LLM 및 생성형 AI용 에너지 최적화 소프트웨어 시장 : 점유율 분석, 업계 동향과 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the LLM and generative AI energy optimization software market size is expected to increase from USD 1.28 billion in 2025 to USD 1.58 billion in 2026 and reach USD 5.07 billion by 2031, growing at a CAGR of 26.26% over 2026-2031.

This report is Segmented by Solution Type (AI Energy Analytics and Observability, and More), Deployment Mode (Cloud-Based, Hybrid, and On-Premise), End User (Hyperscale Cloud and AI Infrastructure Providers, and More), Optimization Objective (Energy and Carbon Optimization, Cost Optimization, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global LLM and Generative AI Energy Optimization Software Market Trends and Insights

Rapid Growth of AI-Heavy Workloads In Data Centers

The LLM and generative AI energy optimization software market is being driven by the rapid expansion of training and inference workloads in modern data centers. A single NVIDIA H100 GPU node rated at 10.2 kW thermal design power drew only 76% of that level during transformer model training in measured tests, underscoring why planning based solely on nameplate ratings can leave operators with a distorted view of actual power behavior. At the cluster scale, that mismatch creates both planning errors and stranded capacity, because facilities still need enough thermal and electrical headroom to cover real-time workload swings. The LLM and generative AI energy optimization software market benefits from this shift because legacy monitoring tools were built for steadier enterprise loads and cannot manage rapid variation with the same precision. The International Energy Agency projected that electricity use from AI-focused data centers would triple by 2030 relative to 2025, underscoring the multi-year need for software that matches compute intensity with available power and cooling resources. As a result, the LLM and generative AI energy optimization software market is increasingly tied to whether operators can keep new GPU capacity active instead of leaving equipment underused while infrastructure catches up.

Rising Electricity Cost Exposure for AI Infrastructure Operators

The LLM and generative AI energy optimization software market is also benefiting from the fact that electricity exposure has moved closer to the center of infrastructure strategy. The International Energy Agency reported that data center electricity demand surged 17% in 2025 alone, while AI-focused facilities grew even faster, which raised the cost base that operators must manage in real time. The same update stated that capital expenditure by 5 large technology companies exceeded USD 400 billion in 2025 and is set to rise another 75% in 2026, which means the LLM and generative AI energy optimization software market now sits closer to revenue protection than simple utility savings. Hammerhead AI stated that each additional megawatt of stranded power recovered through orchestration can be worth USD 20 million to USD 50 million in constrained infrastructure markets, which changes the value logic behind optimization spending. That framing gives the LLM and generative AI energy optimization software market a stronger role in board-level investment decisions, because recovered power can support more productive compute without waiting for a new grid connection. It also explains why buyers are more willing to fund real-time control software when delayed action can leave expensive AI capacity underutilized.

High Integration Complexity Across DCIM, BMS, And IT Stack

The LLM and generative AI energy optimization software market still faces a significant restraint: the difficulty of integrating facility systems, IT operations, and workload controls into a single usable loop. Many buyers need bidirectional connections across asset data, power telemetry, and orchestration layers before they can safely automate any response, which slows deployment and lengthens proof-of-value cycles. Vendors are responding with broader operational platforms, and Nlyte positioned its Operational AI offering around data center, colocation, hybrid cloud, and edge visibility from a single interface, which shows where the market is trying to remove friction. Even so, the LLM and generative AI energy optimization software market still encounters problems when data refresh rates, control permissions, and vendor interfaces do not align across systems. Research on future data center operations also pointed to operator caution about granting more authority to AI systems, reflecting concern about acting on incomplete or inconsistent telemetry. Until integration becomes easier, the LLM and generative AI energy optimization software market will continue to see slower adoption in legacy enterprise estates and complex colocation environments.

Other drivers and restraints analyzed in the detailed report include:

1. Regulation-Driven Need for Auditable Energy And Carbon Optimization
2. Shift From Rule-Based DCIM to Agentic AI Orchestration
3. Cybersecurity And Control-Plane Risk In Autonomous Optimization

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

AI Energy Analytics and Observability held 29.85% of the LLM- and generative-AI energy-optimization software market in 2025, making it the largest solution type, as most operators start with visibility before automating any intervention. The category remains the base layer of the LLM and generative AI energy-optimization software market, since workload orchestration and thermal control are difficult to trust without circuit-level, near-real-time telemetry. Verdigris stated that its sensing platform helped one Fortune 500 operator recover more than 1 MW of stranded capacity across more than 60 facilities, while T-Mobile identified degradation in 4% of its UPS rectifier fleet 21 days before failure without standard alarms. Those examples show why buyers first spend on measurement: the operational case becomes stronger once hidden capacity and early failure risk become visible. In practice, the largest slice of the LLM and generative AI energy optimization software market still starts with data quality, electrical intelligence, and continuous observability.

