Computational Storage Market by Offering (Hardware (Processor and SSD), Software), Type (Fixed Computational Storage and Programmable Computational Storage), End-use Industry, and Region - Global Forecast to 2032

icon1
USD 4.30 BN
MARKET SIZE, 2032
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CAGR 29%
(2026-2032)
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300
REPORT PAGES
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250
MARKET TABLES

OVERVIEW

computational-storage-market Overview

Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis

The computational storage market is projected to reach USD 4.30 billion by 2032 from USD 0.93 billion in 2026, at a CAGR of 29.0% from 2026 to 2032. The demand for faster data processing and reduced latency in AI and big data workloads is driving the growth of the computational storage market.

KEY TAKEAWAYS

  • By Region
    North America accounted for the largest market share of 41.0% in 2025.
  • By Offering
    By offering, the software segment is expected to grow at the highest CAGR of 33.6% from 2026 to 2032.
  • By Type
    By type, fixed computational storage accounted for the largest market share of 66.8% in 2025.
  • BY End Use Industry
    By end-use Industry, enterprise storage is expected to lead the market in 2026.
  • Competitive Landscape
    Samsung Electronics Co., Ltd. (South Korea) and ScaleFlux (US) are identified as the star players in the computational storage market, given their strong market share and product footprint.
  • Competitive Landscape
    Nyriad (US) and Phison Electronics Corporation (Taiwan), among others, have distinguished themselves among startups and SMEs by securing strong footholds in specialized niche areas, underscoring their potential as emerging market leaders.

The computational storage market presents significant opportunities driven by the growing demand for real-time data processing and advanced analytics. Increasing adoption of AI, machine learning, and big data applications is creating strong demand for intelligent storage solutions. Expansion of cloud computing and hyperscale data centers is further opening new avenues for market players. Advancements in storage software and processor technologies are enabling more efficient and scalable solutions. Companies can leverage these opportunities by investing in innovation, forming strategic partnerships, and offering customized solutions for enterprise and cloud customers.

TRENDS & DISRUPTIONS IMPACTING CUSTOMERS' CUSTOMERS

The computational storage market is undergoing a structural shift driven by the rapid growth of AI, big data analytics, and power-constrained data center environments. Traditional storage-centric revenue models are evolving toward programmable and intelligent storage architectures that enable near-data processing. This disruption is creating new revenue streams through advanced computational storage devices, software platforms, and cloud-focused solutions. Over the next 7–10 years, this transition is expected to improve system efficiency, reduce total cost of ownership, and accelerate performance across data-intensive computing environments.

computational-storage-market Disruptions

Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis

MARKET DYNAMICS

Drivers
Impact
Level
  • Rising data-intensive workloads (AI, big data analytics, HPC)
  • Reduction of CPU and memory bottlenecks
RESTRAINTS
Impact
Level
  • High initial implementation cost
  • Limited ecosystem maturity
OPPORTUNITIES
Impact
Level
  • Adoption in data centers and cloud environments
  • Increasing use in AI/ML pipelines
CHALLENGES
Impact
Level
  • Lack of industry-wide standardization
  • Complex system integration

Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis

Driver: Rising data-intensive workloads (AI, big data analytics, HPC)

Rapid growth in AI training, big data analytics, and HPC workloads is increasing data volumes and processing requirements. This is driving demand for near-data processing to reduce data movement, improve throughput, and lower latency in enterprise and hyperscale environments.

Restraint: High initial implementation cost

Computational storage requires specialized hardware such as FPGA- or ASIC-based SSDs along with customized firmware and software integration. The high upfront investment compared to conventional NVMe SSDs limits short-term adoption, especially among cost-sensitive enterprises.

Opportunity: Adoption in data centers and cloud environments

Hyperscale and enterprise data centers are increasingly adopting computational storage to improve performance, reduce data movement, and optimize power consumption across storage-intensive workloads.

Challenge: Lack of industry-wide standardization

Absence of unified standards for interfaces, programming models, and APIs limits interoperability across vendors, increasing vendor lock-in risks and slowing enterprise adoption.

