查看购物车
  • Traditional Chinese
  • English
  • Japanese
  • Korean
封面
市场调查报告书
商品编码
1470702

人工智慧驱动的储存市场：依产品类型、最终用户、地区 - 全球产业分析、规模、占有率、成长、趋势、预测，2024-2031 年

AI-Powered Storage Market by Product Type, End-Users, and Geography (North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa): Global Industry Analysis, Size, Share, Growth, Trends, and Forecast, 2024-2031
出版日期: | 出版商: Persistence Market Research | 英文 310 Pages | 商品交期: 2-5个工作天内

价格
简介目录

本研究报告是Persistence Market Research的市场研究报告，详细分析并预测了全球配备AI的储存市场。 这份综合报告提供了关键市场动态、市场成长驱动因素、课题和新兴趋势的宝贵见解。 它详细概述了资讯科技产业中人工智慧驱动的储存领域，并提供了独家数据和统计数据来预测 2024 年至 2031 年的市场成长轨迹。

关键见解

  • 人工智慧驱动的储存市场规模（2024 年）：233 亿美元
  • 预测市场规模（2031 年）：1,100 亿美元
  • 全球市场成长率（复合年增长率，2024-2031 年）：18.9%

人工智慧驱动的储存市场 - 报告范围：

人工智慧驱动的储存市场涵盖各种硬体、软体和服务，它们利用人工智慧 (AI) 和机器学习 (ML) 技术来优化储存基础设施、资料管理和分析功能。 该市场为企业、云端服务供应商、资料中心和託管服务供应商提供人工智慧驱动的储存解决方案，用于资料处理、分析和洞察生成。 人工智慧驱动的储存平台使企业能够提高资料可存取性、可扩充性和效能，同时降低储存成本、复杂性和管理开销。 市场成长的驱动力是资料量的增加、对即时分析的需求以及采用人工智慧驱动的自动化进行储存最佳化和预测性维护。

推动市场成长的因素：

全球人工智慧驱动的储存市场正受益于几个关键的成长动力。 数位转型计画、物联网设备和多媒体内容产生的数据呈指数级增长，推动了对能够处理不同数据类型和工作负载的人工智慧储存解决方案的需求。 此外，人工智慧演算法、深度学习模型和神经网路的进步支援预测分析、异常检测和资料分类，以实现储存优化和资料治理。 此外，向混合和多云环境、边缘运算和容器化储存架构的转变正在推动对人工智慧驱动的储存平台的需求，这些平台可以实现跨分散式IT 基础设施的无缝资料移动性、安全性和合规性。 此外，金融、医疗保健和零售等行业对即时洞察和决策能力的需求日益增长，从而加速了数据分析和商业智慧应用程式采用人工智慧驱动的储存。

市场限制因素：

儘管成长前景广阔，但人工智慧驱动的储存市场面临技术整合、资料隐私和人才短缺等课题。 将人工智慧驱动的储存解决方案与现有 IT 基础设施和遗留储存系统整合可能需要在硬体、软体和专业服务方面进行大量投资，这可能会影响采用率和投资回报率。 此外，对资料安全、隐私和监管合规性的担忧成为采用人工智慧驱动的储存平台的障碍，特别是在具有严格资料保护要求的高度监管行业。 此外，缺乏能够设计、实施和管理人工智慧驱动的储存解决方案的熟练人工智慧和数据科学专业人员也限制了市场的成长和创新。 解决这些障碍需要技术供应商、网路安全专家和监管机构共同努力，开发强大、可扩展且安全的人工智慧驱动的储存解决方案，以满足企业需求和监管标准。

市场机会：

人工智慧驱动的储存市场透过技术创新、产业联盟和垂直市场扩张提供了巨大的成长机会。 人工智慧驱动的资料管理、联合学习和边缘人工智慧处理等新兴趋势为储存供应商提供了新的途径，使其产品脱颖而出并满足不断变化的客户需求。 开发包含资料治理、加密和合规性功能的人工智慧驱动的储存解决方案可以帮助企业满足监管要求并降低资料密集型环境中的网路安全风险。 此外，与云端服务供应商、人工智慧软体供应商和行业特定解决方案提供商的策略合作伙伴关係加速了人工智慧驱动的储存平台的市场渗透和客户采用。 此外，对人工智慧人才开发、培训计划和认证计划的投资将推动人工智慧驱动的储存市场的创新和人才管道的成长。

本报告涵盖的主要问题

  • 推动全球人工智慧储存市场成长的因素有哪些？
  • 人工智慧演算法和机器学习技术的进步如何改变人工智慧驱动的储存解决方案的竞争格局？
  • 储存供应商和企业客户在人工智慧驱动的储存市场中面临哪些主要课题和机会？
  • 哪些产业和用例在采用人工智慧驱动的储存方面最具成长潜力？
  • 领先公司正在采取哪些策略来实现人工智慧驱动储存的差异化并赢得市场占有率？

竞争资讯与业务策略：

全球人工智慧驱动的储存市场的主要参与者，包括储存供应商、人工智慧软体供应商和云端服务供应商，正在专注于创新、合作伙伴关係和以客户为中心的策略，以推动市场成长并保持竞争力。 这些公司正在投资研发，开发人工智慧驱动的储存平台，该平台具有重复资料删除、压缩和分层等高级功能，以优化成本并提高效能。 此外，与人工智慧软体供应商、系统整合商和产业合作伙伴的策略联盟使企业能够将人工智慧驱动的分析、资料管理和自动化功能整合到其储存解决方案中，从而为客户提供附加价值。 此外，我们对客户参与、解决方案客製化和垂直市场专业化的关注提高了在人工智慧驱动的储存市场竞争格局中的品牌认知度和客户忠诚度。

