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Introduction: Cooling Is the New Bottleneck of the AI Era

AI training and inference workloads are pushing rack power density to levels that traditional facilities were never designed to handle. Cooling is now one of the biggest cost and risk factors in the data center, and software is emerging as the smartest way to control it. According to IG Transformation Partners, the AI data center cooling optimization market was valued at an estimated USD 1.44 billion in 2025 and is projected to reach approximately USD 12.38 billion by 2034, growing at a remarkable CAGR of 27.0% between 2026 and 2034.

In this blog, we unpack the AI data center cooling market size, the technologies behind it, the segments and regions leading adoption, and the vendors competing for position. Whether you are a hyperscaler, colocation provider, infrastructure vendor or investor, these insights can sharpen your strategy.

What Is AI Data Center Cooling Optimization?

The market covers software and AI agent-based systems that sit above conventional building management or SCADA systems and dynamically control how liquid and air cooling infrastructure operates. It is distinct from the cooling hardware market itself: the focus is on the intelligence layer, such as AI cooling control software, monitoring and analytics, and the professional services needed to deploy them.

Unlike static, rule-based building management systems, AI-driven platforms continuously learn from workload and thermal conditions. They move facilities from reactive cooling management to predictive thermal management, adjusting before temperatures rise rather than after.

Market Size and Forecast Snapshot

• Market size in 2025: USD 1.44 billion
• Market size in 2026: USD 1.83 billion
• Market size by 2034: USD 12.38 billion
• Projected CAGR (2026–2034): 27.0%
• Largest region: North America
• Fastest-growing region: Asia-Pacific
• Largest application: Liquid cooling optimization

A CAGR of 27.0% places this among the fastest-expanding niches within data center infrastructure, driven by the collision of exploding AI demand and the need to control energy costs.

Key Growth Drivers

Explosive growth in AI workloads

AI training and inference are driving rack power densities to levels where static, rule-based cooling control is increasingly inadequate. This directly reinforces demand for dynamic, AI-driven optimization.

Cooling is a high-value efficiency target

Cooling accounts for roughly 30% of total data center energy consumption, second only to compute itself. That makes it a uniquely attractive target for AI data center energy efficiency gains compared with other facility subsystems.

Proven, publicly disclosed savings

Credible results build operator confidence. A major hyperscaler has reported a 40% reduction in cooling costs through AI implementation, and the report notes Google has demonstrated up to 40% cooling-energy savings through AI-based optimization. Recent studies also show 9.4% to 27.8% cooling-energy savings through predictive optimization and real-time modeling.

Rising data center electricity demand

The IEA estimates that global data-center electricity consumption will rise from 415 TWh in 2024 to around 945 TWh by 2030, driven mainly by AI workloads. Every efficiency gain becomes more valuable as that load grows.

Supportive policy

The U.S. Department of Energy's COOLERCHIPS 1.5 program supports advanced, water-free cooling systems for AI data centers. India's Energy Conservation and Sustainable Building Code promotes energy and water efficiency in data centers, and the European Commission's 2026 data-center energy-efficiency framework promotes energy and water performance monitoring.

Top Trends Reshaping the Market

Feed-forward predictive control

Feed-forward cooling control uses rack-power measurements to anticipate heat generation and adjust coolant distribution before temperatures actually rise. This is a technically distinct advance over conventional feedback-based control and directly addresses the lag between fast-changing AI workloads and cooling system response times.

Facility-wide AI agent orchestration

Vendors are expanding AI agents beyond cooling into broader facility management, including electrical distribution, battery dispatch and AI workload scheduling. The direction is clear: these systems must be orchestrated together.

Digital twin modeling

Physics-aware modeling of each site, built from a detailed mapping of its sensors and equipment, is becoming a standard deployment prerequisite. A digital twin data center model lets AI control systems be tailored to each facility's unique thermal and electrical characteristics.

Market Segmentation Analysis

By offering

AI control software held the largest share in 2025, as it forms the core algorithm and agent layer that delivers real-time monitoring, predictive control and automated adjustment. Professional services are expected to grow fastest, as facility-specific mapping, digital modeling and integration work scales with adoption. This designation reflects analyst judgment in the report. Monitoring and analytics complete the offering view.

By deployment

The supervisory layer on existing SCADA led in 2025. It connects on top of existing building management systems rather than replacing them, lowering the barrier to adoption for operators with large installed infrastructure. Embedded controls are projected to grow fastest as new-build AI data centers specify AI-native control from the outset.

By application

Liquid cooling optimization is the largest application, reflecting liquid cooling's rising share of new AI capacity and its need for responsive, high-precision control. Direct-to-chip and other liquid architectures in high-density facilities are expected to keep demand for specialized optimization strong. Thermal risk and capacity management is projected to grow fastest, as operators prefer predictive approaches that prevent heat-related incidents before they occur. Air cooling optimization is also covered.

By end user

Hyperscale data center cooling deployments led in 2025 given early adoption, high investment capacity and the outsized share of AI capacity under construction. Colocation cooling optimization is projected to grow fastest as vendors extend beyond hyperscalers into shared-infrastructure facilities serving a broad range of tenants. Enterprise facilities form the third segment.

