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市場調查報告書
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人工智慧資料基礎設施市場預測至2034年—按基礎設施類型、人工智慧工作負載、部署模式、資料環境、最終用戶和地區分類的全球分析

AI Data Infrastructure Market Forecasts to 2034 - Global Analysis By Infrastructure Type, AI Workload, Deployment, Data Environment, End User, and Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球人工智慧數據基礎設施市場規模將達到 120 億美元，並在預測期內以 25.0% 的複合年成長率成長，到 2034 年將達到 715 億美元。

人工智慧資料基礎設施是指收集、儲存、處理、管理和交付人工智慧 (AI) 工作負載所需資料的硬體、軟體、平台和資料管理技術。這包括資料湖、資料倉儲、向量資料庫、資料管道、特徵儲存、元資料平台、高效能儲存和資料處理系統。這些技術在為機器學習、生成式人工智慧、分析和模型開發準備結構化和非結構化資料方面發揮著重要作用。企業正在利用人工智慧資料基礎設施來提高資料的可存取性、品質、可擴展性和處理效能。生成式人工智慧和企業級機器學習的日益普及推動了對專用資料架構的需求。自動化資料準備、向量搜尋和即時處理的整合變得日益重要。企業也在加強管治和安全功能，以管理敏感的人工智慧訓練和推理資料。

市場動態

對人工智慧工作負載的需求不斷成長

隨著人工智慧在各行業的應用不斷深入，對能夠支援人工智慧工作負載的數據基礎設施的需求也日益成長。各組織正在投資於針對機器學習和深度學習最佳化的儲存、處理和網路解決方案。不斷成長的資料量和複雜性要求基礎設施具備可擴展性。人工智慧模型和應用的進步正在加速基礎設施需求的成長。人工智慧資料基礎設施正成為企業取得競爭優勢的關鍵。

高成本且複雜

建構人工智慧最佳化基礎設施的高成本和複雜性是其普及應用的主要障礙。與現有數據環境整合需要專業知識。人工智慧工作負載的電力和冷卻需求也增加了運維的複雜性。科技的快速更新換代需要持續的投資。許多組織缺乏建構全面人工智慧基礎設施所需的資源。

人工智慧專用硬體和雲端服務的進步

人工智慧專用硬體和雲端人工智慧服務的進步正在創造巨大的成長機會。隨著人工智慧最佳化型儲存和網路解決方案的開發，其功能也不斷擴展。隨著託管式人工智慧基礎設施服務的日益普及，營運複雜性正在降低。基礎設施供應商與人工智慧平台公司之間的合作正在加速人工智慧技術的應用。技術進步持續提升性能和效率。

來自通用基礎設施的競爭

通用資料基礎設施的競爭可能會限制人工智慧最佳化解決方案的普及。經濟壓力可能會影響基礎設施投資決策。技術複雜性可能會影響使用者信心和採用決策。整合方面的挑戰可能會限制在某些環境中的部署。缺乏人工智慧基礎設施的專業知識可能會限制市場成長。

新型冠狀病毒（COVID-19）的影響：

新冠感染疾病加速了數位轉型和人工智慧（AI）的普及，也因此增加了對AI數據基礎設施的需求。各組織紛紛投資AI能力建設，以支援遠距辦公和自動化。疫情後，對AI基礎設施的投資仍在持續。人們對AI驅動的洞察日益重視，這持續推動著AI技術的應用。 AI數據基礎設施在數位轉型中正變得愈發重要。

在預測期內，資料儲存基礎設備基礎設施領域預計將佔據最大的市場佔有率。

儲存是人工智慧資料基礎設施的基礎，因此，預計在預測期內，資料儲存基礎設備領域將佔據最大的市場佔有率。人工智慧工作負載需要高效能、可擴展的儲存來儲存訓練資料和模型。不斷成長的資料量和模型規模正在推動對先進儲存解決方案的需求。成熟的儲存供應商和豐富的產品系列鞏固了該領域的領先地位。儲存對於人工智慧資料管道至關重要。