公司主要简介

  • Intel Corporation
  • NVIDIA Corporation
  • IBM
  • Samsung Electronics
  • Pure Storage
  • NetApp
  • Micron Technology
  • CISCO
  • Toshiba
  • Hitachi
  • Lenovo
  • Dell
  • HPE

人工智慧驱动的储存市场研究细分：

透过提供

  • 软体
  • 硬体

依储存系统

  • 直接附加存储
  • 网路附加存储
  • 储存区域网络

依储存架构划分

  • 硬碟机 (HDD)
  • 固态硬碟 (SSD)。

最终使用者

  • 公司
  • 政府机构
  • 云端服务供应商
  • 电信公司

依地区

  • 北美
  • 欧洲
  • 亚太地区
  • 行

目录

第 1 章执行摘要

第二章市场概述

  • 市场范围/分类
  • 市场定义/范围/限制

第三章市场风险与趋势评估

  • 风险评估
    • 新冠肺炎 (COVID-19) 危机及其对人工智慧驱动的储存市场的影响
    • 与过去的危机相比，对新冠肺炎 (COVID-19) 的影响进行基准测试
    • 对市场价值的影响
    • 评估：依主要国家划分
    • 评估：依主要细分市场
    • 针对供应商的行动要点和建议
  • 影响市场的主要趋势
  • 配方和产品开发的趋势

第四章市场背景

  • 人工智慧储存市场：依主要国家划分
  • 人工智慧驱动的储存市场机会评估
  • 市场情境预测
  • 投资可行性分析
  • 预测因子 - 相关性和影响
  • 市场动态

第 5 章关键成功因素

  • 製造商专注于渗透率低但成长率高的市场
  • 押注该细分市场的高增量机会
  • 同业基准

第六章全球人工智慧储存市场需求分析（2019-2023）及预测（2024-2031）

  • 过去的市场分析，2019-2023 年
  • 2024-2031 年当前与未来市场预测
  • 年成长趋势分析

第七章全球人工智慧储存市场货币分析（2019-2023）与预测（2024-2031）

  • 过去的市场价值分析，2019-2023 年
  • 2024-2031 年当前与未来市场价值预测
    • 年成长趋势分析
    • 绝对数量机会分析

第 8 章全球人工智慧储存市场分析（2019-2023 年）与预测（2024-2031 年）：依产品划分

  • 简介/主要发现
  • 2019-2023 年历史市场价值与依产品分析
  • 2024-2031 年当前和未来的市场价值以及依产品分类的分析和预测
    • 硬体
    • 软体
  • 市场吸引力分析：依产品分类

第9章全球人工智慧储存市场分析（2019-2023）与预测（2024-2031）：依储存系统划分

  • 简介/主要发现
  • 过去的市场价值与分析：依储存系统划分，2019-2023 年
  • 当前和未来的市场价值、分析和预测：依储存系统划分，2024 年至 2031 年
    • 直接附加储存 (DAS)
    • 网路附加储存 (NAS)
    • 储存区域网路 (SAN)
  • 市场吸引力分析：依储存系统分类

第 10 章全球人工智慧驱动的储存市场分析（2019-2023 年）与预测（2024-2031 年）：依储存架构划分

  • 简介/主键调查结果
  • 历史市场价值与分析：依储存架构划分，2019-2023 年
  • 2024-2031 年储存架构当前与未来的市场价值、分析与预测
    • 基于檔案/基于物件的存储
    • 物件存储
  • 市场吸引力分析：依储存架构

第11章全球人工智慧储存市场分析（2019-2023）与预测（2024-2031）：依储存媒体划分

  • 简介/主要发现
  • 过去的市场价值与分析：依储存媒体划分，2019-2023 年
  • 当前和未来的市场价值、分析和预测：依储存媒体划分，2024-2031 年
    • 硬碟机 (HDD)
    • 固态硬碟 (SSD)
  • 市场吸引力分析：依储存媒体

第 12 章全球人工智慧储存市场分析（2019-2023 年）和预测（2024-2031 年）：依最终用户划分

  • 简介/主要发现
  • 历史市场价值与分析：依最终用户划分，2019-2023 年
  • 当前和未来的市场价值、分析和预测：依最终用户划分，2024 年至 2031 年
    • 公司
    • 政府机构
    • 云端服务供应商
    • 电信公司
  • 市场吸引力分析：依最终用户分类

第 13 章全球人工智慧储存市场分析（2019-2023 年）与预测（2024-2031 年）：依地区划分

  • 简介
  • 市场价值与分析：依地区划分，2019-2023 年
  • 目前市场规模、分析与预测：依地区划分，2024-2031 年
    • 北美
    • 亚太地区
    • 欧洲
    • 行
  • 市场吸引力分析：依地区划分

