AI-Powered Storage Market

Market Definition

The market refers to solutions that integrate artificial intelligence to manage, optimize, and automate data storage systems. These systems analyze usage patterns, predict storage needs, and enhance data accessibility across hybrid, edge, and cloud environments.

The market covers storage hardware, intelligent software, and integrated platforms. Applications span data centers, autonomous systems, healthcare analytics, and financial services. The report provides a comprehensive analysis of key drivers, emerging trends, and the competitive landscape expected to influence the market over the forecast period.

AI-Powered Storage Market Overview

The global AI-powered storage market size was valued at USD 18.03 billion in 2024 and is projected to grow from USD 24.49 billion in 2025 to USD 217.82 billion by 2032, exhibiting a CAGR of 36.64% during the forecast period.

The growth of the market is supported by the expansion of hyperscale data centers and the proliferation of edge computing infrastructure. This is increasing the demand for intelligent, high-speed storage systems that can handle massive volumes of data in real time. AI-integrated storage solutions meet this need by enabling faster data processing, model training, and analytics across distributed computing networks.

Major companies operating in the AI-powered storage industry are Intel Corporation, NVIDIA, IBM, SAMSUNG, Pure Storage, Inc., NetApp, Inc., Micron Technology, Inc., Cisco Systems, Inc., Toshiba Corporation, Hitachi, Ltd., Dell Inc., Hewlett Packard Enterprise Development LP, Huawei Technologies Co., Ltd., Quantum Corporation, and Veritas Technologies LLC.

The growth of the market is driven by the widespread use of artificial intelligence in sectors like healthcare, finance, and retail. Workloads such as medical imaging analysis, algorithmic trading, and customer behavior prediction generate vast volumes of unstructured data that require intelligent storage systems for efficient processing. 

Enterprises are adopting AI-powered storage to improve data handling, speed up insights, and reduce latency. This shift toward AI-optimized infrastructure is significantly contributing to the expansion of the market globally.

• In July 2024, the National Institute of Advanced Industrial Science and Technology (AIST) in Japan announced the launch of ABCI 3.0, the latest version of its AI infrastructure. ABCI 3.0 features computing servers equipped with 6,128 NVIDIA H200 GPUs and an all-flash storage system, achieving peak performance of 6.22 exaflops in half precision. This upgrade is expected to more than double both storage capacity and theoretical read/write performance compared to its predecessor, ABCI 2.0, and aims to accelerate research and development in AI technologies including generative AI.

Key Highlights:

1. The AI-powered storage market size was valued at USD 18.03 billion in 2024.
2. The market is projected to grow at a CAGR of 36.64% from 2025 to 2032.
3. North America held a market share of 36.05% in 2024, with a valuation of USD 6.50 billion.
4. The direct attached storage (DAS) segment garnered USD 6.33 billion in revenue in 2024.
5. The file based segment is expected to reach USD 124.18 billion by 2032.
6. The hard disk drive (HDD) segment secured the largest revenue share of 52.66% in 2024.
7. The enterprises segment is poised for a robust CAGR of 36.38% through the forecast period.
8. Asia Pacific is anticipated to grow at a CAGR of 37.63% during the forecast period.

Market Driver

Expansion of Hyperscale Data Centers

Hyperscale data centers are increasingly relying on intelligent storage systems to support their high-performance computing needs. The growth of the market is being driven by the integration of advanced storage that enables faster data access and reduces downtime.

AI is being used to automate storage management, optimize resources, and predict potential failures. This ensures greater efficiency in operations, encouraging further investment in AI-powered storage by cloud providers and enterprise data centers.

• In October 2024, Equinix, in partnership with Singapore's GIC and the Canada Pension Plan Investment Board, unveiled a USD 15 billion joint venture to expand U.S. hyperscale data centers. This initiative aims to add over 1.5 gigawatts of new capacity, addressing the growing demand for AI-driven computing power.

Market Challenge

Managing Data Complexity and Scalability

A major challenge in the growth of the AI-powered storage market is managing data complexity and scalability across diverse AI workloads. As AI applications generate massive and varied datasets, maintaining consistent performance, storage efficiency, and data accessibility becomes increasingly difficult.