COMPUTATIONAL STORAGE MARKET: COMMERCIAL USE CASES ACROSS INDUSTRIES

COMPANY USE CASE DESCRIPTION BENEFITS
Integration of computational storage SSDs with embedded processors to accelerate data filtering, compression, and encryption at the storage level Reduces data movement, lowers latency, improves system performance, and decreases CPU workload
Deployment of SmartSSD with FPGA-based processing for real-time analytics and AI inference within storage devices Enhances processing speed, minimizes network traffic, and improves energy efficiency

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

MARKET ECOSYSTEM

The computational storage ecosystem comprises a diverse set of participants, including manufacturers (OEMs/ODMs), storage solution providers, and end users. Leading OEMs such as Dell Technologies, HPE, Huawei, and IBM focus on developing advanced hardware platforms and integrated systems. Storage solution providers like Samsung, Seagate, Micron, and Western Digital drive innovation in intelligent storage devices and software solutions. End users across industries, including IT services, energy, automotive, and financial sectors, adopt computational storage to enhance data processing efficiency. This collaborative ecosystem enables continuous technological advancement and supports the widespread adoption of computational storage solutions.

computational-storage-market Ecosystem

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

MARKET SEGMENTS

computational-storage-market Segments

Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis

Computational Storage Market, by Offering

Hardware is expected to account for the largest share of the computational storage market due to its essential role in enabling in-storage data processing. Components such as processors, controllers, and smart SSDs form the foundation of computational storage systems. Increasing investments in high-performance data centers and AI infrastructure are driving strong demand for advanced storage hardware. Additionally, enterprises prefer reliable, scalable hardware solutions to support data-intensive workloads, reinforcing hardware’s market dominance.

Computational Storage Market, by Type

Fixed computational storage is expected to hold the largest market share due to its high reliability and consistent performance in enterprise and data center environments. It is widely adopted for handling large-scale data processing, analytics, and AI workloads. Fixed storage solutions offer better integration with existing IT infrastructure, making deployment and management more efficient. Their ability to support high-capacity and high-speed operations further drives adoption among cloud and hyperscale providers. As a result, fixed computational storage continues to dominate the market across major end-use industries.

Computational Storage Market, by Type

Enterprise storage is expected to hold the largest market share in the computational storage market due to rising demand for high-performance and secure data management solutions. Large organizations increasingly rely on computational storage to support AI, analytics, and mission-critical applications. Its ability to handle massive data volumes with low latency makes it ideal for enterprise environments. Additionally, strong investments in digital transformation and data center modernization are further driving adoption of enterprise storage solutions.

REGION

Asia Pacific to be fastest-growing region in global computational storage market during forecast period

Asia Pacific is expected to witness the fastest growth in the computational storage market, driven by rapid digital transformation and expanding data center infrastructure. Rising adoption of cloud computing, AI, and big data analytics across countries such as China, India, and Japan is fueling market demand. Increasing investments by technology companies and governments in smart cities and digital initiatives are further supporting growth. The presence of emerging enterprises and growing startup ecosystems is accelerating the adoption of advanced storage solutions. As a result, Asia Pacific is becoming a key growth hub for the global computational storage market.

computational-storage-market Region

COMPUTATIONAL STORAGE MARKET: COMPANY EVALUATION MATRIX

In the computational storage market matrix, stars are the leading market players in terms of new developments, such as product launches, innovative technologies, and the adoption of growth strategies. These players have a broad product portfolio, innovative product offerings, and a global presence. They have well-established partnership networks throughout the value chain. Samsung Electronics Co., Ltd. (South Korea) is a star player in the computational storage market. Emerging leaders are established vendors with effective business strategies and a good market presence. They follow the strategies of stars and have the potential to become market stars. Several companies in the computational storage market depend on their partner networks. NETINT Technologies (Canada) falls under this category.

computational-storage-market Evaluation Metrics

Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis

KEY MARKET PLAYERS

MARKET SCOPE

REPORT METRIC DETAILS
Market Size in 2025 (Value) USD 0.72 Billion
Market Forecast in 2032 (Value) USD 4.30 Billion
Growth Rate CAGR of 29.0% from 2026–2032
Years Considered 2022–2032
Base Year 2025
Forecast Period 2026–2032
Units Considered Value (USD Billion), Volume (Thousand Unit)
Report Coverage Revenue Forecast, Company Ranking, Competitive Landscape, Growth Factors, and Trends
Segments Covered
  • By Offering:
    • Hardware
    • Software
  • By Type:
    • Fixed Computational Storage
    • Programmable Computational Storage
  • By End-use Industry:
    • Enterprise Storage
    • Government
    • CSP
Regions Covered North America, Asia Pacific, Europe, and RoW

WHAT IS IN IT FOR YOU: COMPUTATIONAL STORAGE MARKET REPORT CONTENT GUIDE

computational-storage-market Content Guide

DELIVERED CUSTOMIZATIONS

We have successfully delivered the following deep-dive customizations:

CLIENT REQUEST CUSTOMIZATION DELIVERED VALUE ADDS
Computational Storage Market by Type, Architecture, and Interface Detailed segmentation by storage type (fixed, removable), architecture (CSD, CSP, CSX), and interface (NVMe, SATA, SAS) Identified high-growth segments and key technology adoption trends
Regional Analysis of Computational Storage Adoption Assessment of computational storage deployment across data centers, cloud providers, and enterprises in North America, Europe, and Asia Pacific Provided regional competitiveness mapping and ecosystem analysis
Application-wise Demand Assessment Analysis of computational storage usage in AI/ML, big data analytics, databases, and edge computing applications Highlighted high-potential application areas driving market demand
Competitive Landscape and Vendor Benchmarking Evaluation of key players based on product portfolio, performance, pricing, and strategic initiatives Enabled clients to identify market leaders and partnership opportunities
Technology Roadmap and Future Outlook Analysis of emerging technologies such as AI-accelerated storage, programmable SSDs, and in-storage processing Supported long-term investment and product development strategies

RECENT DEVELOPMENTS

  • December 2025 : AMD strengthened partnerships with storage vendors, OEMs, and cloud service providers to support computational storage architectures through reference designs and optimized platforms, accelerating adoption across enterprise and hyperscale data centers.
  • July 2024 : Introducing the new high-performance NVMe CSD5000 series, featuring the FX5016 PCIe 5 SSD controller designed for AI, cloud, and data center workloads; this marks a significant performance and capacity milestone.
  • August 2023 : AMD enhanced the ROCm software platform to support broader heterogeneous computing and tighter integration with data-centric workloads. These updates enable better coordination between CPUs, accelerators, and storage-adjacent compute in computational storage environments.
  • July 2022 : Samsung launched its SmartSSD, integrating Xilinx FPGA-based processing with high-capacity NAND flash to enable in-storage data processing. The solution offloads tasks such as data filtering, compression, and analytics from the host CPU, improving performance and reducing data movement in data center environments.

Table of Contents

Exclusive indicates content/data unique to MarketsandMarkets and not available with any competitors.

TITLE
PAGE NO
1
INTRODUCTION
 
 
 
15
2
EXECUTIVE SUMMARY
 
 
 
 
3
PREMIUM INSIGHTS
 
 
 
 
4
MARKET OVERVIEW
This section summarizes market dynamics, key shifts, and high-impact trends shaping demand outlook.
 
 
 
 
 
4.1
INTRODUCTION
 
 
 
 
4.2
MARKET DYNAMICS
 
 
 
 
 
4.2.1
DRIVERS
 
 
 
 
 
4.2.1.1
RISING DATA-INTENSIVE WORKLOADS, SUCH AS AI, BIG DATA ANALYTICS, AND HPC, INCREASE NEED TO PROCESS DATA CLOSER TO WHERE IT IS STORED
 
 
 
 
4.2.1.2
DEMAND FOR REDUCED CPU AND MEMORY BOTTLENECKS, AS COMPUTATIONAL STORAGE OFFLOADS DATA PROCESSING TASKS FROM HOST PROCESSORS
 
 
 
4.2.2
RESTRAINTS
 
 
 
 
 
4.2.2.1
HIGH INITIAL IMPLEMENTATION COST DUE TO SPECIALIZED HARDWARE AND SOFTWARE INTEGRATION REQUIREMENTS
 
 
 
 
4.2.2.2
LIMITED ECOSYSTEM MATURITY, WITH FEWER STANDARDIZED TOOLS AND LIMITED COMPATIBILITY ACROSS PLATFORMS
 
 
 
4.2.3
OPPORTUNITIES
 
 
 
 
 
4.2.3.1
GROWING ADOPTION IN DATA CENTERS AND CLOUD ENVIRONMENTS TO IMPROVE PERFORMANCE AND ENERGY EFFICIENCY
 
 
 
 
4.2.3.2
INCREASING USE IN AI/ML PIPELINES, WHERE PREPROCESSING DATA AT STORAGE LEVEL CAN SIGNIFICANTLY REDUCE LATENCY
 
 
 
4.2.4
CHALLENGES
 
 
 
 
 
4.2.4.1
LACK OF INDUSTRY-WIDE STANDARDIZATION, IMPACTING INTEROPERABILITY BETWEEN DEVICES FROM DIFFERENT VENDORS
 
 
 
 
4.2.4.2
COMPLEX SYSTEM INTEGRATION, REQUIRING CHANGES AT THE SOFTWARE, OS, AND APPLICATION LAYERS
 
 
4.3
UNMET NEEDS AND WHITE SPACES
 
 
 
 
4.4
INTERCONNECTED MARKETS AND CROSS-SECTOR OPPORTUNITIES
 
 
 
 
4.5
STRATEGIC MOVES BY TIER-1/2/3 PLAYERS
 
 
 
5
INDUSTRY TRENDS
Provides a snapshot of current market scenario, value chain context, and factors impacting competitive intensity.
 