第14章北美人工智慧储存市场分析（2019-2023）与预测（2024-2031）

第十五章拉丁美洲人工智慧储存市场分析（2019-2023）与预测（2024-2031）

第16章欧洲人工智慧储存市场分析（2019-2023年）与预测（2024-2031年）

第十七章亚太地区人工智慧储存市场分析（2019-2023）与预测（2024-2031）

第十八章中东与非洲人工智慧储存市场分析（2019-2023）与预测（2024-2031）

第十九章主要国家人工智慧储存市场分析（2019-2023）及预测（2024-2031）

  • 简介
    • 市值比分析：依主要国家分类
    • 世界与各国的成长比较
  • 美国人工智慧储存市场分析
    • 价值比率分析：依市场分类
    • 价值、分析与预测：依市场分类，2024-2031 年
      • 依储存架构
      • 透过提供
      • 依储存系统
      • 依储存媒体
      • 依最终用户
  • 加拿大人工智慧储存市场分析
    • 价值比率分析：依市场分类
    • 价值、分析与预测：依市场分类，2024-2031 年
      • 依储存架构
      • 透过提供
      • 依储存系统
      • 依储存媒体
      • 依最终用户
  • 墨西哥人工智慧储存市场分析
    • 价值比率分析：依市场分类
    • 价值、分析与预测：依市场分类，2024-2031 年
      • 依储存架构
      • 透过提供
      • 依储存系统
      • 依储存媒体
      • 依最终用户
  • 巴西人工智慧储存市场分析
    • 价值比率分析：依市场分类
    • 价值、分析与预测：依市场分类，2024-2031 年
      • 依储存架构
      • 透过提供
      • 依储存系统
      • 依储存媒体
      • 依最终用户
  • 德国人工智慧储存市场分析
    • 价值比率分析：依市场分类
    • 价值、分析与预测：依市场分类，2024-2031 年
      • 依储存架构
      • 透过提供
      • 依储存系统
      • 依储存媒体
      • 依最终用户
  • 法国人工智慧储存市场分析
    • 价值比率分析：依市场分类
    • 价值、分析与预测：依市场分类，2024-2031 年
      • 依储存架构
      • 透过提供
      • 依储存系统
      • 依储存媒体
      • 依最终用户
  • 义大利人工智慧储存市场分析
    • 价值比率分析：依市场分类
    • 价值、分析与预测：依市场分类，2024-2031 年
      • 依储存架构
      • 透过提供
      • 依储存系统
      • 依储存媒体
      • 依最终用户
  • 俄罗斯人工智慧储存市场分析
    • 价值比率分析：依市场分类
    • 价值、分析与预测：依市场分类，2024-2031 年
      • 依储存架构
      • 透过提供
      • 依储存系统
      • 依储存媒体
      • 依最终用户
  • 英国人工智慧储存市场分析
    • 价值比率分析：依市场分类
    • 价值、分析与预测：依市场分类，2024-2031 年
      • 依储存架构
      • 透过提供
      • 依储存系统
      • 依储存媒体
      • 依最终用户
  • 中国人工智慧储存市场分析
    • 价值比率分析：依市场分类
    • 价值、分析与预测：依市场分类，2024-2031 年
      • 依储存架构
      • 透过提供
      • 依储存系统
      • 依储存媒体
      • 依最终用户
  • 日本人工智慧储存市场分析
    • 价值比率分析：依市场分类
    • 价值、分析与预测：依市场分类，2024-2031 年
      • 依储存架构
      • 透过提供
      • 依储存系统
      • 依储存媒体
      • 依最终用户
  • 韩国人工智慧储存市场分析
    • 价值比率分析：依市场分类
    • 价值、分析与预测：依市场分类，2024-2031 年
      • 依储存架构
      • 透过提供
      • 依储存系统
      • 依储存媒体
      • 依最终用户
  • 海湾合作委员会国家配备人工智慧的储存市场分析
    • 价值比率分析：依市场分类
    • 价值、分析与预测：依市场分类，2024-2031 年
      • 依储存架构
      • 透过提供
      • 依储存系统
      • 依储存媒体
      • 依最终用户
  • 南非人工智慧储存市场分析
    • 价值比率分析：依市场分类
    • 价值、分析与预测：依市场分类，2024-2031 年
      • 依储存架构
      • 透过提供
      • 依储存系统
      • 依储存媒体
      • 依最终用户
  • 土耳其人工智慧储存市场分析
    • 价值比率分析：依市场分类
    • 价值、分析与预测：依市场分类，2024-2031 年
      • 依储存架构
      • 透过提供
      • 依储存系统
      • 依储存媒体
      • 依最终用户
    • 国内竞争情势及企业集中度

第20章市场结构分析

  • 市场分析：依公司层级
  • 市场集中度
  • 主要公司市占率分析
  • 市场现况分析

第21章竞争分析

  • 竞争对手仪表板
  • 竞争基准
  • 衝突详情
    • Intel Corporation
    • NVIDIA Corporation
    • IBM
    • Samsung Electronics
    • Pure Storage
    • NetApp
    • Micron Technology
    • CISCO
    • Toshiba
    • Hitachi
    • Lenovo
    • Dell
    • HPE

第 22 章先决条件与使用的缩写

第23章研究方法

简介目录
Product Code: PMRREP33040

Persistence Market Research, a leading market research firm, has conducted an in-depth analysis of the global AI-Powered Storage Market. This comprehensive report provides valuable insights into key market dynamics, growth drivers, challenges, and emerging trends. It offers a detailed overview of the AI-powered storage segment within the information technology industry, presenting exclusive data and statistics projecting the market's growth trajectory from 2024 to 2031.

