By 2035, AI-powered Storage Market Size, Share, Trends and Industry Analysis

AI-powered Storage Market by Offering (Hardware, Software), Storage System (DAS, NAS, SAN), Storage Architecture (File & Object-Based Storage), Storage Medium (SSD, HDD), & End User (Enterprises, CSP, Government, Telecom) - Global Forecast to 2035

USD 321.93 BN

MARKET SIZE, 2035

CAGR 24.4%

(2025-2035)

300

REPORT PAGES

230

MARKET TABLES

OVERVIEW

Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis

The AI-Powered Storage market is projected to reach USD 321.93 billion by 2035 from USD 36.28 billion in 2025, at a CAGR of 24.4% from 2025 to 2035. The AI-powered storage market is expanding rapidly as enterprises generate massive volumes of unstructured data and require high-performance, low-latency storage to feed AI/ML workloads. Growth is further driven by the rise of hybrid/multi-cloud environments and the need for automated, intelligent data management at scale. Additionally, advancements in flash, NVMe and software-defined architectures are enabling more efficient, AI-optimized storage deployments across data centers and edge environments.

KEY TAKEAWAYS

By Region

Asia Pacific will be the fastest-growing regional market for AI-powered storage, driven by hyperscale data center expansion and accelerating enterprise AI adoption.
By Storage System

By storage system, the Network-Attached Storage (NAS) segment is expected to dominate the market as AI workloads increasingly require scalable, high-throughput file access.
By Storage Medium

By storage medium, Solid State Drives (SSDs) will register the highest CAGR due to AI’s need for low-latency, high-IOPS performance.
By Deployment

By deployment, the cloud segment will hold the largest market share, while hybrid deployment will grow at the fastest rate through 2035.
By End User

By end user, cloud service providers and hyperscalers will dominate adoption, while BFSI and healthcare enterprises will be the fastest-growing enterprise verticals.

The AI-powered storage market is expanding rapidly as organizations require high-throughput, low-latency data infrastructures to support increasingly complex AI, ML, and GPU-accelerated workloads. Demand is being propelled by the explosive growth of unstructured data, large-scale model training, and real-time analytics across cloud, edge, and enterprise environments. Advancements in NVMe, all-flash arrays, software-defined storage, and unified file-object architectures are enabling faster data access and improved scalability. Major vendors such as VAST Data, WEKA, NetApp, and Lightbits are launching AI-optimized storage platforms, while cloud providers and hyperscalers continue to invest in next-generation data fabrics to remove GPU bottlenecks. Strategic partnerships between storage vendors, semiconductor suppliers, and AI infrastructure providers are accelerating innovation and strengthening the global ecosystem for AI-centric storage solutions.

TRENDS & DISRUPTIONS IMPACTING CUSTOMERS' CUSTOMERS

The AI-powered storage ecosystem spans specialized storage vendors, flash and NVMe providers, software-defined storage platforms, and AI infrastructure integrators who together deliver the high-performance data backbone required for modern AI workloads. These players ultimately serve hyperscalers, data center operators, AI developers, enterprises, and vertical industries like healthcare, BFSI, automotive, and telecom—each relying on fast, scalable, and intelligent data pipelines. The end outcomes include accelerated model training, higher GPU utilization, lower latency across cloud and edge environments, stronger data security, and reduced operational costs, enabling organizations to deploy and scale AI applications more efficiently.

Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis

MARKET DYNAMICS

Drivers

Impact
Level

Rapid growth of unstructured data and AI/ML workloads
Expansion of hybrid and multi-cloud environments

RESTRAINTS

Impact
Level

High upfront investment
Data privacy and regulatory constraints

OPPORTUNITIES

Impact
Level

Rising adoption of edge AI and inference workloads
Growth of AI model training and multimodal workloads

CHALLENGES

Impact
Level

Managing exponential data growth
Eliminating data bottlenecks between storage and GPUs

Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis

Driver: Rising Adoption of AI/ML Workloads

The rapid expansion of AI and machine learning applications is creating unprecedented demand for high-performance, low-latency storage infrastructure. As enterprises scale GPU clusters and train larger multimodal models, they require storage systems capable of delivering massive throughput, intelligent data tiering, and real-time access to unstructured datasets. This surge in AI workloads is directly accelerating investments in AI-optimized storage platforms across cloud, hybrid, and on-prem environments.