 
 
 
 
5.1
PORTER'S FIVE FORCES ANALYSIS
 
 
 
 
 
5.1.1
THREAT OF NEW ENTRANTS
 
 
 
 
5.1.2
THREAT OF SUBSTITUTES
 
 
 
 
5.1.3
BARGAINING POWER OF SUPPLIERS
 
 
 
 
5.1.4
BARGAINING POWER OF BUYERS
 
 
 
 
5.1.5
INTENSITY OF COMPETITIVE RIVALRY
 
 
 
5.2
MACROECONOMICS OUTLOOK
 
 
 
 
 
5.2.1
INTRODUCTION
 
 
 
 
5.2.2
GDP TRENDS AND FORECAST
 
 
 
 
5.2.3
TRENDS IN GLOBAL STORAGE INDUSTRY
 
 
 
5.3
SUPPLY CHAIN ANALYSIS
 
 
 
 
 
5.4
ECOSYSTEM ANALYSIS
 
 
 
 
 
5.5
PRICING ANALYSIS
 
 
 
 
 
 
5.5.1
AVERAGE SELLING PRICE TREND OF STORAGE TYPE, BY KEY PLAYER (2022–2025)
 
 
 
 
5.5.2
AVERAGE SELLING PRICE TREND, BY REGION (2022–2024)
 
 
 
5.6
TRADE ANALYSIS
 
 
 
 
 
 
5.6.1
IMPORT SCENARIO (HS CODE 848620)
 
 
 
 
5.6.2
EXPORT SCENARIO (HS CODE 848620)
 
 
 
5.7
KEY CONFERENCES AND EVENTS, 2025–2026
 
 
 
 
5.8
TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
 
 
 
 
5.9
INVESTMENT AND FUNDING SCENARIO
 
 
 
 
5.10
CASE STUDY ANALYSIS
 
 
 
 
5.11
IMPACT OF 2025 US TARIFF – COMPUTATIONAL STORAGE MARKET
 
 
 
 
 
 
5.11.1
INTRODUCTION
 
 
 
 
5.11.2
KEY TARIFF RATES
 
 
 
 
5.11.3
PRICE IMPACT ANALYSIS
 
 
 
 
5.11.4
IMPACT ON COUNTRIES/REGIONS
 
 
 
 
 
5.11.4.1
US
 
 
 
 
5.11.4.2
EUROPE
 
 
 
 
5.11.4.3
ASIA PACIFIC
 
 
 
5.11.5
IMPACT ON INDUSTRIES
 
 
6
TECHNOLOGICAL ADVANCEMENTS, AI-DRIVEN IMPACT, PATENTS, INNOVATIONS, AND FUTURE APPLICATIONS
 
 
 
 
 
6.1
KEY EMERGING TECHNOLOGIES
 
 
 
 
 
6.1.1
NVME/PCIE INTERFACES
 
 
 
 
6.1.2
EMBEDDED PROCESSING UNITS (FPGAS, SOCS, & ASICS)
 
 
 
6.2
COMPLEMENTARY TECHNOLOGIES
 
 
 
 
 
6.2.1
SOLID-STATE DRIVES (SSDS) & FLASH MEMORY
 
 
 
 
6.2.2
IN-STORAGE PROCESSING APIS & SOFTWARE STACKS
 
 
 
6.3
ADJACENT TECHNOLOGIES
 
 
 
 
 
6.3.1
DISAGGREGATED STORAGE/COMPOSABLE INFRASTRUCTURE
 
 
 
 
6.3.2
EDGE & NEAR-DATA PROCESSING PARADIGMS
 
 
 
6.4
TECHNOLOGY/PRODUCT ROADMAP
 
 
 
 
6.5
PATENT ANALYSIS
 
 
 
 
 
6.6
FUTURE APPLICATIONS
 
 
 
 
6.7
IMPACT OF AI/GEN AI ON COMPUTATIONAL STORAGE MARKET
 
 
 
 
 
 
6.7.1
TOP USE CASES AND MARKET POTENTIAL
 
 
 