To address this, key players are adopting scalable storage architectures with AI-driven data management features. They are using tiered storage, NVMe-based systems, and intelligent data classification tools to improve processing speed and reduce latency. In addition, software-defined storage solutions are implemented to allow dynamic scaling and support seamless integration with evolving AI infrastructure.

Market Trend

Proliferation of Edge Computing Infrastructure

Edge computing is reshaping data storage requirements by pushing processing closer to the data source. This shift influences the expansion of  the market  by creating demand for systems that can intelligently manage and analyze data at the edge.

AI-powered storage platforms help in local decision-making, reducing the need for constant data transmission to centralized servers. This is critical for industries with rapidly expanding edge deployment such as manufacturing, smart cities, and telecom.

• In February 2024, Intel unveiled its new Edge Platform, a modular, open software solution designed to simplify the development, deployment, and management of edge AI applications at the Mobile World Congress. This platform enables enterprises to process data where it is generated, reducing latency and enhancing operational efficiency. It supports various use cases, including defect detection in manufacturing, inventory management in retail, and traffic management in smart cities.

AI-Powered Storage Market Report Snapshot

Market Segmentation

• By Storage System (Direct Attached Storage (DAS), Network Attached Storage (NAS), and Storage Area Network (SAN)): The direct attached storage (DAS) segment earned USD 6.33 billion in 2024 due to its low-latency performance, cost-efficiency, and ability to deliver high-speed data access essential for AI model training and inferencing at the device level.
• By Storage Architecture (File Based and Object Based): The file based segment held 59.48% of the market in 2024, due to its ability to efficiently handle unstructured data at scale, which is critical for AI model training and high-throughput analytics.
• By Storage Medium (Hard Disk Drive (HDD), and Solid State Drive (SSD)): The hard disk drive (HDD) segment is projected to reach USD 102.83 billion by 2032, owing to its cost-effective scalability and ability to store large volumes of archival and training data required for AI workloads.
• By End User (Enterprises, Telecom Companies, Cloud Service Providers (CSPs), and Government Bodies): The enterprises segment is poised for significant growth at a CAGR of 36.38% through the forecast period, attributed to its cost-effective scalability for handling large volumes of data generated by AI workloads across enterprise environments.

AI-Powered Storage Market Regional Analysis

Based on region, the global market has been classified into North America, Europe, Asia Pacific, Middle East & Africa, and South America.

The North America AI-powered storage market accounted for approximately 36.05% of the global share in 2024, with a valuation of USD 6.50 billion. The region is home to a high concentration of AI-centric enterprises and global technology leaders that consistently advance data storage capabilities.

Companies such as NVIDIA, Google, IBM, and Amazon are deploying large-scale AI models that demand high-performance, intelligent storage infrastructure. Their ongoing investments in AI research and infrastructure development are playing a key role in driving the growth of the market across North America. 

Moreover, organizations in North America are among the earliest adopters of AI-integrated IT infrastructure. Enterprises and research institutions are modernizing their data centers with intelligent storage to support AI training, big data analytics, and machine learning operations. This shift toward AI-optimized systems is significantly contributing to the growth of the market in the region.

The AI-powered storage industry in Asia Pacific is poised for significant growth at a robust CAGR of 37.63% over the forecast period. Telecom operators in Asia Pacific are heavily investing in AI to support 5G deployment and smart network operations.

These applications require fast, scalable, and intelligent storage to manage real-time data traffic and analytics. The demand for AI-powered storage is rising as telecom providers seek infrastructure that supports low-latency processing and autonomous network management, thereby fueling market growth.

Furthermore, Asia Pacific leads in some of the most advanced manufacturing hubs, where factories are integrating AI and robotics. These environments generate continuous streams of sensor data, video feeds, and machine logs.

AI-powered storage systems are being deployed to process, analyze, and store this data efficiently, enabling predictive maintenance and process optimization, thereby contributing to the market expansion in the region.

