Data Center IT Market: 6 Decisions Companies Should Make to Optimize Infrastructure, Scale AI Workloads, and Improve Operational Efficiency

The global data center IT market was valued at USD 78.34 billion in 2025 and is projected to reach approximately USD 288.76 billion by 2035, expanding at a compound annual growth rate (CAGR) of 13.9% from 2026 to 2035. This growth is being driven by rising demand for artificial intelligence (AI), cloud computing, high-performance computing (HPC), enterprise digitalization, and data-intensive applications. As organizations process increasingly complex workloads, decisions about servers, storage, networking, and infrastructure management are becoming central to technology investment and operational performance.

Data center IT infrastructure supports the computing, storage, networking, and management capabilities required to run digital services. AI model training and inference are increasing demand for accelerated servers, GPUs, high-bandwidth memory, high-speed networking, and scalable storage systems. At the same time, enterprises must continue supporting databases, business applications, cybersecurity, virtualization, and conventional cloud workloads. These requirements are creating a more complex infrastructure environment in which performance, scalability, utilization, and cost must be evaluated together.

Recent investment trends reinforce this shift. In October 2026, TrendForce projected that combined capital expenditure by nine leading cloud service providers could reach USD 957 billion in 2026 and exceed USD 1.5 trillion in 2027. It also forecast AI server revenue to approach USD 479 billion in 2026, highlighting the scale of investment in accelerated computing. However, rising component prices and supply constraints mean that increased spending does not automatically translate into better infrastructure economics. <Cite refs={["turn885184search0"]} />

For enterprises, cloud providers, colocation operators, and infrastructure suppliers, the challenge is to determine which technologies to deploy, where to invest, and how to balance performance requirements against long-term operating costs.

1. Select the Right Server Architecture for AI and General-Purpose Workloads

Server architecture is one of the most important decisions in data center IT planning because it determines how efficiently infrastructure can process different workloads. Traditional CPU-based servers remain essential for databases, enterprise applications, virtualization, and general-purpose computing. AI training, large-scale inference, and other computationally intensive workloads may require GPU-accelerated servers or systems built around specialized AI accelerators.

Choosing the most powerful hardware for every application can increase acquisition costs and energy consumption without delivering proportional performance improvements. Organizations should instead match server configurations to workload characteristics, performance requirements, and expected utilization.

Companies should prioritize the following decisions:

• Classify workloads before procurement: Distinguish AI training, inference, analytics, databases, virtualization, and general-purpose applications according to their processing requirements.
• Evaluate CPU and accelerator combinations: Determine whether workloads benefit from CPUs alone, GPUs, custom AI accelerators, or heterogeneous computing architectures.
• Assess memory and interconnect requirements: Consider memory bandwidth, capacity, processor-to-processor communication, and accelerator connectivity when selecting systems.
• Measure performance per dollar: Compare acquisition costs against useful throughput, application performance, and expected utilization rather than relying solely on theoretical specifications.
• Plan for future workload changes: Select architectures that can accommodate growing AI demand and evolving software requirements without unnecessary overcapacity.

AI infrastructure investments are increasing rapidly, but companies should avoid treating every workload as an AI workload. A balanced server strategy can improve resource utilization while preserving the specialized computing capacity required for demanding applications.

2. Build a Storage Strategy That Balances Performance, Capacity, and Cost

Data center storage requirements are changing as organizations generate larger datasets, expand cloud applications, and deploy AI systems that require frequent access to training data, model parameters, and inference-related information. Storage architecture can influence application response times, data availability, computing efficiency, and total infrastructure expenditure.

Solid-state drives (SSDs) provide high performance for latency-sensitive workloads, while hard disk drives (HDDs) remain relevant for cost-effective storage of large datasets. Object storage, distributed storage systems, and tiered architectures can help organizations manage different data-access requirements across applications.

The appropriate configuration depends on how frequently information is accessed, how quickly it must be retrieved, and how long it needs to be retained.

Key priorities include:

• Segment data by access requirements: Separate frequently accessed operational data from archival information and datasets that require specialized performance.
• Match storage media to workloads: Use high-performance storage for latency-sensitive applications and lower-cost options where capacity is more important than speed.
• Plan for AI data pipelines: Ensure storage systems can supply data to GPUs and other accelerators without creating avoidable processing bottlenecks.
• Improve utilization and reduce duplication: Apply appropriate data lifecycle policies, compression, deduplication, and capacity management where technically suitable.
• Strengthen resilience and recovery: Evaluate redundancy, backup strategies, disaster recovery, and recovery-time objectives for critical systems.

Storage decisions should account for more than the cost per terabyte. Companies must consider performance, reliability, data movement, power consumption, and lifecycle costs to determine the most economical architecture for each workload.

3. Upgrade Networking to Prevent Bottlenecks in High-Performance Computing

As server density and accelerator usage increase, networking becomes a critical factor in overall data center performance. AI clusters may require high-bandwidth connections between accelerators, servers, and storage systems to support distributed training and large-scale inference. Conventional enterprise workloads also depend on reliable networking for application access, data transfers, cloud connectivity, and business continuity.

Insufficient network capacity can prevent expensive computing resources from reaching their full potential. However, upgrading every network segment to the highest available speed can increase costs without delivering equivalent benefits for all applications.

Companies should make networking decisions based on traffic patterns, workload architecture, latency requirements, and expected growth.

Important actions include:

• Identify traffic bottlenecks: Measure congestion, latency, packet loss, and throughput across server-to-server, storage, and external network connections.
• Select appropriate bandwidth: Evaluate Ethernet speeds and other suitable interconnect technologies according to the requirements of each workload.
• Plan for AI cluster communication: Assess east-west traffic, collective communication, and the interconnect requirements of distributed AI systems.
• Improve network visibility: Use monitoring and analytics tools to detect congestion, identify underutilized links, and support capacity planning.
• Design for scalability and resilience: Consider redundancy, network segmentation, fault tolerance, and future hardware compatibility.