Key Insights:

  • AI-Powered Storage Market Size (2024): US$ 23.3 Billion
  • Projected Market Value (2031): US$ 110 Billion
  • Global Market Growth Rate (CAGR 2024 to 2031): 18.9%

AI-Powered Storage Market - Report Scope:

The AI-Powered Storage Market encompasses a diverse range of hardware, software, and services leveraging artificial intelligence (AI) and machine learning (ML) technologies to optimize storage infrastructure, data management, and analytics capabilities. This market serves enterprises, cloud service providers, data centers, and managed service providers, offering AI-driven storage solutions for data processing, analysis, and insights generation. AI-powered storage platforms enable organizations to improve data accessibility, scalability, and performance while reducing storage costs, complexity, and management overhead. Market growth is driven by increasing data volumes, demand for real-time analytics, and adoption of AI-driven automation for storage optimization and predictive maintenance.

Market Growth Drivers:

The global AI-Powered Storage Market benefits from several key growth drivers. The exponential growth of data generated by digital transformation initiatives, IoT devices, and multimedia content fuels demand for AI-powered storage solutions capable of handling diverse data types and workloads. Moreover, advancements in AI algorithms, deep learning models, and neural networks enable predictive analytics, anomaly detection, and data classification for storage optimization and data governance. Furthermore, the shift towards hybrid and multi-cloud environments, edge computing, and containerized storage architectures drives demand for AI-driven storage platforms that deliver seamless data mobility, security, and compliance across distributed IT infrastructure. Additionally, the growing need for real-time insights and decision-making capabilities in industries such as finance, healthcare, and retail accelerates adoption of AI-powered storage for data analytics and business intelligence applications.

Market Restraints:

Despite its promising growth prospects, the AI-Powered Storage Market faces challenges related to technology integration, data privacy, and talent shortage. Integrating AI-driven storage solutions with existing IT infrastructure and legacy storage systems may require significant investments in hardware, software, and professional services, impacting adoption rates and return on investment. Moreover, concerns about data security, privacy, and regulatory compliance pose barriers to deploying AI-powered storage platforms, particularly in highly regulated industries with strict data protection requirements. Additionally, the shortage of skilled AI and data science professionals capable of designing, implementing, and managing AI-powered storage solutions limits market growth and innovation. Addressing these barriers requires collaboration between technology vendors, cybersecurity experts, and regulatory authorities to develop robust, scalable, and secure AI-driven storage solutions that meet enterprise needs and regulatory standards.

Market Opportunities:

The AI-Powered Storage Market presents significant growth opportunities driven by technological innovations, industry partnerships, and vertical market expansion. Emerging trends such as AI-driven data management, federated learning, and edge AI processing offer new avenues for storage vendors to differentiate their offerings and address evolving customer needs. The development of AI-powered storage solutions with built-in data governance, encryption, and compliance features enables organizations to meet regulatory requirements and mitigate cybersecurity risks in data-intensive environments. Furthermore, strategic partnerships with cloud service providers, AI software vendors, and industry-specific solution providers facilitate market penetration and customer adoption of AI-driven storage platforms. Moreover, investment in AI talent development, training programs, and certification initiatives fosters innovation and talent pipeline growth in the AI-Powered Storage Market.

Key Questions Answered in the Report:

  • What factors are driving the growth of the AI-Powered Storage Market globally?
  • How are advancements in AI algorithms and machine learning technologies reshaping the competitive landscape of AI-driven storage solutions?
  • What are the key challenges and opportunities facing storage vendors and enterprise customers in the AI-Powered Storage Market?
  • Which industries and use cases offer the highest growth potential for AI-powered storage adoption?
  • What strategies are leading companies employing to differentiate their AI-driven storage offerings and gain market share?

Competitive Intelligence and Business Strategy:

Leading players in the global AI-Powered Storage Market, including storage vendors, AI software providers, and cloud service providers, focus on innovation, partnerships, and customer-centric strategies to drive market growth and maintain competitiveness. These companies invest in research and development to develop AI-driven storage platforms with advanced features such as data deduplication, compression, and tiering for cost optimization and performance enhancement. Moreover, strategic alliances with AI software vendors, system integrators, and industry partners enable companies to integrate AI-driven analytics, data management, and automation capabilities into their storage solutions, delivering added value to customers. Furthermore, emphasis on customer engagement, solution customization, and vertical market specialization enhances brand reputation and customer loyalty in the competitive landscape of AI-Powered Storage Market.

Key Companies Profiled:

  • Intel Corporation
  • NVIDIA Corporation
  • IBM
  • Samsung Electronics
  • Pure Storage
  • NetApp
  • Micron Technology
  • CISCO
  • Toshiba
  • Hitachi
  • Lenovo
  • Dell
  • HPE

AI-Powered Storage Market Research Segmentation:

By Offering

  • Software
  • Hardware

By Storage System

  • Direct-Attached Storage
  • Network-Attached Storage
  • Storage Area Network

By Storage Architecture

  • Hard Disk Drives (HDD)
  • Solid-State Drives (SSD).