Sustainability Intelligence and Reporting is expected to expand at a 27.34% CAGR through 2031, which makes it the fastest-growing solution type in the LLM and generative AI energy optimization software market. Its growth is tied to mandatory disclosure and audit needs, especially where data center performance must be reported in standardized formats under European rules. The LLM and generative AI energy optimization software market is also seeing stronger interest in orchestration, thermal optimization, and digital twin tools as operators move beyond first deployments and begin asking how to raise compute output per watt. NVIDIA and Jacobs announced work around AI factory digital twins to simulate facility equipment efficiency, thermal performance, and throughput before physical deployment, which supports this shift toward planning-led optimization. Across the LLM and generative AI energy optimization software market, the common direction is toward integrated suites that link observability, scheduling, thermal response, and reporting rather than keeping each function in a separate tool.

Cloud-Based solutions held 66.41% of the LLM and generative AI energy optimization software market in 2025, reflecting the ease of rolling out software across distributed estates through centralized delivery models. That position remains strong because many operators still want continuous updates, broad remote visibility, and lower deployment friction across multiple facilities. Even so, the LLM and generative AI energy optimization software market is moving toward hybrid setups where local control and cloud analytics can coexist. This is especially important where inference latency is sensitive or where facility control signals should not depend entirely on public cloud endpoints. In those environments, the LLM and generative AI energy optimization software market is being shaped by architecture choices as much as by algorithm quality.

Hybrid deployments are projected to grow at a 26.92% CAGR through 2031, which makes them the fastest-growing mode in the LLM and generative AI energy optimization software market. The main reason is that operators do not want to choose between local control over cooling and power systems and broader analytics that span sites and workloads. Nlyte positioned its Operational AI platform around data center, colocation, hybrid cloud, and edge operations, which reflects how vendors are adapting their products to this demand pattern. On-premises models also remain relevant in the LLM and generative AI energy optimization software market for regulated sectors and sovereign AI programs where data residency, network isolation, and direct control of optimization logic remain essential. As a result, deployment demand in the LLM and generative AI energy optimization software market is broadening from SaaS convenience toward a more mixed architecture that mirrors how AI infrastructure is actually being built and governed.

Complete Report Scope:

• By Solution Type
  • AI Energy Analytics and Observability
  • AI Workload Orchestration and Scheduling
  • Thermal and Infrastructure Optimization
  • Digital Twin and Simulation Platforms
  • Sustainability Intelligence and Reporting
• By Deployment Mode
  • Cloud Based
  • Hybrid
  • On Premises
• By End User
  • Hyperscale Cloud and AI Infrastructure Providers
  • Colocation Data Center Operators
  • Enterprise Data Centers
  • Sovereign and Government AI Infrastructure Operators
• By Optimization Objective
  • Energy And Carbon Optimization
  • Performance Optimization
  • Cost Optimization
  • Reliability And Availability Optimization
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Russia
    • Rest of Europe
  • Asia-Pacific
    • China
    • India
    • Japan
    • South Korea
    • Australia
    • Rest of Asia-Pacific
  • Middle East and Africa
    • Middle East
      • Saudi Arabia
      • United Arab Emirates
      • Turkey
      • Rest of Middle East
    • Africa
      • South Africa
      • Egypt
      • Rest of Africa

Geography Analysis

North America held 34.56% of the LLM and generative AI energy optimization software market share in 2025, making it the largest regional market. The region maintained this lead thanks to its concentration of hyperscale and cloud AI infrastructure, alongside early large-scale demand for integrated energy, cooling, and compute management. The White House issued an executive order in July 2025 to accelerate federal permitting for data center infrastructure and energy transmission, supporting continued expansion of AI campuses that require optimization systems from the outset. Canada added a public-sector demand layer when the federal government sought proposals for sovereign AI data centers above 100 MW in early 2026. Together, these factors kept North America at the center of the market in 2025 and sustained new software demand into 2026.

Asia-Pacific is projected to grow at a 27.45% CAGR through 2031, making it the fastest-growing geography in the market. Japan strengthened its efficiency efforts through METI policy updates and collaborative programs for software-defined liquid-cooling facilities. China introduced a formal evaluation framework with T/CCSA 619-2025, setting AI-based methods for data center energy-saving evaluation. South Korea's advanced national AI programs, with Lablup and Upstage passing Phase 1 evaluation under the sovereign AI foundation model project. These developments give Asia-Pacific a mix of regulatory pressure, infrastructure buildout, and national AI investment, driving adoption.

Europe remains strategically important because it combines capacity expansion with structured efficiency and reporting requirements. Germany approved a national data center strategy in March 2026, aiming to double total capacity and quadruple AI compute capacity by 2030, while tying new assets to strict efficiency and renewable power expectations. The wider EU framework also supports adoption by imposing standardized reporting requirements for larger data centers, making software-based measurement and disclosure unavoidable. Meanwhile, the Middle East, Africa, and South America remain earlier-stage opportunities, with adoption likely to follow sovereign AI buildouts, new capacity programs, and rising interest in sustainability-linked infrastructure procurement.