在預測期內，人工智慧世代細分市場預計將呈現最高的複合年成長率。

在預測期內，生成式人工智慧領域預計將呈現最高的成長率，這主要得益於生成式人工智慧模型日益普及。生成式人工智慧需要大規模資料基礎設施進行訓練和推理。對生成式人工智慧能力的投資不斷增加，正在加速對基礎設施的需求。模型架構的進步也提高了對基礎設施的要求。生成式人工智慧正在改變人工智慧資料基礎設施的需求。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這得益於其先進的人工智慧應用、強大的人工智慧基礎設施供應商網路以及對人工智慧能力的巨額投資。美國位置許多主要的人工智慧基礎設施公司和超大規模資料中心。高額的技術投資和創新文化正在鞏固其在該地區的市場領導地位。人工智慧應用的不斷擴展正在推動全部區域的基礎設施需求。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於快速的數位轉型、人工智慧應用的不斷擴展以及對數據基礎設施投資的持續成長。中國、日本和韓國正在不斷提升其人工智慧基礎設施能力。技術投資和資料中心建設的不斷增加正在加速市場成長。政府對人工智慧發展的支持也全部區域的市場擴張提供了有力支撐。

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所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要公司進行SWOT分析（最多3家公司）
  • 區域細分
    • 根據客戶要求，我們可以提供主要國家的市場估算和預測，以及複合年成長率（註：需經可行性確認）。
  • 競爭性標竿分析
    • 根據產品系列、地理覆蓋範圍和策略聯盟對領先公司進行基準分析。

目錄

第1章：執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章：全球人工智慧資料基礎設施市場：以基礎設施類型分類

  • 資料儲存基礎設備
  • 資料處理基礎設施
  • 數據整合基礎設施
  • 資料管理基礎設施
  • 數據網路基礎設施
  • 其他基礎設施類型

第6章 全球人工智慧資料基礎設施市場：按人工智慧工作負載分類

  • 機器學習
  • 深度學習
  • 人工智慧世代
  • 電腦視覺
  • 自然語言處理
  • 其他人工智慧工作負載

第7章 全球人工智慧資料基礎設施市場：按部署類型分類

  • 雲
  • 現場

第8章 全球人工智慧資料基礎設施市場：按資料環境分類

  • 資料湖
  • 資料倉儲
  • 湖畔別墅
  • 物件儲存
  • 分散式資料庫
  • 其他數據環境

第9章 全球人工智慧資料基礎設施市場：按最終用戶分類

  • 科技公司
  • 銀行和金融服務
  • 衛生保健
  • 製造業
  • 零售與電子商務
  • 其他最終用戶

第10章：全球人工智慧資料基礎設施市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第11章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第12章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第13章：公司簡介

  • NVIDIA Corporation
  • Amazon Web Services, Inc.
  • Microsoft Corporation
  • Google LLC
  • IBM Corporation
  • Oracle Corporation
  • Databricks, Inc.
  • Snowflake Inc.
  • Palantir Technologies Inc.
  • Cloudera, Inc.
  • Confluent, Inc.
  • NetApp, Inc.
  • Dell Technologies Inc.
  • Hewlett Packard Enterprise Company
  • Pure Storage, Inc.
Product Code: SMRC39802

According to Stratistics MRC, the Global AI Data Infrastructure Market is accounted for $12.0 billion in 2026 and is expected to reach $71.5 billion by 2034 growing at a CAGR of 25.0% during the forecast period. AI Data Infrastructure encompasses the hardware, software, platforms, and data-management technologies required to collect, store, process, govern, and deliver data for artificial intelligence workloads. It includes data lakes, data warehouses, vector databases, data pipelines, feature stores, metadata platforms, high-performance storage, and data-processing systems. These technologies prepare structured and unstructured data for machine learning, generative AI, analytics, and model development. Organizations use AI data infrastructure to improve data accessibility, quality, scalability, and processing performance. Growing adoption of generative AI and enterprise machine learning is increasing demand for specialized data architectures. Integration of automated data preparation, vector search, and real-time processing is becoming increasingly important. Organizations are also strengthening governance and security capabilities to manage sensitive AI training and inference data.