Restraint: High Unfront Investment

Despite strong demand, the market faces adoption barriers due to the high capital expenditure associated with implementing AI-optimized storage systems such as all-flash arrays, NVMe fabrics, and AI-driven data management software. Many mid-sized enterprises struggle with the upfront investment required to modernize legacy storage architectures, slowing market penetration and widening the gap between large hyperscale operators and smaller AI adopters.

Opportunity: Expansion of Edge AI and 5G Infrastructure

The rise of edge AI, autonomous systems, and 5G networks is opening new opportunities for AI-powered storage vendors to deliver distributed, low-latency storage solutions. As analytics shift closer to the data source, organizations increasingly require intelligent, compact, and high-throughput storage architectures at the edge to support real-time inference, video analytics, and IoT-driven AI workloads—creating a fast-growing frontier for solution providers.

Challenge: Eliminating Data Bottlenecks for GPUs

A critical challenge for the industry is delivering storage fast enough to keep GPUs fully utilized, especially during large-scale model training and inference operations. As compute power outpaces storage throughput, even high-end AI clusters risk idle time due to inefficient data pipelines, metadata bottlenecks, or insufficient bandwidth. Overcoming this imbalance requires significant innovation in parallel file systems, caching algorithms, and NVMe-over-Fabrics technologies.

ai-powered-storage-market: COMMERCIAL USE CASES ACROSS INDUSTRIES

COMPANY	USE CASE DESCRIPTION	BENEFITS
	A global AI research lab deployed VAST Data’s AI-native storage platform to unify file-object access and feed large GPU clusters with high-throughput data pipelines for model training and inference.	Delivered consistently high GPU utilization and faster model-training cycles by eliminating data bottlenecks and enabling low-latency access at petabyte scale.
	A hyperscale AI compute provider integrated WEKA’s high-performance data platform to accelerate multimodal model training and real-time inference across hybrid and multi-cloud environments.	Enabled ultra-low-latency data delivery and accelerated time-to-insight by keeping GPU fleets fully saturated with optimized data throughput.
	A cloud service provider adopted Lightbits’ NVMe/TCP-based disaggregated storage to support large AI workloads requiring scalable, high-performance block storage for distributed training clusters.	Improved training stability and reduced infrastructure cost by delivering cloud-native, high-IOPS storage with predictable low latency.
	A robotics AI company used Hammerspace’s global data platform to orchestrate data across edge sites, private cloud and GPU training clusters to support continuous model updates.	Enabled seamless data mobility and faster model iteration by providing a single global namespace with automated, AI-driven data placement.

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MARKET ECOSYSTEM

The AI-powered storage market is supported by a broad and interconnected ecosystem of technology providers, infrastructure operators, and solution innovators working together to meet the rising demands of modern AI workloads. This landscape continues to evolve as organizations accelerate digital transformation and look for more efficient ways to manage, process, and scale their data. Growing adoption of AI across industries is driving continuous advancement, collaboration, and investment throughout the ecosystem, strengthening its role in enabling next-generation computing.

Logos and trademarks shown above are the property of their respective owners. Their use here is for informational and illustrative purposes only.

MARKET SEGMENTS

Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis

AI-Powered Storage Market, By Storage System

The Network-Attached Storage (NAS) segment is expected to lead the AI-powered storage market through 2035, supported by the rapid rise of unstructured data and the need for scalable, high-throughput file systems for AI and ML workflows. As enterprises and hyperscalers expand their GPU clusters, NAS platforms deliver the parallel access, metadata handling, and data-sharing capabilities required for large-scale training and inference. With AI workloads increasingly dependent on high-bandwidth, multi-node data pipelines, NAS will remain the preferred storage system architecture across cloud and enterprise environments.