Networking investments should be coordinated with server and storage procurement. A well-balanced architecture can prevent performance bottlenecks and help organizations extract greater value from their computing infrastructure.

4. Optimize Infrastructure Utilization and Total Cost of Ownership

Rapid data center expansion can create pressure to purchase additional IT equipment before existing resources are fully utilized. Underused servers, inefficient storage allocation, oversized configurations, and fragmented infrastructure management can increase costs while leaving significant computing capacity idle.

At the same time, aggressive consolidation may create resource contention or reduce resilience if organizations overlook application requirements. The goal should be to improve utilization without compromising performance, availability, or security.

Companies should evaluate infrastructure efficiency through several measures:

• Measure actual utilization: Track CPU, GPU, memory, storage, and network utilization across production and development environments.
• Consolidate suitable workloads: Use virtualization, containers, and workload orchestration where they improve resource allocation without introducing unacceptable risks.
• Right-size infrastructure: Match server and storage configurations to observed demand instead of maintaining excessive capacity across every application.
• Evaluate total cost of ownership: Include hardware acquisition, software licensing, power, cooling, maintenance, support, and replacement costs.
• Improve capacity forecasting: Use historical demand, business growth projections, and workload forecasts to determine when additional infrastructure is necessary.

The financial implications are becoming more significant as hardware prices rise. Dell'Oro Group reported that global data center IT semiconductor and component revenue for servers and storage systems increased 182% year over year in the second quarter of 2026, driven by strong AI infrastructure demand and higher memory and storage prices. <Cite refs={["turn885184search2"]} />

In this environment, organizations should assess whether additional spending will increase useful capacity or simply compensate for inefficient resource allocation. Better utilization can defer unnecessary purchases and improve the economics of existing infrastructure.

5. Invest in Intelligent Data Center Management, Automation, and Security

As data centers become larger and more distributed, manual infrastructure management becomes increasingly difficult. Operators must coordinate hardware provisioning, software updates, performance monitoring, capacity allocation, configuration management, incident response, and security across complex environments.

Data center management software and IT infrastructure management solutions can help centralize operations and improve visibility. Automation can support routine provisioning, configuration enforcement, incident detection, and workload orchestration. AI-assisted tools may also help identify anomalies, forecast resource demand, and recommend corrective actions.

However, automation must be implemented with appropriate controls. Incorrect configurations or poorly governed automated actions can affect multiple systems simultaneously, making change management and access control essential.

Companies should focus on these priorities:

• Centralize infrastructure visibility: Maintain a consistent view of assets, utilization, configurations, dependencies, and operational health.
• Automate repeatable processes: Apply automation to provisioning, patch management, configuration checks, and routine maintenance where appropriate.
• Introduce predictive monitoring: Use operational data to identify emerging performance problems and anticipate capacity requirements.
• Strengthen cybersecurity: Implement identity and access management, network segmentation, vulnerability management, encryption, and appropriate monitoring.
• Maintain governance and accountability: Establish approval controls, audit trails, rollback procedures, and human oversight for critical automated actions.

Management platforms should be selected according to the organization's infrastructure architecture and operational maturity. The objective is to reduce manual effort, improve reliability, and maintain control as infrastructure complexity increases.

6. Develop a Scalable Hybrid and Multi-Cloud Infrastructure Strategy

Organizations increasingly operate across on-premises data centers, public cloud platforms, colocation facilities, and edge environments. Each model offers different advantages in terms of control, scalability, latency, data governance, and cost. The challenge is determining which workloads should run in each environment and how systems should communicate across them.

Public cloud infrastructure can support rapidly changing demand and reduce the need for upfront hardware purchases. On-premises systems may be appropriate for workloads requiring greater control, predictable performance, or specific data-handling arrangements. Colocation can provide access to power, connectivity, and data center facilities without requiring organizations to build their own sites.

A hybrid strategy can offer flexibility, but poorly coordinated environments may introduce duplicated resources, inconsistent security controls, and unpredictable cloud expenditure.

Companies should consider the following actions:

• Classify workloads by placement requirements: Assess latency, data sensitivity, performance, compliance, and demand variability before selecting an infrastructure environment.
• Compare long-term economics: Evaluate cloud consumption charges, data transfer costs, hardware depreciation, support, and internal operating expenses.
• Standardize management and security: Establish consistent identity, monitoring, configuration, and governance practices across environments.
• Plan data movement carefully: Consider bandwidth, transfer costs, application dependencies, and synchronization requirements when distributing workloads.
• Prepare for future scalability: Select architectures that support changing demand without creating excessive vendor dependence or unnecessary complexity.

The most effective strategy is not necessarily to move everything to the cloud or retain every workload on-premises. Organizations should place workloads where they can meet technical and business requirements at an acceptable total cost.

Conclusion

The data center IT market is expanding as AI, cloud computing, digital services, and enterprise workloads increase demand for computing, storage, networking, and infrastructure management. With the market projected to grow from USD 78.34 billion in 2025 to USD 288.76 billion by 2035, investment decisions will have an increasingly significant impact on performance, scalability, and operating economics.

Companies should focus on six priorities: selecting the right server architecture, optimizing storage, upgrading networking, improving infrastructure utilization, investing in intelligent management and security, and developing a scalable hybrid and multi-cloud strategy.

The central challenge is to build infrastructure that can support demanding workloads without allowing costs and complexity to grow unchecked. Organizations that align hardware, software, networking, and management decisions with actual workload requirements can improve resource efficiency, reduce avoidable spending, and establish a more adaptable foundation for future digital growth.

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