1. Phaidra Inc.
2. Sunbird Software, Inc.
3. EkkoSense Limited
4. Verdigris Technologies, Inc.
5. GridPoint, Inc.
6. Lancium Technologies
7. Gridmatic
8. WattTime
9. C3.ai, Inc.
10. Modius, Inc.
11. Virtual Power Systems, Inc.
12. FieldView Solutions, Inc.
13. Equilibrium Energy, Inc.
14. Tyba Energy, Inc.
15. Verse, Inc.
16. J4 Energy AG
17. Hammerhead AI, Inc.
18. Lablup, Inc.
19. Enverus, Inc.
20. Nlyte Software, Inc.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope Of The Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Rapid Growth Of AI-Heavy Workloads In Data Centers
  • 4.2.2 Rising Electricity Cost Exposure For AI Infrastructure Operators
  • 4.2.3 Regulation-Driven Need For Auditable Energy And Carbon Optimization
  • 4.2.4 Shift From Rule-Based DCIM To Agentic AI Orchestration
  • 4.2.5 Hidden Power And Cooling Stranding In GPU-Dense Facilities
  • 4.2.6 Demand For Real-Time Workload Placement Across Compute And Energy Constraints
• 4.3 Market Restraints
  • 4.3.1 High Integration Complexity Across DCIM, BMS, And IT Stack
  • 4.3.2 Cybersecurity And Control-Plane Risk In Autonomous Optimization
  • 4.3.3 Data Fragmentation Limits Model Accuracy And ROI Visibility
  • 4.3.4 Long Procurement Cycles In Mission-Critical Infrastructure Environments
• 4.4 Impact Of Macroeconomic Factors On The Market
• 4.5 Industry Value-Chain Analysis
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Bargaining Power Of Buyers
  • 4.8.2 Bargaining Power Of Suppliers
  • 4.8.3 Threat Of New Entrants
  • 4.8.4 Threat Of Substitutes
  • 4.8.5 Intensity Of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Solution Type
  • 5.1.1 AI Energy Analytics and Observability
  • 5.1.2 AI Workload Orchestration and Scheduling
  • 5.1.3 Thermal and Infrastructure Optimization
  • 5.1.4 Digital Twin and Simulation Platforms
  • 5.1.5 Sustainability Intelligence and Reporting
• 5.2 By Deployment Mode
  • 5.2.1 Cloud Based
  • 5.2.2 Hybrid
  • 5.2.3 On Premises
• 5.3 By End User
  • 5.3.1 Hyperscale Cloud and AI Infrastructure Providers
  • 5.3.2 Colocation Data Center Operators
  • 5.3.3 Enterprise Data Centers
  • 5.3.4 Sovereign and Government AI Infrastructure Operators
• 5.4 By Optimization Objective
  • 5.4.1 Energy And Carbon Optimization
  • 5.4.2 Performance Optimization
  • 5.4.3 Cost Optimization
  • 5.4.4 Reliability And Availability Optimization
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 South America
    • 5.5.2.1 Brazil
    • 5.5.2.2 Argentina
    • 5.5.2.3 Rest of South America
  • 5.5.3 Europe
    • 5.5.3.1 Germany
    • 5.5.3.2 United Kingdom
    • 5.5.3.3 France
    • 5.5.3.4 Italy
    • 5.5.3.5 Spain
    • 5.5.3.6 Russia
    • 5.5.3.7 Rest of Europe
  • 5.5.4 Asia-Pacific
    • 5.5.4.1 China
    • 5.5.4.2 India
    • 5.5.4.3 Japan
    • 5.5.4.4 South Korea
    • 5.5.4.5 Australia
    • 5.5.4.6 Rest of Asia-Pacific
  • 5.5.5 Middle East and Africa
    • 5.5.5.1 Middle East
      • 5.5.5.1.1 Saudi Arabia
      • 5.5.5.1.2 United Arab Emirates
      • 5.5.5.1.3 Turkey
      • 5.5.5.1.4 Rest of Middle East
    • 5.5.5.2 Africa
      • 5.5.5.2.1 South Africa
      • 5.5.5.2.2 Egypt
      • 5.5.5.2.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 Phaidra Inc.
  • 6.4.2 Sunbird Software, Inc.
  • 6.4.3 EkkoSense Limited
  • 6.4.4 Verdigris Technologies, Inc.
  • 6.4.5 GridPoint, Inc.
  • 6.4.6 Lancium Technologies
  • 6.4.7 Gridmatic
  • 6.4.8 WattTime
  • 6.4.9 C3.ai, Inc.
  • 6.4.10 Modius, Inc.
  • 6.4.11 Virtual Power Systems, Inc.
  • 6.4.12 FieldView Solutions, Inc.
  • 6.4.13 Equilibrium Energy, Inc.
  • 6.4.14 Tyba Energy, Inc.
  • 6.4.15 Verse, Inc.
  • 6.4.16 J4 Energy AG
  • 6.4.17 Hammerhead AI, Inc.
  • 6.4.18 Lablup, Inc.
  • 6.4.19 Enverus, Inc.
  • 6.4.20 Nlyte Software, Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space And Unmet-Need Assessment