By End User

  • Enterprises
  • Government Bodies
  • Cloud Service Providers
  • Telecom Companies

By Region

  • North America
  • Europe
  • Asia-Pacific
  • ROW

Table of Contents

1. Executive Summary

  • 1.1. Global Market Outlook
  • 1.2. Summary of Statistics
  • 1.3. Key Market Characteristics & Attributes
  • 1.4. Fact.MR Analysis and Recommendations

2. Market Overview

  • 2.1. Market Coverage / Taxonomy
  • 2.2. Market Definition / Scope / Limitations

3. Market Risks and Trends Assessment

  • 3.1. Risk Assessment
    • 3.1.1. COVID-19 Crisis and Impact on AI Powered Storage Market
    • 3.1.2. COVID-19 Impact Benchmark with Previous Crisis
    • 3.1.3. Impact on Market Value (US$ Mn)
    • 3.1.4. Assessment by Key Countries
    • 3.1.5. Assessment by Key Market Segments
    • 3.1.6. Action Points and Recommendation for Suppliers
  • 3.2. Key Trends Impacting the Market
  • 3.3. Formulation and Product Development Trends

4. Market Background

  • 4.1. AI Powered Storage Market, by Key Countries
  • 4.2. AI Powered Storage Market Opportunity Assessment (US$ Mn)
    • 4.2.1. Total Available Market
    • 4.2.2. Serviceable Addressable Market
    • 4.2.3. Serviceable Obtainable Market
  • 4.3. Market Scenario Forecast
    • 4.3.1. Demand in optimistic Scenario
    • 4.3.2. Demand in Likely Scenario
    • 4.3.3. Demand in Conservative Scenario
  • 4.4. Investment Feasibility Analysis
    • 4.4.1. Investment in Established Markets
      • 4.4.1.1. In Short Term
      • 4.4.1.2. In Long Term
    • 4.4.2. Investment in Emerging Markets
      • 4.4.2.1. In Short Term
      • 4.4.2.2. In Long Term
  • 4.5. Forecast Factors - Relevance & Impact
    • 4.5.1. Top Companies Historical Growth
    • 4.5.2. Growth in Automation, By Country
    • 4.5.3. AI Powered Storage Market Adoption Rate, By Country
  • 4.6. Market Dynamics
    • 4.6.1. Market Driving Factors and Impact Assessment
    • 4.6.2. Prominent Market Challenges and Impact Assessment
    • 4.6.3. AI Powered Storage Market Opportunities
    • 4.6.4. Prominent Trends in the Global Market & Their Impact Assessment

5. Key Success Factors

  • 5.1. Manufacturers' Focus on Low Penetration High Growth Markets
  • 5.2. Banking on with Segments High Incremental Opportunity
  • 5.3. Peer Benchmarking

6. Global AI Powered Storage Market Demand Analysis 2019-2023 and Forecast, 2024-2031

  • 6.1. Historical Market Analysis, 2019-2023
  • 6.2. Current and Future Market Projections, 2024-2031
  • 6.3. Y-o-Y Growth Trend Analysis

7. Global AI Powered Storage Market Value Analysis 2019-2023 and Forecast, 2024-2031

  • 7.1. Historical Market Value (US$ Mn) Analysis, 2019-2023
  • 7.2. Current and Future Market Value (US$ Mn) Projections, 2024-2031
    • 7.2.1. Y-o-Y Growth Trend Analysis
    • 7.2.2. Absolute $ Opportunity Analysis

8. Global AI Powered Storage Market Analysis 2019-2023 and Forecast 2024-2031, By Offering

  • 8.1. Introduction / Key Findings
  • 8.2. Historical Market Value (US$ Mn) and Analysis By Offering, 2019-2023
  • 8.3. Current and Future Market Value (US$ Mn) and Analysis and Forecast By Offering, 2024-2031
    • 8.3.1. Hardware
    • 8.3.2. Software
  • 8.4. Market Attractiveness Analysis By Offering

9. Global AI Powered Storage Market Analysis 2019-2023 and Forecast 2024-2031, By Storage System

  • 9.1. Introduction / Key Findings
  • 9.2. Historical Market Value (US$ Mn) and Analysis By Storage System, 2019-2023
  • 9.3. Current and Future Market Value (US$ Mn) and Analysis and Forecast By Storage System, 2024-2031
    • 9.3.1. Direct-attached Storage (DAS)
    • 9.3.2. Network-attached Storage (NAS)
    • 9.3.3. Storage Area Network (SAN)
  • 9.4. Market Attractiveness Analysis By Storage System

10. Global AI Powered Storage Market Analysis 2019-2023 and Forecast 2024-2031, By Storage Architecture

  • 10.1. Introduction / Key Findings
  • 10.2. Historical Market Value (US$ Mn) and Analysis By Storage Architecture, 2019-2023
  • 10.3. Current and Future Market Value (US$ Mn) and Analysis and Forecast By Storage Architecture, 2024-2031
    • 10.3.1. File- and Object-Based Storage
    • 10.3.2. Object Storage
  • 10.4. Market Attractiveness Analysis By Storage Architecture

11. Global AI Powered Storage Market Analysis 2019-2023 and Forecast 2024-2031, By Storage Medium

  • 11.1. Introduction / Key Findings
  • 11.2. Historical Market Value (US$ Mn) and Analysis By Storage Medium, 2019-2023
  • 11.3. Current and Future Market Value (US$ Mn) and Analysis and Forecast By Storage Medium, 2024-2031
    • 11.3.1. Hard Disk Drive (HDD)
    • 11.3.2. Solid State Drive (SSD)
  • 11.4. Market Attractiveness Analysis By Storage Medium

12. Global AI Powered Storage Market Analysis 2019-2023 and Forecast 2024-2031, By End-user

  • 12.1. Introduction / Key Findings
  • 12.2. Historical Market Value (US$ Mn) and Analysis By End-user, 2019-2023
  • 12.3. Current and Future Market Value (US$ Mn) and Analysis and Forecast By End-user, 2024-2031
    • 12.3.1. Enterprises
    • 12.3.2. Government Bodies
    • 12.3.3. Cloud Service Providers
    • 12.3.4. Telecom Companies
  • 12.4. Market Attractiveness Analysis By End-user