Market Dynamics

Driver:

Growing demand for AI workloads

Increasing adoption of artificial intelligence across industries is driving demand for data infrastructure that can support AI workloads. Organizations are investing in storage, processing, and networking solutions optimized for machine learning and deep learning. Growing data volumes and complexity require scalable infrastructure. Advances in AI models and applications are accelerating infrastructure requirements. AI data infrastructure is becoming essential for competitive advantage.

Restraint:

High costs and complexity

High costs of AI-optimized infrastructure and complexity of deployment present significant adoption barriers. Integration with existing data environments requires specialized expertise. Power and cooling requirements for AI workloads add operational complexity. Rapid technology obsolescence requires continuous investment. Many organizations lack resources for comprehensive AI infrastructure.

Opportunity:

Advances in AI-specific hardware and cloud services

Advances in AI-specific hardware and cloud-based AI services present significant growth opportunities. Development of optimized storage and networking solutions for AI is expanding capabilities. Growing availability of managed AI infrastructure services reduces operational complexity. Partnerships between infrastructure providers and AI platform companies accelerate adoption. Technology advances continue improving performance and efficiency.

Threat:

Competition from general-purpose infrastructure

Competition from general-purpose data infrastructure may limit adoption of AI-optimized solutions. Economic pressures may affect infrastructure investment decisions. Technology complexity may affect user confidence and adoption decisions. Integration challenges may limit adoption in certain environments. Limited availability of AI infrastructure expertise may constrain market growth.

Covid-19 Impact:

The COVID-19 pandemic accelerated digital transformation and AI adoption, increasing demand for AI data infrastructure. Organizations invested in AI capabilities to support remote operations and automation. The post-pandemic period has witnessed sustained investment in AI infrastructure. Growing focus on AI-driven insights continues driving adoption. AI data infrastructure has gained importance for digital transformation.

The data storage infrastructure segment is expected to be the largest during the forecast period

The data storage infrastructure segment is expected to account for the largest market share during the forecast period as storage represents the foundation of AI data infrastructure. AI workloads require high-performance, scalable storage for training data and models. Growing data volumes and model sizes drive demand for advanced storage solutions. Established storage vendors and broad product portfolios support segment leadership. Storage is essential for AI data pipelines.

The generative AI segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the generative AI segment is predicted to witness the highest growth rate driven by increasing adoption of generative AI models. Generative AI requires massive data infrastructure for training and inference. Growing investment in generative AI capabilities is accelerating infrastructure demand. Advances in model architectures increase infrastructure requirements. Generative AI is transforming AI data infrastructure needs.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share owing to advanced AI adoption, strong presence of AI infrastructure vendors, and significant investment in AI capabilities. The United States hosts major AI infrastructure companies and hyperscale data centers. High technology investment and innovation culture reinforce regional market leadership. Growing AI adoption drives infrastructure demand across the region.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR driven by rapid digital transformation, growing AI adoption, and increasing investment in data infrastructure. China, Japan, and South Korea are expanding AI infrastructure capabilities. Growing technology investment and data center development accelerate market growth. Government support for AI development supports market expansion across the region.

Key players in the market

Some of the key players in the AI Data Infrastructure Market include NVIDIA Corporation, Amazon Web Services, Inc., Microsoft Corporation, Google LLC, IBM Corporation, Oracle Corporation, Databricks, Inc., Snowflake Inc., Palantir Technologies Inc., Cloudera, Inc., Confluent, Inc., NetApp, Inc., Dell Technologies Inc., Hewlett Packard Enterprise Company, and Pure Storage, Inc.

Key Developments:

In May 2025, NVIDIA Corporation launched a new generation of AI data infrastructure solutions with enhanced performance for generative AI workloads. The solutions enable faster training and inference for large language models. The development responds to growing demand for AI infrastructure.

In April 2025, Amazon Web Services, Inc. announced significant expansion of its AI data infrastructure services with new storage and networking capabilities for AI workloads.