AI-Powered Storage Market, By Storage Medium

Solid State Drives (SSDs) are projected to dominate the AI-powered storage market and record the highest growth rate through 2035, driven by AI’s demand for ultra-low latency and high IOPS performance. As model sizes grow and training cycles accelerate, SSDs particularly NVMe-based systems enable faster data retrieval, parallelism, and sustained throughput essential for keeping GPU clusters saturated. With declining flash prices and expanding adoption of all-flash arrays, SSDs will continue to outpace HDDs and tapes in AI-centric deployments.

AI-Powered Storage Market, By Deployment

The cloud segment is expected to maintain the largest market share, while hybrid deployments will witness the fastest growth through 2035 as organizations balance scalability with data governance needs. AI workloads increasingly leverage cloud-based storage for elasticity, distributed access, and rapid provisioning of high-performance data pipelines. At the same time, hybrid architectures are becoming critical for enterprises managing sensitive data, enabling seamless movement of datasets between on-premise environments and cloud platforms. This dual demand positions both cloud and hybrid models as central pillars of future AI storage strategies.

AI-Powered Storage Market, By Storage Architecture

File-and-object-based storage is set to dominate the AI-powered storage market through 2035, reflecting the explosive rise of unstructured data used for model training, multimodal processing, and analytics. These architectures offer the scalability, parallel access, and flexible metadata structures necessary for modern AI workflows, outperforming legacy file-only or block-only systems. As enterprises adopt unified data lakes and GPU-powered analytics pipelines, file-object architectures will serve as the backbone of next-generation AI storage infrastructures.

AI-Powered Storage Market, By End User

Cloud service providers and hyperscalers are expected to be the largest and fastest-growing end-user segment, driven by their massive investments in AI compute, data centers, and large-scale storage infrastructure. As generative AI, LLM training, and real-time inference workloads surge, hyperscalers require advanced storage architectures capable of delivering extreme bandwidth and reliability at multi-petabyte scale. With continuous expansion of global cloud regions and edge footprints, they will remain the primary demand engine shaping the AI-powered storage market through 2035.

REGION

Asia Pacific to be fastest-growing region in global AI-Powered Storage market during forecast period

Asia Pacific will experience strong growth in the AI-powered storage market as hyperscalers, cloud providers, and semiconductor manufacturers rapidly expand regional AI infrastructure and data center capacity. Rising enterprise adoption of AI/ML across automotive, telecom, and manufacturing, combined with supportive government digital-transformation policies, will further accelerate demand for high-performance, scalable storage.

ai-powered-storage-market: COMPANY EVALUATION MATRIX

In the AI-powered storage market matrix, Dell Technologies (Star) stands at the forefront with a broad, market-leading portfolio of all-flash arrays, software-defined storage, and integrated AI infrastructure that delivers high-performance, scalable data pipelines for enterprise and cloud AI workloads. Its deep ecosystem partnerships and continuous investment in NVMe platforms, intelligent automation, and hybrid cloud capabilities reinforce its leadership in enabling modern AI environments. NetApp (Emerging Leader) is rapidly strengthening its position through its unified file-object architectures and ONTAP AI ecosystem, leveraging strong collaborations with hyperscalers and GPU vendors to deliver efficient, hybrid-ready data fabrics tailored for AI training and inference workloads.

ai-powered-storage-market Evaluation Metrics

Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis

KEY MARKET PLAYERS

MARKET SCOPE

REPORT METRIC	DETAILS
Market Size in 2024 (Value)	USD 29.05 BN
Market Forecast in 2030 (Value)	USD 321.93 BN
Growth Rate	24.40%
Years Considered	2021–2035
Base Year	2024
Forecast Period	2025–2035
Units Considered	Value (USD Billion)
Report Coverage	Revenue forecast, company ranking, competitive landscape, growth factors, and trends
Segments Covered	By Storage System: Direct Attached Storage Network Attached Storage. Storage Area Network By Storage Medium: Hard Disk Drive Solid Disk Drive Tapes By Deployment: On-premises Cloud Hybrid By Storage Architecture: File-object-based-storage Block Storage By End User: Enterprises Govrnment Bodies Cloud Service Providers Telecom Companies
Regions Covered	North America, Europe, Asia Pacific, RoW

