AI Data Center Industry worth $933.76 billion by 2030

The report "AI Data Center Industry by Offering (Compute Server (GPU-Based, FPGA-Based, ASIC-based), Storage, Cooling, Power, DCIM), Data Center Type (Hyperscale, Colocation), Application (GenAI, Machine Learning, NLP, Computer Vision) - Global Forecast to 2030" The global AI data center market is projected to grow from USD 236.44 billion in 2025 to USD 933.76 billion by 2030, at a CAGR of 31.6% during the forecast period. The AI data center industry is booming as more companies are adopting AI to make better business choices and take over boring jobs - and they need strong, adaptable data centers to do this on a big scale. There are roadblocks like high prices and fears about keeping data safe and private, but the market keeps growing. These centers aim to do heavy number-crunching without harming the planet too much.

Even with problems like power use and supply chain disruptions, smart money and advanced technologies provide a boost to the market. Big players like Microsoft (US), Google (US), Amazon Web Services (US), and NVIDIA (US) are all cooking up data center fixes that play nice with AI, setting themselves up to win down the road.

By data center type, the colocation data center segment is expected to record the highest CAGR during the forecast period.

Colocation data centers are expected to have the highest CAGR in the AI data center market during the forecast period as they offer scalable, cost-effective, AI-ready infrastructure without the capital-intensive burden of building and maintaining in-house facilities. As enterprises and startups increasingly build applications powered by AI technologies, many cannot access the computing power and other infrastructure necessary to develop and run their applications in data centers optimized for artificial intelligence. That is where colocation providers can help make the difference, providing shared physical infrastructure with high-density computing, advanced cooling, and access to dedicated AI hardware such as general-purpose and specialized hardware like GPUs, TPUs, and more.

One of the attractive features of colocation models is the flexibility they provide to scale resources, a key capability for handling the ebb and flow of AI workloads. Colocation centers add connectivity, security compliance, and geographical reach, which is ideal for edge-AI applications as well as any tasks or applications needing low latency. This is crucial, especially as sectors such as finance, health, and manufacturing prioritize how fast data is processed and where the data goes.

By deployment, the cloud segment is projected to account for the largest market share in the AI data center market during the forecast period.

The cloud segment is projected to have the largest market size in the AI data center market during the forecast period due to its scalability, availability, and lower cost of deployment. As more organizations fuel growth with AI, many are turning to cloud-based platforms to run their large-scale AI workloads without making substantial system investments or breaking their budget. Allowing users to access computing resources in the form of GPU, TPU, and AI-specific framework on-demand, as well as cloud deployment, is perfect for both AI model training and inference. This agility enables enterprises to accelerate innovation, lower costs, and scale AI applications worldwide with low latency.

The main cloud providers, such as AWS (US), Microsoft Azure (US), and Google Cloud (US), have leaped ahead, and AI-relevant infrastructure is out of the box and running in no time. These platforms include an array of AI capabilities and services like AWS SageMaker, Azure AI, and Google Vertex AI for organizations to create, train, and deploy machine learning models at scale. For example, Google Cloud's focus on AI-optimized data centers and custom AI chips (TPUs) set them apart as the top cloud provider for AI workloads. In addition, AI-as-a-Service (AIaaS) growth is driving cloud adoption as organizations increasingly favor subscription-based access to state-of-the-art AI functionality. Thus, cloud deployment is still the major selection for contemporary enterprising AI organizations.

By region, Asia Pacific is projected to dominate the market during the forecast period.

Asia Pacific is expected to hold the highest market share in the AI data center market by 2030, inspired by rapid digitalization, strong government support, and large-scale AI adoption in major industries. Major economies like China, Japan, India, and South Korea are investing heavily in AI infrastructure to increase competition, innovation and support digital changes. China, especially, leads the charge with its national AI development strategy and important funds in AI-operated smart cities, autonomous systems, and monitoring infrastructure, which requires advanced AI data centers.

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The growing demand for hyperscale and cloud data centers in the region, which is inspired by adopting a growing internet economy and growing A-A-Service (AIAS), also contributes to the dominance of the market. Prominent players such as Alibaba Cloud (China), Tencent Cloud (China), Huawei Claude (China), and Navar Claude (South Korea) are aggressively expanding the footprints of their A-Taiyar Data Centers in the Asia Pacific. For example, in 2023, Alibaba Claude announced a plan to open several new AI-unlike data centers in Southeast Asia and the Middle East. Additionally, India's data center area is experiencing strong growth supported by initiatives such as the Digital India program and data localization rules. With the development of large-scale data volumes, favorable policy structures, and ongoing infrastructure with a large population, Asia Pacific will be well-positioned to lead the AI data center market by 2030.

Key Players

Key companies operating in the AI Data Center market include Dell Inc. (US), Hewlett Packard Enterprise Development LP (US), Lenovo (US), Huawei Technologies Co., Ltd (China), IBM (US), Super Micro Computer, Inc. (US), IEIT SYSTEMS CO., LTD. (China), among others.