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市场调查报告书
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1725056

2032 年人工智慧储存市场预测：按产品、解决方案类型、储存系统、储存架构、部署模型、储存媒体、最终用户和地区进行的全球分析

AI Powered Storage Market Forecasts to 2032 - Global Analysis By Offering, Solution Type, Storage System, Storage Architecture, Deployment Model, Storage Medium, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的预测，全球人工智慧储存市场规模预计在 2025 年达到 347.1 亿美元，到 2032 年将达到 1,495.1 亿美元，预测期内的复合年增长率为 23.2%。使用人工智慧来改善管理、效能和效率的系统称为人工智慧储存。这些智慧型系统使用机器学习演算法和预测分析来自动化产能预测、异常检测、资料分层和效能调整等流程。支援人工智慧 (AI) 的储存不断评估使用趋势和应用需求，以提高资料安全性、减少延迟并动态分配资源。这意味着一种响应更快、更经济的储存解决方案，特别是对于处理大量非结构化资料的企业而言。此外，随着数位转型的加速，人工智慧储存对于寻求有效扩展并做出更明智的数据主导决策的企业来说变得至关重要。

根据国际数据公司 (IDC) 的数据，受人工智慧、物联网和边缘运算日益普及的推动，全球数据领域预计到 2025 年将成长到 175 Zetta位元组。这种成长需要先进的储存解决方案，包括人工智慧存储，以有效地管理和分析数据。

对高速资料存取的需求

快速资料存取对于人工智慧和机器学习应用中的高频分析、即时推理和模型训练至关重要。由于 IOPS（每秒输入/输出操作）有限且延迟问题，传统储存基础设施可能会成为瓶颈。人工智慧储存解决方案利用 NVMe、全快闪阵列和智慧快取演算法等技术，实现超低延迟和高吞吐量。此外，它还确保数据顺利传输到AI引擎，从而显着加快工作负载的执行速度，并提高金融建模、医疗诊断和无人驾驶汽车等行业的整体应用效能。

营运和实施成本高

人工智慧储存系统需要在专门的软体、硬体和训练有素的人员方面进行大量的前期投资。儘管效能很高，但储存层级记忆体、NVMe 和全快闪阵列等技术比传统的基于 HDD 的系统更昂贵。将AI演算法整合到储存工作流程中需要GPU、高效能CPU等先进的运算资源，进一步增加了成本。这些成本对于许多小型企业来说过高，限制了其采用。此外，由于需要频繁更新、模型训练和维护以确保储存系统以最佳状态运行，因此营运成本也会上升。

企业对智慧数据管理的需求日益增长

随着各行各业数位转型不断加速，企业正以前所未有的规模产生和收集数据。人们对不仅能储存数据，还能帮助管理、分析和提取数据见解的系统的需求日益增长。人工智慧储存提供智慧分层、预测分析、自动资料分类和自我修復机制等功能，以提高业务连续性并实现更智慧的决策。此外，随着越来越多的企业意识到资料的战略价值，对能够优化资料效用的智慧储存解决方案的需求将不断增加。

技术快速淘汰

人工智慧和储存技术的快速进步可能很快就会使现有的解决方案过时。目前由人工智慧驱动的储存系统可能很快就会被新的架构、储存通讯协定或人工智慧硬体（如量子运算和神经型态晶片）的进步所取代。此外，如果更新、更有效的替代方案迅速出现，那么在当今解决方案上投入大量资金的公司就有可能落后。这种不断变化的环境导致潜在采用者产生犹豫，尤其是那些关心系统寿命和长期投资回报率的人。

COVID-19的影响：

COVID-19 疫情对人工智慧储存市场产生了多种影响。一方面，由于供应链中断和 IT 投资推迟，早期疫情发展和硬体可用性暂时放缓。但这场危机也加速了许多行业的数位转型，企业迅速转向云端服务、人工智慧主导的分析和远端操作，以处理不断增长的数据量并确保业务永续营运连续性。此外，不断增长的资料量凸显了对能够支援人工智慧工作负载的高度扩充性、智慧储存系统的需求。因此，人工智慧储存解决方案正受到越来越多的关注和资金筹措，尤其是来自医疗保健、电子商务和金融等行业。