• In April 2024, Huawei introduced a series of F5G-A (Fifth Generation Fixed Network-Advanced) products and solutions at the Global Optical Summit in Bangkok, focused on advancing digital transformation across industries in the Asia Pacific region. These offerings are designed to support intelligent operations in sectors such as electric power and transportation. The solutions include high-speed home broadband tailored to various usage environments, next-generation enterprise campus networks, and all-optical communication infrastructure to meet the growing data demands of industrial and public service applications.

Regulatory Frameworks

• In the U.S., regulations like the Health Insurance Portability and Accountability Act (HIPAA) and the Federal Information Security Modernization Act (FISMA) impose strict data protection standards. The National Institute of Standards and Technology (NIST) provides AI Risk Management Frameworks, guiding organizations in managing AI-related risks. Additionally, the Federal Trade Commission (FTC) enforces actions against deceptive AI practices, impacting AI-powered storage solutions.
• The European Union is finalizing the Artificial Intelligence Act, set to become law by 2025. This regulation classifies AI applications by risk levels, imposing strict requirements on high-risk systems, including those in data storage. It mandates transparency, data governance, and human oversight, with penalties for non-compliance reaching up to USD 37.80 million or 7% of global turnover.
• Japan is progressing towards formal AI regulation. The proposed Basic Law for the Promotion of Responsible AI aims to establish guidelines for AI development and use, including aspects like transparency and accountability.
• India's Digital Personal Data Protection Bill (DPDPB) is poised to regulate data processing activities, including AI-powered storage. The bill emphasizes data localization and user consent. Additionally, the government has issued advisories for platforms to label AI-generated content and ensure transparency in AI operations.

Competitive Landscape

Market players in the AI-powered storage industry are adopting strategic approaches to meet the evolving demands. Companies are designing storage systems that support end-to-end AI workflows, such as data collection, model training, and inference on a larger scale.

These innovations are being developed to manage high volumes of unstructured data while maintaining speed and accuracy. The focus is on building scalable, intelligent storage platforms tailored for AI processing.

• In October 2023, Huawei Technologies Co. Ltd. introduced the OceanStor A310, an AI storage model designed for large-scale AI applications. This system offers scalable storage capacity for training and deploying AI models across diverse industries. The OceanStor A310 supports comprehensive data management for AI processes such as data collection, preprocessing, training, and inference, leveraging big data and high-performance computing capabilities.

List of Key Companies in AI-Powered Storage Market:

• Intel Corporation
• NVIDIA
• IBM
• SAMSUNG
• Pure Storage, Inc.
• NetApp, Inc.
• Micron Technology, Inc.
• Cisco Systems, Inc.
• Toshiba Corporation
• Hitachi, Ltd.
• Dell Inc.
• Hewlett Packard Enterprise Development LP
• Huawei Technologies Co., Ltd.
• Quantum Corporation
• Veritas Technologies LLC

Recent Developments (Product Launches)

• In March 2025, NVIDIA launched NVIDIA Dynamo, an open-source inference serving framework designed for high-throughput and low-latency deployment of generative AI and reasoning models in large-scale distributed environments. Compatible with open-source tools like PyTorch, SGLang, NVIDIA TensorRT-LLM, and vLLM, NVIDIA Dynamo is part of the growing ecosystem of inference tools that enable AI developers and researchers to accelerate their work.
• In January 2025, Quantum announced scalability enhancements to its Myriad all-flash file system, including incremental in-place scaling and dynamic data leveling. These features aim to provide high scalability and performance for AI and data-intensive workloads.
• In November 2024, IBM unveiled its next-generation Storage DS8000 system, designed to support AI and hybrid cloud workloads. The system offers enhanced performance, scalability, and resilience to meet the demands of modern enterprises leveraging AI technologies.
• In May 2024, NetApp introduced the AFF A-Series systems, eliminating storage silos and complexity. These systems provide intelligent and secure storage solutions, optimized for AI workloads, and are designed to accelerate and optimize every workload across hybrid cloud environments.