13. Global AI Powered Storage Market Analysis 2019-2023 and Forecast 2024-2031, By Region

  • 13.1. Introduction
  • 13.2. Historical Market Value (US$ Mn) and Analysis By Region, 2019-2023
  • 13.3. Current Market Size (US$ Mn) & Analysis and Forecast By Region, 2024-2031
    • 13.3.1. North America
    • 13.3.2. APAC
    • 13.3.3. Europe
    • 13.3.4. ROW
  • 13.4. Market Attractiveness Analysis By Region

14. North America AI Powered Storage Market Analysis 2019-2023 and Forecast 2024-2031

  • 14.1. Introduction
  • 14.2. Pricing Analysis
  • 14.3. Historical Market Value (US$ Mn) and Trend Analysis By Market Taxonomy, 2019-2023
  • 14.4. Market Value (US$ Mn) & Forecast By Market Taxonomy, 2024-2031
    • 14.4.1. By Country
      • 14.4.1.1. U.S.
      • 14.4.1.2. Canada
      • 14.4.1.3. Rest of North America
    • 14.4.2. By Storage Architecture
    • 14.4.3. By Offering
    • 14.4.4. By Storage System
    • 14.4.5. By Storage Medium
    • 14.4.6. By End-user
  • 14.5. Market Attractiveness Analysis
    • 14.5.1. By Country
    • 14.5.2. By Storage Architecture
    • 14.5.3. By Offering
    • 14.5.4. By Storage System
    • 14.5.5. By Storage Medium
    • 14.5.6. By End-user

15. Latin America AI Powered Storage Market Analysis 2019-2023 and Forecast 2024-2031

  • 15.1. Introduction
  • 15.2. Pricing Analysis
  • 15.3. Historical Market Value (US$ Mn) and Trend Analysis By Market Taxonomy, 2019-2023
  • 15.4. Market Value (US$ Mn) & Forecast By Market Taxonomy, 2024-2031
    • 15.4.1. By Country
      • 15.4.1.1. Brazil
      • 15.4.1.2. Mexico
      • 15.4.1.3. Rest of Latin America
    • 15.4.2. By Storage Architecture
    • 15.4.3. By Offering
    • 15.4.4. By Storage System
    • 15.4.5. By Storage Medium
    • 15.4.6. By End-user
  • 15.5. Market Attractiveness Analysis
    • 15.5.1. By Country
    • 15.5.2. By Storage Architecture
    • 15.5.3. By Offering
    • 15.5.4. By Storage System
    • 15.5.5. By Storage Medium
    • 15.5.6. By End-user

16. Europe AI Powered Storage Market Analysis 2019-2023 and Forecast 2024-2031

  • 16.1. Introduction
  • 16.2. Pricing Analysis
  • 16.3. Historical Market Value (US$ Mn) and Trend Analysis By Market Taxonomy, 2019-2023
  • 16.4. Market Value (US$ Mn) & Forecast By Market Taxonomy, 2024-2031
    • 16.4.1. By Country
      • 16.4.1.1. Germany
      • 16.4.1.2. France
      • 16.4.1.3. U.K.
      • 16.4.1.4. Italy
      • 16.4.1.5. Russia
      • 16.4.1.6. Rest of Europe
    • 16.4.2. By Storage Architecture
    • 16.4.3. By Offering
    • 16.4.4. By Storage System
    • 16.4.5. By Storage Medium
    • 16.4.6. By End-user
  • 16.5. Market Attractiveness Analysis
    • 16.5.1. By Country
    • 16.5.2. By Storage Architecture
    • 16.5.3. By Offering
    • 16.5.4. By Storage System
    • 16.5.5. By Storage Medium
    • 16.5.6. By End-user

17. Asia Pacific AI Powered Storage Market Analysis 2019-2023 and Forecast 2024-2031

  • 17.1. Introduction
  • 17.2. Pricing Analysis
  • 17.3. Historical Market Value (US$ Mn) and Trend Analysis By Market Taxonomy, 2019-2023
  • 17.4. Market Value (US$ Mn) & Forecast By Market Taxonomy, 2024-2031
    • 17.4.1. By Country
      • 17.4.1.1. China
      • 17.4.1.2. Japan
      • 17.4.1.3. South Korea
      • 17.4.1.4. Rest of Asia Pacific
    • 17.4.2. By Storage Architecture
    • 17.4.3. By Offering
    • 17.4.4. By Storage System
    • 17.4.5. By Storage Medium
    • 17.4.6. By End-user
  • 17.5. Market Attractiveness Analysis
    • 17.5.1. By Country
    • 17.5.2. By Storage Architecture
    • 17.5.3. By Offering
    • 17.5.4. By Storage System
    • 17.5.5. By Storage Medium
    • 17.5.6. By End-user

18. Middle East and Africa AI Powered Storage Market Analysis 2019-2023 and Forecast 2024-2031

  • 18.1. Introduction
  • 18.2. Pricing Analysis
  • 18.3. Historical Market Value (US$ Mn) and Trend Analysis By Market Taxonomy, 2019-2023
  • 18.4. Market Value (US$ Mn) & Forecast By Market Taxonomy, 2024-2031
    • 18.4.1. By Country
      • 18.4.1.1. GCC Countries
      • 18.4.1.2. South Africa
      • 18.4.1.3. Turkey
      • 18.4.1.4. Rest of Middle East and Africa
    • 18.4.2. By Storage Architecture
    • 18.4.3. By Offering
    • 18.4.4. By Storage System
    • 18.4.5. By Storage Medium
    • 18.4.6. By End-user
  • 18.5. Market Attractiveness Analysis
    • 18.5.1. By Country
    • 18.5.2. By Storage Architecture
    • 18.5.3. By Offering
    • 18.5.4. By Storage System
    • 18.5.5. By Storage Medium
    • 18.5.6. By End-user