预计人工智慧整合软体领域将成为预测期内最大的市场

预计预测期内，AI整合软体部分将占据最大的市场占有率。该领域的目标是透过将人工智慧融入储存系统来提高资料管理和营运效率。透过实现智慧数据搜寻、即时分析、预测性维护和自动数据分类，人工智慧整合可以提高储存效率并降低营运费用。此外，它还能实现储存系统与其他业务应用程式的平滑集成，从而实现更智慧、灵活的基础架构。对人工智慧增强技术的需求不断增长，以及对更智慧、更有效率的储存系统的需求正在推动各行各业这一领域的成长。

预计在预测期内，储存区域网路(SAN) 部分将以最高的复合年增长率成长。

预计储存区域网路（SAN）部分将在预测期内实现最高的成长率。 SAN 系统提供高速、低延迟的资料访问，使其非常适合灾难復原和巨量资料分析等企业级应用。它的灵活性和扩充性使企业能够增加储存容量，而不会中断正在进行的业务。由于其强大的效能和改进的资料安全性，SAN 解决方案在医疗保健和 BFSI 等领域尤其受到青睐。此外，SAN 在人工智慧储存市场的快速崛起是其广泛使用的结果，反映了其管理繁重工作负载和复杂资料环境的能力。

比最大的地区

预计预测期内欧洲地区将占据最大的市场占有率。随着製造业、医疗保健和金融等各个领域的人工智慧技术应用日益广泛，该地区正在经历快速扩张。法国、德国和英国等国家正在透过投资云端基础的储存和人工智慧基础设施树立标准。此外，欧盟 (EU) 对数位转型的重视以及《一般资料保护规范 (GDPR)》等法律体制的建立也增加了对安全有效的人工智慧储存解决方案的需求。此外，欧洲在人工智慧储存市场的份额受到数据分析日益增长的需求以及云端运算和人工智慧的发展的推动。

复合年增长率最高的地区：

预计亚太地区在预测期内的复合年增长率最高。机器人技术在许多行业的广泛应用、对云端基础的服务的需求不断增长以及对即时数据处理的要求不断增加是这种快速扩张的主要因素。而且，此次扩张的重点国家是中国、日本和印度。印度尤其在人工智慧基础设施方面投入了大量资金，亚马逊和微软等科技巨头在云端服务和资料中心建设方面投资了数十亿美元，使该国成为亚太地区人工智慧储存市场的主要参与者。

提供免费客製化：

订阅此报告的客户可享有以下免费自订选项之一：

  • 公司简介
    • 全面分析其他市场参与者（最多 3 家公司）
    • 主要企业的SWOT分析（最多3家公司）
  • 地理细分
    • 根据客户兴趣对主要国家市场进行估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 研究范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 主要研究资料
    • 次级研究资讯来源
    • 先决条件

第三章市场走势分析

  • 驱动程式
  • 限制因素
  • 机会
  • 威胁
  • 最终用户分析
  • 新兴市场
  • COVID-19的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

第五章 全球人工智慧储存市场（依产品分类）

  • 硬体
  • 软体

第六章 全球人工智慧储存市场（按解决方案类型）

  • 人工智慧整合软体
  • 资料保护软体
  • 储存管理软体

第七章 全球人工智慧储存市场（按储存系统）

  • 直接连接储存 (DAS)
  • 网路附加储存 (NAS)
  • 储存区域网路(SAN)

第八章 全球人工智慧储存市场：储存架构

  • 檔案式的存储
  • 物件储存
  • 区块储存

第九章 全球人工智慧储存市场（按部署模式）

  • 混合云端
  • 私有云端
  • 公共云端
  • 边缘运算
  • 本地

第 10 章 全球 AI 储存市场（按储存媒体）

  • 硬碟机（HDD）
  • 固态硬碟 (SSD)

第 11 章 全球 AI 储存市场（依最终用户划分）

  • BFSI
  • 公司
  • 政府
  • 云端服务供应商(CSP)
  • 电信业者
  • 其他最终用户

第十二章 全球人工智慧储存市场（按地区）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十三章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十四章 公司概况