WHAT IS IN IT FOR YOU: ai-powered-storage-market REPORT CONTENT GUIDE

DELIVERED CUSTOMIZATIONS

We have successfully delivered the following deep-dive customizations:

CLIENT REQUEST	CUSTOMIZATION DELIVERED	VALUE ADDS
CLIENT REQUEST	CUSTOMIZATION DELIVERED	VALUE ADDS
AI Infrastructure Provider / Hyperscaler	Performance benchmarking of AI-optimized storage platforms (NVMe, all-flash, file-object systems) Assessment of throughput & latency requirements for GPU clusters Mapping of storage scalability options across cloud, edge, and hybrid environments	Improve storage planning accuracy Remove GPU bottlenecks Enhance cluster utilization and reduce training time for large AI models
Enterprise IT / Data Center Operator	Evaluation of storage modernization pathways TCO comparison across on-prem, cloud, and hybrid AI storage models Vendor capability assessment for AI data workloads	Reduce operational overhead Optimize storage footprint for AI/ML pipelines Support long-term architectural scaling
Storage Hardware Manufacturer (SSD/NVMe/HDD)	Market sizing across AI-driven storage media (SSD, NVMe, HDD, tape) Competitive analysis of performance leaders Mapping enterprise & cloud adoption trends for flash-first architectures	Identify high-growth media segments Guide R&D investment toward AI-centric storage components Strengthen product competitiveness
Software-Defined Storage (SDS) Vendor	Analysis of file-object, block, and unified data lake architectures Benchmarking AI metadata management & caching strategies Assessment of multi-cloud data mobility requirements	Accelerate product innovation Strengthen differentiation in AI-optimized storage Enable targeted go-to-market alignment
AI/ML Platform Company	Mapping storage bottlenecks across training and inference pipelines Recommendation of data-tiering frameworks Assessment of integration with top storage vendors (VAST, WEKA, NetApp, Pure)	Boost model training speed Reduce infrastructure cost Improve reliability and scalability for production AI
Telecom / 5G & Edge Computing Provider	Edge storage architecture analysis Benchmarking low-latency distributed storage solutions Mapping regulatory & data-sovereignty constraints	Enhance edge-AI readiness Support ultra-low latency services Improve network-wide data orchestration

RECENT DEVELOPMENTS

January 2025 : Dell Technologies unveiled its next-generation PowerScale and PowerFlex AI-optimized storage platforms, integrating enhanced NVMe performance, automated data tiering, and GPU-aware data paths designed to accelerate generative AI training and inference pipelines in hybrid and multicloud environments.
November 2024 : NetApp expanded its ONTAP AI ecosystem with new unified file-object architectures and tighter integrations with NVIDIA DGX systems, enabling higher throughput and lower latency for large-scale LLM training while simplifying data mobility across cloud and on-prem deployments.
October 2024 : VAST Data launched its latest Universal Storage platform update featuring a new AI-driven data management engine and massively parallel data pipelines purpose-built to keep GPU clusters saturated, supporting petabyte-scale workloads for hyperscalers and enterprise AI labs.
August 2024 : WEKA introduced its NeuralMesh™ data platform upgrade with enhanced metadata acceleration and multicloud deployment capabilities, delivering improved performance for generative AI workflows, real-time inference, and high-bandwidth distributed training environments.
June 2024 : Lightbits Labs released an enhanced version of its NVMe-over-TCP storage software optimized for AI cluster elasticity, providing predictable low-latency block storage for cloud-native GPU workloads and reducing TCO for large-scale model training operations.