Regional Outlook

North America: the largest region

North America held an estimated 48% share in 2025, reflecting the concentration of hyperscale AI data center construction in the United States and the early commercial presence of leading AI cooling vendors. Canada is expanding capacity and adopting advanced cooling for high-density computing, while Mexico is an emerging market. The report notes this regional allocation is an analyst-modeled estimate.

Asia-Pacific: the fastest-growing region

Asia-Pacific, estimated at 22% share in 2025, is projected to grow fastest. China is expanding domestic AI computing infrastructure at scale, India is increasing capacity for cloud and AI workloads, Japan continues to invest in advanced facilities, and South Korea is scaling AI computing supported by its semiconductor ecosystem. The report advises treating this outlook as directional given limited category-specific regional data.

Europe and other regions

Europe (Germany, the UK, France, Italy), Latin America (Brazil, Chile) and the Middle East & Africa (Saudi Arabia, U.A.E.) are also covered in the full report.

Opportunities for Vendors and Operators

• Facility-wide orchestration: Vendors that become the unified control layer across cooling, electrical distribution, battery dispatch and workload scheduling can capture outsized value.
• Liquid-cooling-specific AI control: Liquid cooling needs more responsive, higher-precision control than air, creating room for specialized capability.
• International expansion: Regions with higher electricity prices generate proportionally larger returns from AI-driven cooling energy savings.
• Policy tailwinds: Programs like COOLERCHIPS 1.5 and the EU's 2026 framework open doors for optimized cooling solutions across markets.

Competitive Landscape: Who Leads the Market?

The market is fragmented, led by a small group of specialist AI control vendors alongside AI capabilities embedded in larger industrial and technology companies' offerings.

• Phaidra, a Seattle-based company founded by alumni of Alphabet's DeepMind, builds AI agents that coordinate cooling, power and workload management as a unified system. Its liquid-cooling agent has been deployed and tested in production alongside a major cloud provider's liquid-cooled fleet.
• Vigilent specializes in data center cooling optimization software and services.
• EkkoSense provides AI-driven operational visibility, cooling capacity optimization and thermal and power risk mitigation.
• Schneider Electric, Vertiv, Siemens, Johnson Controls, nVent Electric, NVIDIA, Hewlett Packard Enterprise, Eaton, Rittal, Carrier Global, Trane Technologies and Submer Technologies strengthen the landscape through AI-enabled controls, liquid-cooling systems and intelligent infrastructure.

Competition centers on production-validated energy savings and thermal risk reduction, because operators trust autonomous control only when results are credible and disclosed.

Recent Developments

• July 2025: Activate Capital invested in XNRGY Climate Systems to expand U.S. manufacturing of air handling and liquid-cooling solutions.
• January 2026: Motivair by Schneider Electric launched the MCDU-70, a 2.5 MW coolant distribution unit scalable to 10 MW+.
• March 2026: Eaton completed its acquisition of Boyd Thermal to enable integrated grid-to-chip cooling.
• April 2026: Vertiv acquired Strategic Thermal Labs to strengthen cold-plate design, server-side cooling and thermal validation.

Conclusion

The AI data center cooling optimization market is moving from niche to necessity. With USD 1.44 billion in 2025 growing to USD 12.38 billion by 2034, the opportunity is shaped by rising rack density, proven energy savings, liquid cooling adoption and supportive regulation. Operators that adopt predictive, facility-wide AI control early can cut energy costs and reduce thermal risk, while vendors that prove results in production will win trust and share.

Frequently Asked Questions (FAQs)

1. What is the AI data center cooling optimization market? It covers software and AI agent-based systems that sit above conventional building management or SCADA systems and dynamically control liquid and air cooling infrastructure, distinct from the cooling hardware market.

2. What is the size of the market? The market was valued at an estimated USD 1.44 billion in 2025 and is projected to reach USD 12.38 billion by 2034.

3. What is the CAGR of the market? It is expected to grow at a CAGR of 27.0% from 2026 to 2034.

4. What is driving market growth? Explosive AI workload growth, rising rack power density and proven energy savings are the key drivers, supported by government initiatives.

5. How much of data center energy use does cooling represent? Cooling represents roughly 30% of total data center energy consumption, second only to compute.

6. What energy savings has AI cooling optimization demonstrated? Google has demonstrated up to 40% cooling-energy savings, and recent studies show 9.4% to 27.8% savings through predictive optimization.

7. Which region dominates the market? North America leads with an estimated 48% share in 2025, while Asia-Pacific is the fastest-growing region.

8. Who are the leading vendors? Key players include Phaidra, Vigilent, EkkoSense, Schneider Electric, Vertiv, Siemens, Johnson Controls, NVIDIA, Eaton and Carrier Global.

Get the Full Report

Want the full breakdown of the AI data center cooling optimization market? The IG Transformation Partners report spans 110-120 pages and 25-35 market tables covering offerings, deployment models, applications, end users, regional forecasts and competitor profiles.

or Request the full breakdown today and plan your cooling strategy with confidence.