  • Alphabet（Google Inc.）
  • Dell Technologies Inc.
  • Huawei Technologies Co., Ltd.
  • Cisco Systems, Inc.
  • Intel Corporation
  • Flextronics International Ltd.
  • Toshiba Corporation
  • Lenovo Group Limited
  • Amazon Web Services
  • Hewlett Packard Enterprise Company（HPE）
  • Fujitsu Limited
  • NVIDIA Corporation
  • IBM Corporation
  • Hitachi Ltd.
  • Samsung Electronics Co. Ltd.
Product Code: SMRC29362

According to Stratistics MRC, the Global AI Powered Storage Market is accounted for $34.71 billion in 2025 and is expected to reach $149.51 billion by 2032 growing at a CAGR of 23.2% during the forecast period. Systems that use artificial intelligence to improve management, performance, and efficiency are referred to as AI-powered storage. These smart systems automate processes like capacity forecasting, anomaly detection, data tiering, and performance tuning through the use of machine learning algorithms and predictive analytics. Artificial intelligence (AI)-powered storage can improve data security, lower latency, and dynamically allocate resources by continuously evaluating usage trends and application demands. This leads to storage solutions that are more responsive and economical, particularly for businesses handling substantial amounts of unstructured data. Moreover, AI-powered storage is becoming indispensable for companies looking to scale effectively and make more informed, data-driven decisions as digital transformation picks up speed.

According to the International Data Corporation (IDC), the global datasphere is expected to grow to 175 zettabytes by 2025, driven by the increasing adoption of AI, IoT, and edge computing. This growth necessitates advanced storage solutions, including AI-powered storage, to manage and analyze data efficiently.

Market Dynamics:

Driver:

Demand for fast data access

Rapid data access is essential for high-frequency analytics, real-time inference, and model training in AI and machine learning applications. Because of limited IOPS (input/output operations per second) and latency problems, traditional storage infrastructure can lead to bottlenecks. AI-powered storage solutions provide ultra-low latency and high throughput by utilizing technologies such as NVMe, all-flash arrays, and intelligent caching algorithms. Additionally, this guarantees smooth data transfer to AI engines, greatly speeding up workload execution and enhancing overall application performance in industries like financial modeling, healthcare diagnostics, and driverless cars.

Restraint:

High operational and implementation costs

AI-powered storage systems demand a large initial outlay of funds for specialized software, hardware, and trained staff. Despite their high performance, technologies like storage-class memory, NVMe, and all-flash arrays are pricier than conventional HDD-based systems. Costs are further increased by the requirement for sophisticated computing resources like GPUs and high-performance CPUs to integrate AI algorithms into storage workflows. These costs can be prohibitive for many small and mid-sized businesses, which restricts adoption. Furthermore, the requirement for frequent updates, model training, and maintenance to guarantee the storage system operates at its best raises operational costs as well.

Opportunity:

Growing enterprise need for intelligent data management

The speed at which digital transformation is occurring across industries is causing organizations to generate and collect data at a never-before-seen scale. Demand is rising for systems that help manage, analyze, and extract insights from data in addition to storing it. AI-powered storage provides features like intelligent tiering, predictive analytics, automated data classification, and self-healing mechanisms that enhance business continuity and allow for more intelligent decision-making. Moreover, intelligent storage solutions that can optimize data utility will become more and more in demand as more businesses realize the strategic value of data.

Threat:

Quick obsolescence of technology

Existing solutions may soon become outdated due to the rapid advancements in AI and storage technologies. Current AI-powered storage systems might be swiftly surpassed by new architectures, storage protocols, or advances in AI hardware (like quantum computing or neuromorphic chips). Additionally, businesses that make significant investments in today's solutions run the risk of lagging behind if more effective, newer alternatives appear soon after. Potential adopters are hesitant because of this changing environment, particularly those who are worried about system longevity and long-term ROI.