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TITLE

PAGE NO

INTRODUCTION

EXECUTIVE SUMMARY

PREMIUM INSIGHTS

MARKET OVERVIEW

INDUSTRY TRENDS

5.1 INTRODUCTION
5.2 PORTERS FIVE FORCE ANALYSIS

THREAT FROM NEW ENTRANTS

THREAT OF SUBSTITUTES

BARGAINING POWER OF SUPPLIERS

BARGAINING POWER OF BUYERS

INTENSITY OF COMPETITIVE RIVALRY
5.3 MACROECONOMICS INDICATORS

INTRODUCTION

GDP TRENDS AND FORECAST

TRENDS IN GLOBAL AI-POWERED STORAGE MARKET
5.4 TRADE ANALYSIS

IMPORT SCENARIO

EXPORT SCENARIO
5.5 VALUE CHAIN ANALYSIS
5.6 ECOSYSTEM ANALYSIS
5.7 PRICING ANALYSIS
5.8 KEY CONFERENCES AND EVENTS, 2025–2026
5.9 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS

INVESTMENT AND FUNDING SCENARIO

CASE STUDY ANALYSIS

IMPACT OF 2025 US TARIFF – AI-POWERED STORAGE MARKET

- Introduction

- Key Tariff Rates

- Price Impact Analysis

- Impact on Countries/Regions

- Impact on Applications

STRATEGIC DISRUPTION THROUGH TECHNOLOGY, PATENTS, DIGITAL, AND AI ADOPTIONS

6.1 KEY EMERGING TECHNOLOGIES
6.2 COMPLEMENTARY TECHNOLOGIES
6.3 ADJACENT TECHNOLOGIES
6.4 TECHNOLOGY ROADMAP
6.5 PATENT ANALYSIS
6.6 FUTURE APPLICATIONS
6.7 IMPACT OF AI/GEN AI ON AI-POWERED STORAGE MARKET

TOP USE CASES AND MARKET POTENTIAL

BEST PRACTICES IN AI-POWERED STORAGE USAGE

CASE STUDIES OF AI IMPLEMENTATION IN THE AI-POWERED STORAGE MARKET

INTERCONNECTED ADJACENT ECOSYSTEM AND IMPACT ON MARKET PLAYERS

CLIENTS’ READINESS TO ADOPT AI IN THE AI-POWERED STORAGE MARKET

REGULATORY LANDSCAPE

7.1 REGIONAL REGULATIONS AND COMPLIANCE

REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS

INDUSTRY STANDARDS

CUSTOMER LANDSCAPE & BUYER BEHAVIOR

8.1 DECISION-MAKING PROCESS
8.2 BUYER STAKEHOLDERS AND BUYING EVALUATION CRITERIA

KEY STAKEHOLDERS IN THE BUYING PROCESS

BUYING CRITERIA
8.3 ADOPTION BARRIERS & INTERNAL CHALLENGES
8.4 UNMET NEEDS FROM VARIOUS APPLICATIONS
8.5 MARKET PROFITABILITY