Infrastructure Types Covered:

  • Data Storage Infrastructure
  • Data Processing Infrastructure
  • Data Integration Infrastructure
  • Data Management Infrastructure
  • Data Networking Infrastructure
  • Other Infrastructure Types

AI Workloads Covered:

  • Machine Learning
  • Deep Learning
  • Generative AI
  • Computer Vision
  • Natural Language Processing
  • Other AI Workloads

Deployments Covered:

  • Cloud
  • On-Premises

Data Environments Covered:

  • Data Lakes
  • Data Warehouses
  • Lakehouses
  • Object Storage
  • Distributed Databases
  • Other Data Environments

End Users Covered:

  • Technology Companies
  • Banking & Financial Services
  • Healthcare
  • Manufacturing
  • Retail & E-Commerce
  • Other End Users

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of 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 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • 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

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI Data Infrastructure Market, By Infrastructure Type

  • 5.1 Data Storage Infrastructure
  • 5.2 Data Processing Infrastructure
  • 5.3 Data Integration Infrastructure
  • 5.4 Data Management Infrastructure
  • 5.5 Data Networking Infrastructure
  • 5.6 Other Infrastructure Types

6 Global AI Data Infrastructure Market, By AI Workload

  • 6.1 Machine Learning
  • 6.2 Deep Learning
  • 6.3 Generative AI
  • 6.4 Computer Vision
  • 6.5 Natural Language Processing
  • 6.6 Other AI Workloads

7 Global AI Data Infrastructure Market, By Deployment

  • 7.1 Cloud
  • 7.2 On-Premises

8 Global AI Data Infrastructure Market, By Data Environment

  • 8.1 Data Lakes
  • 8.2 Data Warehouses
  • 8.3 Lakehouses
  • 8.4 Object Storage
  • 8.5 Distributed Databases
  • 8.6 Other Data Environments

9 Global AI Data Infrastructure Market, By End User

  • 9.1 Technology Companies
  • 9.2 Banking & Financial Services
  • 9.3 Healthcare
  • 9.4 Manufacturing
  • 9.5 Retail & E-Commerce
  • 9.6 Other End Users

10 Global AI Data Infrastructure Market, By Geography

  • 10.1 North America
    • 10.1.1 United States
    • 10.1.2 Canada
    • 10.1.3 Mexico
  • 10.2 Europe
    • 10.2.1 United Kingdom
    • 10.2.2 Germany
    • 10.2.3 France
    • 10.2.4 Italy
    • 10.2.5 Spain
    • 10.2.6 Netherlands
    • 10.2.7 Belgium
    • 10.2.8 Sweden
    • 10.2.9 Switzerland
    • 10.2.10 Poland
    • 10.2.11 Rest of Europe
  • 10.3 Asia Pacific
    • 10.3.1 China
    • 10.3.2 Japan
    • 10.3.3 India
    • 10.3.4 South Korea
    • 10.3.5 Australia
    • 10.3.6 Indonesia
    • 10.3.7 Thailand
    • 10.3.8 Malaysia
    • 10.3.9 Singapore
    • 10.3.10 Vietnam
    • 10.3.11 Rest of Asia Pacific
  • 10.4 South America
    • 10.4.1 Brazil
    • 10.4.2 Argentina
    • 10.4.3 Colombia
    • 10.4.4 Chile
    • 10.4.5 Peru
    • 10.4.6 Rest of South America
  • 10.5 Rest of the World (RoW)
    • 10.5.1 Middle East
      • 10.5.1.1 Saudi Arabia
      • 10.5.1.2 United Arab Emirates
      • 10.5.1.3 Qatar
      • 10.5.1.4 Israel
      • 10.5.1.5 Rest of Middle East
    • 10.5.2 Africa
      • 10.5.2.1 South Africa
      • 10.5.2.2 Egypt
      • 10.5.2.3 Morocco
      • 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

  • 11.1 Industry Value Network and Supply Chain Assessment
  • 11.2 White-Space and Opportunity Mapping
  • 11.3 Product Evolution and Market Life Cycle Analysis
  • 11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

  • 12.1 Mergers and Acquisitions
  • 12.2 Partnerships, Alliances, and Joint Ventures
  • 12.3 New Product Launches and Certifications
  • 12.4 Capacity Expansion and Investments
  • 12.5 Other Strategic Initiatives