Covid-19 Impact:

The COVID-19 pandemic affected the market for AI-powered storage in different ways. On the one hand, early pandemic deployments and hardware availability were momentarily slowed down by supply chain interruptions and postponed IT investments. But the crisis also sped up digital transformation in many industries, with businesses quickly turning to cloud services, AI-driven analytics, and remote operations to handle growing data volumes and ensure business continuity. Additionally, this increase in data production brought attention to the need for scalable, intelligent storage systems that could handle AI workloads. As a result, there is now more interest in and funding for AI-powered storage solutions, particularly in industries like healthcare, e-commerce, and finance.

The AI integration software segment is expected to be the largest during the forecast period

The AI integration software segment is expected to account for the largest market share during the forecast period. The goal of this section is to improve data management and operational efficiency by incorporating artificial intelligence into storage systems. By enabling intelligent data retrieval, real-time analytics, predictive maintenance, and automated data classification, AI integration enhances storage efficiency and lowers operating expenses. Additionally, it enables the smooth integration of storage systems with other business applications, resulting in a more intelligent and flexible infrastructure. The growing demand for AI-enhanced technologies and the need for more intelligent, efficient storage systems across industries are driving the segment's growth.

The storage area network (SAN) segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the storage area network (SAN) segment is predicted to witness the highest growth rate. SAN systems are appropriate for enterprise-grade applications like disaster recovery and big data analytics because they offer fast, low-latency data access. Because of their flexibility and scalability, companies can increase storage capacity without interfering with ongoing operations. Because of their strong performance and improved data security, SAN solutions are especially preferred by sectors like healthcare and BFSI. Furthermore, SAN's quick rise in the market for AI-powered storage is a result of its widespread use, which reflects its ability to manage demanding workloads and intricate data environments.

Region with largest share:

During the forecast period, the Europe region is expected to hold the largest market share. The region is expanding rapidly as a result of the growing use of AI technologies in a variety of sectors, including manufacturing, healthcare, and finance. With their investments in cloud-based storage and AI infrastructure, nations like France, Germany, and the UK are setting the standard. The need for safe and effective AI-powered storage solutions has also increased as a result of the European Union's emphasis on digital transformation and its legislative frameworks, such as the General Data Protection Regulation (GDPR). Moreover, Europe's share of the AI-powered storage market is being driven by the growing demand for data analytics as well as developments in cloud computing and artificial intelligence.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. The broad use of robotics in many industries, the growing demand for cloud-based services, and the growing requirement for real-time data processing are the main drivers of this quick expansion. Additionally, the leading nations in this expansion are China, Japan, and India. India, in particular, is seeing large investments in AI infrastructure, with tech behemoths like Amazon and Microsoft investing billions to construct cloud services and data centers, making the nation a major player in the APAC region's AI-powered storage market.

Key players in the market

Some of the key players in AI Powered Storage Market include Alphabet (Google Inc.), Dell Technologies Inc., Huawei Technologies Co., Ltd., Cisco Systems, Inc., Intel Corporation, Flextronics International Ltd., Toshiba Corporation, Lenovo Group Limited, Amazon Web Services, Hewlett Packard Enterprise Company (HPE), Fujitsu Limited, NVIDIA Corporation, IBM Corporation, Hitachi Ltd. and Samsung Electronics Co. Ltd.

Key Developments:

In March 2025, Google's parent company, Alphabet Inc., has agreed to acquire Israeli-founded cybersecurity startup Wiz for at least $32 billion, marking the largest acquisition in the tech giant's history. The deal, announced Tuesday morning, underscores Google's intensified efforts to bolster its cloud security capabilities and compete with Microsoft and Amazon in the highly competitive enterprise cloud market.

In November 2024, Cisco and MGM Resorts International announce that the companies have signed a Whole Portfolio Agreement (WPA), empowering MGM Resorts with the majority of Cisco's software portfolio. This includes cyber security, software defined networking, software defined-WAN, digital experience assurance, full-stack observability, data center and services. This agreement spans 5.5 years, benefiting guests and employees across all of MGM Resorts' properties.