19. Key Countries AI Powered Storage Market Analysis 2019-2023 and Forecast 2024-2031

  • 19.1. Introduction
    • 19.1.1. Market Value Proportion Analysis, By Key Countries
    • 19.1.2. Global Vs. Country Growth Comparison
  • 19.2. US AI Powered Storage Market Analysis
    • 19.2.1. Value Proportion Analysis by Market Taxonomy
    • 19.2.2. Value & Analysis and Forecast by Market Taxonomy, 2024-2031
      • 19.2.2.1. By Storage Architecture
      • 19.2.2.2. By Offering
      • 19.2.2.3. By Storage System
      • 19.2.2.4. By Storage Medium
      • 19.2.2.5. By End-user
  • 19.3. Canada AI Powered Storage Market Analysis
    • 19.3.1. Value Proportion Analysis by Market Taxonomy
    • 19.3.2. Value & Analysis and Forecast by Market Taxonomy, 2024-2031
      • 19.3.2.1. By Storage Architecture
      • 19.3.2.2. By Offering
      • 19.3.2.3. By Storage System
      • 19.3.2.4. By Storage Medium
      • 19.3.2.5. By End-user
  • 19.4. Mexico AI Powered Storage Market Analysis
    • 19.4.1. Value Proportion Analysis by Market Taxonomy
    • 19.4.2. Value & Analysis and Forecast by Market Taxonomy, 2024-2031
      • 19.4.2.1. By Storage Architecture
      • 19.4.2.2. By Offering
      • 19.4.2.3. By Storage System
      • 19.4.2.4. By Storage Medium
      • 19.4.2.5. By End-user
  • 19.5. Brazil AI Powered Storage Market Analysis
    • 19.5.1. Value Proportion Analysis by Market Taxonomy
    • 19.5.2. Value & Analysis and Forecast by Market Taxonomy, 2024-2031
      • 19.5.2.1. By Storage Architecture
      • 19.5.2.2. By Offering
      • 19.5.2.3. By Storage System
      • 19.5.2.4. By Storage Medium
      • 19.5.2.5. By End-user
  • 19.6. Germany AI Powered Storage Market Analysis
    • 19.6.1. Value Proportion Analysis by Market Taxonomy
    • 19.6.2. Value & Analysis and Forecast by Market Taxonomy, 2024-2031
      • 19.6.2.1. By Storage Architecture
      • 19.6.2.2. By Offering
      • 19.6.2.3. By Storage System
      • 19.6.2.4. By Storage Medium
      • 19.6.2.5. By End-user
  • 19.7. France AI Powered Storage Market Analysis
    • 19.7.1. Value Proportion Analysis by Market Taxonomy
    • 19.7.2. Value & Analysis and Forecast by Market Taxonomy, 2024-2031
      • 19.7.2.1. By Storage Architecture
      • 19.7.2.2. By Offering
      • 19.7.2.3. By Storage System
      • 19.7.2.4. By Storage Medium
      • 19.7.2.5. By End-user
  • 19.8. Italy AI Powered Storage Market Analysis
    • 19.8.1. Value Proportion Analysis by Market Taxonomy
    • 19.8.2. Value & Analysis and Forecast by Market Taxonomy, 2024-2031
      • 19.8.2.1. By Storage Architecture
      • 19.8.2.2. By Offering
      • 19.8.2.3. By Storage System
      • 19.8.2.4. By Storage Medium
      • 19.8.2.5. By End-user
  • 19.9. Russia AI Powered Storage Market Analysis
    • 19.9.1. Value Proportion Analysis by Market Taxonomy
    • 19.9.2. Value & Analysis and Forecast by Market Taxonomy, 2024-2031
      • 19.9.2.1. By Storage Architecture
      • 19.9.2.2. By Offering
      • 19.9.2.3. By Storage System
      • 19.9.2.4. By Storage Medium
      • 19.9.2.5. By End-user
  • 19.10. UK AI Powered Storage Market Analysis
    • 19.10.1. Value Proportion Analysis by Market Taxonomy
    • 19.10.2. Value & Analysis and Forecast by Market Taxonomy, 2024-2031
      • 19.10.2.1. By Storage Architecture
      • 19.10.2.2. By Offering
      • 19.10.2.3. By Storage System
      • 19.10.2.4. By Storage Medium
      • 19.10.2.5. By End-user
  • 19.11. China AI Powered Storage Market Analysis
    • 19.11.1. Value Proportion Analysis by Market Taxonomy
    • 19.11.2. Value & Analysis and Forecast by Market Taxonomy, 2024-2031
      • 19.11.2.1. By Storage Architecture
      • 19.11.2.2. By Offering
      • 19.11.2.3. By Storage System
      • 19.11.2.4. By Storage Medium
      • 19.11.2.5. By End-user
  • 19.12. Japan AI Powered Storage Market Analysis
    • 19.12.1. Value Proportion Analysis by Market Taxonomy
    • 19.12.2. Value & Analysis and Forecast by Market Taxonomy, 2024-2031
      • 19.12.2.1. By Storage Architecture
      • 19.12.2.2. By Offering
      • 19.12.2.3. By Storage System
      • 19.12.2.4. By Storage Medium
      • 19.12.2.5. By End-user
  • 19.13. South Korea AI Powered Storage Market Analysis
    • 19.13.1. Value Proportion Analysis by Market Taxonomy
    • 19.13.2. Value & Analysis and Forecast by Market Taxonomy, 2024-2031
      • 19.13.2.1. By Storage Architecture
      • 19.13.2.2. By Offering
      • 19.13.2.3. By Storage System
      • 19.13.2.4. By Storage Medium
      • 19.13.2.5. By End-user
  • 19.14. GCC Countries AI Powered Storage Market Analysis
    • 19.14.1. Value Proportion Analysis by Market Taxonomy
    • 19.14.2. Value & Analysis and Forecast by Market Taxonomy, 2024-2031
      • 19.14.2.1. By Storage Architecture
      • 19.14.2.2. By Offering
      • 19.14.2.3. By Storage System
      • 19.14.2.4. By Storage Medium
      • 19.14.2.5. By End-user
  • 19.15. South Africa AI Powered Storage Market Analysis
    • 19.15.1. Value Proportion Analysis by Market Taxonomy
    • 19.15.2. Value & Analysis and Forecast by Market Taxonomy, 2024-2031
      • 19.15.2.1. By Storage Architecture
      • 19.15.2.2. By Offering
      • 19.15.2.3. By Storage System
      • 19.15.2.4. By Storage Medium
      • 19.15.2.5. By End-user
  • 19.16. Turkey AI Powered Storage Market Analysis
    • 19.16.1. Value Proportion Analysis by Market Taxonomy
    • 19.16.2. Value & Analysis and Forecast by Market Taxonomy, 2024-2031
      • 19.16.2.1. By Storage Architecture
      • 19.16.2.2. By Offering
      • 19.16.2.3. By Storage System
      • 19.16.2.4. By Storage Medium
      • 19.16.2.5. By End-user
    • 19.16.3. Competition Landscape and Player Concentration in the Country