AI-POWERED STORAGE MARKET, BY STORAGE SYSTEM

110

9.1 INTRODUCTION
9.2 DIRECT-ATTACHED STORAGE (DAS)
9.3 NETWORK-ATTACHED STORAGE (NAS)
9.4 STORAGE AREA NETWORK (SAN)

AI-POWERED STORAGE MARKET, BY STORAGE MEDIUM

130

10.1 INTRODUCTION
10.2 HARD DISK DRIVE (HDD)
10.3 SOLID STATE DRIVE (SDD)
10.4 TAPES

AI-POWERED STORAGE MARKET, BY DEPLOYMENT

150

11.1 INTRODUCTION
11.2 CLOUD
11.3 ON-PREMISES
11.4 HYBRID

AI-POWERED STORAGE MARKET, BY STORAGE ARCHITECTURE

170

12.1 INTRODUCTION
12.2 FILE-AND-OBJECT-BASED STORAGE

FILE STORAGE

OBJECT STORAGE
12.3 BLOCK STORAGE

AI-POWERED STORAGE MARKET, BY END USER

190

13.1 INTRODUCTION
13.2 ENTERPRISES

BFSI

MANUFACTURING

CONSUMER GOODS & RETAIL

HEALTHCARE & LIFE SCIENCES

MEDIA & ENTERTAINMENT

OTHER ENTERPRISES
13.3 GOVERNMENT BODIES
13.4 CLOUD SERVICE PROVIDERS/HYPERSCALERS

AI-POWERED STORAGE MARKET, BY REGION

210

14.1 INTRODUCTION
14.2 NORTH AMERICA

US

CANADA

MEXICO
14.3 EUROPE

GERMANY

UK

FRANCE

ITALY

REST OF EUROPE
14.4 ASIA PACIFIC

CHINA

JAPAN

SOUTH KOREA

INDIA

REST OF ASIA PACIFIC
14.5 ROW

MIDDLE EAST & AFRICA

- GCC

- Rest of the Middle East

SOUTH AMERICA

AI-POWERED STORAGE MARKET, COMPETITIVE LANDSCAPE

230

15.1 INTRODUCTION
15.2 KEY PLAYER STRATEGIES/RIGHT TO WIN
15.3 REVENUE ANALYSIS
15.4 MARKET SHARE ANALYSIS, 2024
15.5 COMPANY VALUATION AND FINANCIAL METRICS
15.6 PRODUCT/BRAND COMPARISON
15.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024

STARS

EMERGING LEADERS

PERVASIVE PLAYERS

PARTICIPANTS

COMPANY FOOTPRINT: KEY PLAYERS, 2024

- Company Footprint

- Region Footprint

- End User Footprint

- Storage System Footprint

- Storage Type Footprint
15.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024

PROGRESSIVE COMPANIES

RESPONSIVE COMPANIES

DYNAMIC COMPANIES

STARTING BLOCKS

COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024

- Detailed List of Key Startups/SMEs

- Competitive Benchmarking of Key Startups/SMEs
15.9 COMPETITIVE SCENARIO

PRODUCT LAUNCHES

DEALS

AI-POWERED STORAGE MARKET, COMPANY PROFILES

250

16.1 KEY PLAYERS

DELL TECHNOLOGIES

HEWLETT PACKARD ENTERPRISE (HPE) COMPANY

IBM

HUAWEI TECHNOLOGIES

PURE STORAGE

VAST DATA

NETAPP

SAMSUNG ELECTRONICS

COHESITY, INC.

- Cloudian, Inc.
16.2 OTHER PLAYERS

RESEARCH METHODOLOGY

270

17.1 RESEARCH DATA
17.2 SECONDARY DATA

KEY DATA FROM SECONDARY SOURCES

PRIMARY DATA

- Key Data from Primary Sources

- Key Primary Participants

- Breakdown of Primary Interviews

- Key Industry Insights

MARKET SIZE ESTIMATION

- Bottom-Up Approach

- Top-Down Approach

- Base Number Calculation

MARKET FORECAST APPROACH

- Supply Side

- Demand Side

DATA TRIANGULATION

RESEARCH ASSUMPTIONS

RESEARCH LIMITATIONS AND RISK ASSESSMENT

APPENDIX

300

18.1 DISCUSSION GUIDE
18.2 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
18.3 AVAILABLE CUSTOMIZATIONS
18.4 RELATED REPORTS
18.5 AUTHOR DETAILS

AI-POWERED STORAGE Market, by storage system

INTRODUCTION

The AI-powered storage market has been segmented by storage system.

TABLE 1AI-POWERED STORAGE Market, By storage system, 2021–2024 (USD MILLION)

Storage System	2021	2022	2023	2024	CAGR (2021–2024)
Direct-Attached Storage (DAS)	xx	xx	xx	xx	xx%
Network-Attached Storage (NAS)	xx	xx	xx	xx	xx%
Storage Area Network (SAN)	xx	xx	xx	xx	xx%
Total	xx	xx	xx	xx	xx%