In July 2023, Dell Technologies announced it has signed a definitive agreement to acquire Moogsoft, an AI-driven provider of intelligent monitoring solutions that support DevOps and ITOps. This transaction will further enhance Dell's AIOps capabilities, as part of its longstanding approach of embedding AI functionality within its product portfolio and as a critical component of its "multicloud by design" strategy.

Offerings Covered:

  • Hardware
  • Software

Solution Types Covered:

  • AI Integration Software
  • Data Protection Software
  • Storage Management Software

Storage Systems Covered:

  • Direct-attached Storage (DAS)
  • Network-attached Storage (NAS)
  • Storage Area Network (SAN)

Storage Architectures Covered:

  • File-Based Storage
  • Object Storage
  • Block Storage

Deployment Models Covered:

  • Hybrid Cloud
  • Private Cloud
  • Public Cloud
  • Edge Computing
  • On-Premises

Storage Mediums Covered:

  • Hard Disk Drive (HDD)
  • Solid State Drive (SSD)

End Users Covered:

  • BFSI
  • Enterprises
  • Government Bodies
  • Cloud Service Providers (CSP)
  • Telecom Companies
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 End User Analysis
  • 3.7 Emerging Markets
  • 3.8 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI Powered Storage Market, By Offering

  • 5.1 Introduction
  • 5.2 Hardware
  • 5.3 Software

6 Global AI Powered Storage Market, By Solution Type

  • 6.1 Introduction
  • 6.2 AI Integration Software
  • 6.3 Data Protection Software
  • 6.4 Storage Management Software

7 Global AI Powered Storage Market, By Storage System

  • 7.1 Introduction
  • 7.2 Direct-attached Storage (DAS)
  • 7.3 Network-attached Storage (NAS)
  • 7.4 Storage Area Network (SAN)

8 Global AI Powered Storage Market, By Storage Architecture

  • 8.1 Introduction
  • 8.2 File-Based Storage
  • 8.3 Object Storage
  • 8.4 Block Storage

9 Global AI Powered Storage Market, By Deployment Model

  • 9.1 Introduction
  • 9.2 Hybrid Cloud
  • 9.3 Private Cloud
  • 9.4 Public Cloud
  • 9.5 Edge Computing
  • 9.6 On-Premises

10 Global AI Powered Storage Market, By Storage Medium

  • 10.1 Introduction
  • 10.2 Hard Disk Drive (HDD)
  • 10.3 Solid State Drive (SSD)

11 Global AI Powered Storage Market, By End User

  • 11.1 Introduction
  • 11.2 BFSI
  • 11.3 Enterprises
  • 11.4 Government Bodies
  • 11.5 Cloud Service Providers (CSP)
  • 11.6 Telecom Companies
  • 11.7 Other End Users

12 Global AI Powered Storage Market, By Geography

  • 12.1 Introduction
  • 12.2 North America
    • 12.2.1 US
    • 12.2.2 Canada
    • 12.2.3 Mexico
  • 12.3 Europe
    • 12.3.1 Germany
    • 12.3.2 UK
    • 12.3.3 Italy
    • 12.3.4 France
    • 12.3.5 Spain
    • 12.3.6 Rest of Europe
  • 12.4 Asia Pacific
    • 12.4.1 Japan
    • 12.4.2 China
    • 12.4.3 India
    • 12.4.4 Australia
    • 12.4.5 New Zealand
    • 12.4.6 South Korea
    • 12.4.7 Rest of Asia Pacific
  • 12.5 South America
    • 12.5.1 Argentina
    • 12.5.2 Brazil
    • 12.5.3 Chile
    • 12.5.4 Rest of South America
  • 12.6 Middle East & Africa
    • 12.6.1 Saudi Arabia
    • 12.6.2 UAE
    • 12.6.3 Qatar
    • 12.6.4 South Africa
    • 12.6.5 Rest of Middle East & Africa

13 Key Developments

  • 13.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 13.2 Acquisitions & Mergers
  • 13.3 New Product Launch
  • 13.4 Expansions
  • 13.5 Other Key Strategies

14 Company Profiling

  • 14.1 Alphabet (Google Inc.)
  • 14.2 Dell Technologies Inc.
  • 14.3 Huawei Technologies Co., Ltd.
  • 14.4 Cisco Systems, Inc.
  • 14.5 Intel Corporation
  • 14.6 Flextronics International Ltd.
  • 14.7 Toshiba Corporation
  • 14.8 Lenovo Group Limited
  • 14.9 Amazon Web Services
  • 14.10 Hewlett Packard Enterprise Company (HPE)
  • 14.11 Fujitsu Limited
  • 14.12 NVIDIA Corporation
  • 14.13 IBM Corporation
  • 14.14 Hitachi Ltd.
  • 14.15 Samsung Electronics Co. Ltd.