13 Company Profiles

  • 13.1 NVIDIA Corporation
  • 13.2 Amazon Web Services, Inc.
  • 13.3 Microsoft Corporation
  • 13.4 Google LLC
  • 13.5 IBM Corporation
  • 13.6 Oracle Corporation
  • 13.7 Databricks, Inc.
  • 13.8 Snowflake Inc.
  • 13.9 Palantir Technologies Inc.
  • 13.10 Cloudera, Inc.
  • 13.11 Confluent, Inc.
  • 13.12 NetApp, Inc.
  • 13.13 Dell Technologies Inc.
  • 13.14 Hewlett Packard Enterprise Company
  • 13.15 Pure Storage, Inc.

List of Tables

  • Table 1 Global AI Data Infrastructure Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI Data Infrastructure Market, By Infrastructure Type (2023-2034) ($MN)
  • Table 3 Global AI Data Infrastructure Market, By Data Storage Infrastructure (2023-2034) ($MN)
  • Table 4 Global AI Data Infrastructure Market, By Data Processing Infrastructure (2023-2034) ($MN)
  • Table 5 Global AI Data Infrastructure Market, By Data Integration Infrastructure (2023-2034) ($MN)
  • Table 6 Global AI Data Infrastructure Market, By Data Management Infrastructure (2023-2034) ($MN)
  • Table 7 Global AI Data Infrastructure Market, By Data Networking Infrastructure (2023-2034) ($MN)
  • Table 8 Global AI Data Infrastructure Market, By Other Infrastructure Types (2023-2034) ($MN)
  • Table 9 Global AI Data Infrastructure Market, By AI Workload (2023-2034) ($MN)
  • Table 10 Global AI Data Infrastructure Market, By Machine Learning (2023-2034) ($MN)
  • Table 11 Global AI Data Infrastructure Market, By Deep Learning (2023-2034) ($MN)
  • Table 12 Global AI Data Infrastructure Market, By Generative AI (2023-2034) ($MN)
  • Table 13 Global AI Data Infrastructure Market, By Computer Vision (2023-2034) ($MN)
  • Table 14 Global AI Data Infrastructure Market, By Natural Language Processing (2023-2034) ($MN)
  • Table 15 Global AI Data Infrastructure Market, By Other AI Workloads (2023-2034) ($MN)
  • Table 16 Global AI Data Infrastructure Market, By Deployment (2023-2034) ($MN)
  • Table 17 Global AI Data Infrastructure Market, By Cloud (2023-2034) ($MN)
  • Table 18 Global AI Data Infrastructure Market, By On-Premises (2023-2034) ($MN)
  • Table 19 Global AI Data Infrastructure Market, By Data Environment (2023-2034) ($MN)
  • Table 20 Global AI Data Infrastructure Market, By Data Lakes (2023-2034) ($MN)
  • Table 21 Global AI Data Infrastructure Market, By Data Warehouses (2023-2034) ($MN)
  • Table 22 Global AI Data Infrastructure Market, By Lakehouses (2023-2034) ($MN)
  • Table 23 Global AI Data Infrastructure Market, By Object Storage (2023-2034) ($MN)
  • Table 24 Global AI Data Infrastructure Market, By Distributed Databases (2023-2034) ($MN)
  • Table 25 Global AI Data Infrastructure Market, By Other Data Environments (2023-2034) ($MN)
  • Table 26 Global AI Data Infrastructure Market, By End User (2023-2034) ($MN)
  • Table 27 Global AI Data Infrastructure Market, By Technology Companies (2023-2034) ($MN)
  • Table 28 Global AI Data Infrastructure Market, By Banking & Financial Services (2023-2034) ($MN)
  • Table 29 Global AI Data Infrastructure Market, By Healthcare (2023-2034) ($MN)
  • Table 30 Global AI Data Infrastructure Market, By Manufacturing (2023-2034) ($MN)
  • Table 31 Global AI Data Infrastructure Market, By Retail & E-Commerce (2023-2034) ($MN)
  • Table 32 Global AI Data Infrastructure Market, By Other End Users (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.