20. Market Structure Analysis

  • 20.1. Market Analysis by Tier of Companies
  • 20.2. Market Concentration
  • 20.3. Market Share Analysis of Top Players
  • 20.4. Market Presence Analysis
    • 20.4.1. By Regional footprint of Players
    • 20.4.2. Product footprint by Players

21. Competition Analysis

  • 21.1. Competition Dashboard
  • 21.2. Competition Benchmarking
  • 21.3. Competition Deep Dive
    • 21.3.1. Intel Corporation
      • 21.3.1.1. Overview
      • 21.3.1.2. Product Portfolio
      • 21.3.1.3. Sales Footprint
      • 21.3.1.4. Strategy Overview
    • 21.3.2. NVIDIA Corporation
      • 21.3.2.1. Overview
      • 21.3.2.2. Product Portfolio
      • 21.3.2.3. Sales Footprint
      • 21.3.2.4. Strategy Overview
    • 21.3.3. IBM
      • 21.3.3.1. Overview
      • 21.3.3.2. Product Portfolio
      • 21.3.3.3. Sales Footprint
      • 21.3.3.4. Strategy Overview
    • 21.3.4. Samsung Electronics
      • 21.3.4.1. Overview
      • 21.3.4.2. Product Portfolio
      • 21.3.4.3. Sales Footprint
      • 21.3.4.4. Strategy Overview
    • 21.3.5. Pure Storage
      • 21.3.5.1. Overview
      • 21.3.5.2. Product Portfolio
      • 21.3.5.3. Sales Footprint
      • 21.3.5.4. Strategy Overview
    • 21.3.6. NetApp
      • 21.3.6.1. Overview
      • 21.3.6.2. Product Portfolio
      • 21.3.6.3. Sales Footprint
      • 21.3.6.4. Strategy Overview
    • 21.3.7. Micron Technology
      • 21.3.7.1. Overview
      • 21.3.7.2. Product Portfolio
      • 21.3.7.3. Sales Footprint
      • 21.3.7.4. Strategy Overview
    • 21.3.8. CISCO
      • 21.3.8.1. Overview
      • 21.3.8.2. Product Portfolio
      • 21.3.8.3. Sales Footprint
      • 21.3.8.4. Strategy Overview
    • 21.3.9. Toshiba
      • 21.3.9.1. Overview
      • 21.3.9.2. Product Portfolio
      • 21.3.9.3. Sales Footprint
      • 21.3.9.4. Strategy Overview
    • 21.3.10. Hitachi
      • 21.3.10.1. Overview
      • 21.3.10.2. Product Portfolio
      • 21.3.10.3. Sales Footprint
      • 21.3.10.4. Strategy Overview
    • 21.3.11. Lenovo
      • 21.3.11.1. Overview
      • 21.3.11.2. Product Portfolio
      • 21.3.11.3. Sales Footprint
      • 21.3.11.4. Strategy Overview
    • 21.3.12. Dell
      • 21.3.12.1. Overview
      • 21.3.12.2. Product Portfolio
      • 21.3.12.3. Sales Footprint
      • 21.3.12.4. Strategy Overview
    • 21.3.13. HPE
      • 21.3.13.1. Overview
      • 21.3.13.2. Product Portfolio
      • 21.3.13.3. Sales Footprint
      • 21.3.13.4. Strategy Overview

22. Assumptions and Acronyms Used

23. Research Methodology