List of Tables

  • Table 1 Global AI Powered Storage Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI Powered Storage Market Outlook, By Offering (2024-2032) ($MN)
  • Table 3 Global AI Powered Storage Market Outlook, By Hardware (2024-2032) ($MN)
  • Table 4 Global AI Powered Storage Market Outlook, By Software (2024-2032) ($MN)
  • Table 5 Global AI Powered Storage Market Outlook, By Solution Type (2024-2032) ($MN)
  • Table 6 Global AI Powered Storage Market Outlook, By AI Integration Software (2024-2032) ($MN)
  • Table 7 Global AI Powered Storage Market Outlook, By Data Protection Software (2024-2032) ($MN)
  • Table 8 Global AI Powered Storage Market Outlook, By Storage Management Software (2024-2032) ($MN)
  • Table 9 Global AI Powered Storage Market Outlook, By Storage System (2024-2032) ($MN)
  • Table 10 Global AI Powered Storage Market Outlook, By Direct-attached Storage (DAS) (2024-2032) ($MN)
  • Table 11 Global AI Powered Storage Market Outlook, By Network-attached Storage (NAS) (2024-2032) ($MN)
  • Table 12 Global AI Powered Storage Market Outlook, By Storage Area Network (SAN) (2024-2032) ($MN)
  • Table 13 Global AI Powered Storage Market Outlook, By Storage Architecture (2024-2032) ($MN)
  • Table 14 Global AI Powered Storage Market Outlook, By File-Based Storage (2024-2032) ($MN)
  • Table 15 Global AI Powered Storage Market Outlook, By Object Storage (2024-2032) ($MN)
  • Table 16 Global AI Powered Storage Market Outlook, By Block Storage (2024-2032) ($MN)
  • Table 17 Global AI Powered Storage Market Outlook, By Deployment Model (2024-2032) ($MN)
  • Table 18 Global AI Powered Storage Market Outlook, By Hybrid Cloud (2024-2032) ($MN)
  • Table 19 Global AI Powered Storage Market Outlook, By Private Cloud (2024-2032) ($MN)
  • Table 20 Global AI Powered Storage Market Outlook, By Public Cloud (2024-2032) ($MN)
  • Table 21 Global AI Powered Storage Market Outlook, By Edge Computing (2024-2032) ($MN)
  • Table 22 Global AI Powered Storage Market Outlook, By On-Premises (2024-2032) ($MN)
  • Table 23 Global AI Powered Storage Market Outlook, By Storage Medium (2024-2032) ($MN)
  • Table 24 Global AI Powered Storage Market Outlook, By Hard Disk Drive (HDD) (2024-2032) ($MN)
  • Table 25 Global AI Powered Storage Market Outlook, By Solid State Drive (SSD) (2024-2032) ($MN)
  • Table 26 Global AI Powered Storage Market Outlook, By End User (2024-2032) ($MN)
  • Table 27 Global AI Powered Storage Market Outlook, By BFSI (2024-2032) ($MN)
  • Table 28 Global AI Powered Storage Market Outlook, By Enterprises (2024-2032) ($MN)
  • Table 29 Global AI Powered Storage Market Outlook, By Government Bodies (2024-2032) ($MN)
  • Table 30 Global AI Powered Storage Market Outlook, By Cloud Service Providers (CSP) (2024-2032) ($MN)
  • Table 31 Global AI Powered Storage Market Outlook, By Telecom Companies (2024-2032) ($MN)
  • Table 32 Global AI Powered Storage Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.