipment influence how efficiently data center capacity can be deployed and expanded. Higher-density AI environments require careful coordination between rack dimensions, structural capacity, power delivery, cooling connections, cable management, and network topology.

A rack should not be treated as a standalone enclosure. Its design affects installation time, service access, airflow, cabling complexity, and the ability to replace or upgrade equipment.

Companies should focus on:

• Rack-level power planning: Match rack capacity and distribution equipment to expected server and accelerator loads.
• Physical compatibility: Verify dimensions, weight limits, cooling connections, and service clearances before deployment.
• Network capacity: Select switches, optical transceivers, fiber, and cabling that meet bandwidth and latency requirements.
• Cable management: Reduce congestion and ensure that power and data connections remain accessible for maintenance.
• Modular expansion: Use repeatable rack and infrastructure designs where possible to simplify future deployments.
• Supplier interoperability: Confirm that components from different vendors can work together within the proposed architecture.

Networking requirements are especially important for AI clusters, where large amounts of data move between accelerators, storage systems, and other servers. Insufficient bandwidth can limit overall system performance even when computing and cooling capacity are adequate.

A scalable design should allow operators to add capacity with minimal disruption, while preserving access for maintenance and avoiding unnecessary changes to adjacent infrastructure.

5. Strengthen Component Sourcing and Lifecycle Management

Data center projects depend on a broad supply chain spanning semiconductors, memory, storage, electrical equipment, cooling systems, networking components, and specialist installation services. Delays in a single critical component can postpone commissioning or leave expensive infrastructure underutilized.

Rapid technology development creates another challenge: operators must avoid purchasing equipment that becomes difficult to support or expand before the facility reaches its planned operating life.

Procurement teams should assess:

• Lead times: Identify components with long manufacturing, delivery, or installation schedules.
• Supplier concentration: Evaluate the risk of relying on a single vendor for critical infrastructure.
• Compatibility and support: Confirm software, firmware, spare-parts availability, warranties, and service arrangements.
• Standardization: Reduce unnecessary variation in component models where standardization simplifies maintenance.
• Lifecycle costs: Compare acquisition costs with energy consumption, maintenance, replacement, and upgrade expenses.
• Phased procurement: Align component deliveries with construction milestones and verified demand.

Dell'Oro Group's August 2026 forecast projected that the data center IT semiconductors and components market would reach USD 1.8 trillion by 2030, reflecting strong investment across accelerators, memory, storage, CPUs, and network interface cards. The forecast also highlighted continuing supply constraints and elevated pricing in certain component categories.

For infrastructure buyers, this reinforces the need to coordinate IT procurement with power, cooling, and construction schedules. Buying computing equipment before the supporting infrastructure is ready can tie up capital without producing usable capacity.

6. Use Monitoring and Energy Metrics to Improve Total Cost of Ownership

Once a data center becomes operational, its performance depends on how effectively components are monitored, maintained, and optimized. Equipment that operates reliably at installation may become less efficient as workloads change, utilization increases, or cooling and power systems age.

Monitoring and management systems can help operators identify inefficiencies, predict maintenance needs, and understand how individual infrastructure components affect overall facility performance.

Companies should track:

• Power usage effectiveness (PUE): Compare total facility energy consumption with the energy used by IT equipment.
• Component efficiency: Monitor UPS performance, cooling-system efficiency, server utilization, and electrical losses.
• Thermal performance: Track rack temperatures, coolant conditions, cooling capacity, and thermal incidents.
• Availability: Measure equipment failures, downtime, maintenance frequency, and recovery time.
• Capacity utilization: Identify underused servers, power systems, cooling equipment, and rack space.
• Cost per unit of computing: Evaluate energy and infrastructure costs relative to useful workload output.

The objective is to improve infrastructure performance without compromising reliability. For example, a cooling system operating continuously at maximum capacity may consume unnecessary energy under lighter loads, while aggressive efficiency adjustments could create thermal risks if they ignore changes in computing demand.

Automated monitoring and predictive maintenance can help operators balance these trade-offs, provided the underlying data is accurate and the controls are tested. Performance metrics should also be reviewed over time so that expansion decisions reflect actual operating conditions rather than original design assumptions.

Conclusion

The global data center components market is projected to grow from USD 260 billion in 2025 to USD 1,009 billion by 2035, at a CAGR of 14.5% from 2026 to 2035. AI adoption, cloud expansion, and increasing rack densities are creating demand for advanced IT equipment, power infrastructure, cooling systems, networking, and intelligent monitoring.

Companies should make six connected decisions: match components to workloads, prioritize power availability, select cooling for projected heat loads, design for scalability, strengthen procurement resilience, and optimize lifecycle performance through monitoring. The most effective infrastructure strategy treats computing, power, cooling, networking, and operations as one integrated system.

As data center requirements continue to evolve, operators that align component selection with measurable performance, reliable supply, and long-term operating costs will be better positioned to expand capacity without creating avoidable infrastructure bottlenecks.

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