 
6.7.2
BEST PRACTICES IN COMPUTATIONAL STORAGE MARKET
 
 
 
 
6.7.3
CASE STUDIES OF AI IMPLEMENTATION IN COMPUTATIONAL STORAGE MARKET
 
 
 
 
6.7.4
INTERCONNECTED ADJACENT ECOSYSTEM AND IMPACT ON MARKET PLAYERS
 
 
 
 
6.7.5
CLIENTS’ READINESS TO ADOPT GENERATIVE AI IN COMPUTATIONAL STORAGE MARKET
 
 
7
REGULATORY LANDSCAPE AND SUSTAINABILITY INITIATIVES
 
 
 
 
 
7.1
REGIONAL REGULATIONS AND COMPLIANCE
 
 
 
 
 
7.1.1
REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
 
 
 
 
7.1.2
INDUSTRY STANDARDS
 
 
 
7.2
SUSTAINABILITY INITIATIVES
 
 
 
 
7.3
IMPACT OF REGULATORY POLICIES ON SUSTAINABILITY INITIATIVES
 
 
 
8
CUSTOMER LANDSCAPE & BUYER BEHAVIOR
 
 
 
 
 
8.1
INTRODUCTION
 
 
 
 
8.2
DECISION-MAKING PROCESS
 
 
 
 
8.3
KEY STAKEHOLDERS INVOLVED IN BUYING PROCESS AND THEIR EVALUATION CRITERIA
 
 
 
 
8.4
ADOPTION BARRIERS & INTERNAL CHALLENGES
 
 
 
 
8.5
UNMET NEEDS FROM END-USE INDUSTRIES
 
 
 
9
COMPUTATIONAL STORAGE MARKET, BY OFFERING
Market Size, Volume & Forecast – USD Million
 
 
 
 
 
9.1
INTRODUCTION
 
 
 
 
9.2
HARDWARE
 
 
 
 
 
9.2.1
PROCESSORS
 
 
 
 
 
9.2.1.1
EMBEDDED
 
 
 
 
9.2.1.2
NON-EMBEDDED
 
 
 
9.2.2
SSD (ONLY EMBEDDED)
 
 
 
9.3
SOFTWARE
 
 
 
10
COMPUTATIONAL STORAGE MARKET, BY TYPE
Market Size, Volume & Forecast – USD Million
 
 
 
 
 
10.1
INTRODUCTION
 
 
 
 
10.2
FIXED COMPUTATIONAL STORAGE
 
 
 
 
10.3
PROGRAMMABLE COMPUTATIONAL STORAGE
 
 
 
11
COMPUTATIONAL STORAGE MARKET, BY END-USE INDUSTRY
Market Size, Volume & Forecast – USD Million
 
 
 
 
 
11.1
INTRODUCTION
 
 
 
 
11.2
ENTERPRISE STORAGE
 
 
 
 
 
11.2.1
BFSI
 
 
 
 
11.2.2
AUTOMOTIVE
 
 
 
 
11.2.3
RETAIL
 
 
 
 
11.2.4
HEALTHCARE
 
 
 
 
11.2.5
TELECOMMUNICATION
 
 
 
 
11.2.6
MANUFACTURING
 
 
 
 
11.2.7
OTHERS
 
 
 
11.3
GOVERNMENT
 
 
 
 
11.4
CSP
 
 
 
12
COMPUTATIONAL STORAGE MARKET, BY REGION
Market Size, Volume & Forecast – USD Million
 
 
 
 
 
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
FRANCE
 
 
 
 
12.3.3
UK
 
 
 
 
12.3.4
ITALY
 
 
 
 
12.3.5
SPAIN
 
 
 
 
12.3.6
POLAND
 
 
 
 
12.3.7
NORDIC COUNTRIES
 
 
 
 
12.3.8
REST OF EUROPE
 
 
 
12.4
ASIA PACIFIC
 
 
 
 
 
12.4.1
CHINA
 
 
 
 
12.4.2
JAPAN
 
 
 
 
12.4.3
INDIA
 
 
 
 
12.4.4
SOUTH KOREA
 
 
 
 
12.4.5
AUSTRALIA
 
 
 
 
12.4.6
REST OF ASIA PACIFIC
 
 
 
12.1
REST OF THE WORLD
 
 
 
 
 
12.1.1
MIDDLE EAST & AFRICA
 
 
 
 
 
12.1.1.1
GCC COUNTRIES
 
 
 
 
12.1.1.2
REST OF MIDDLE EAST & AFRICA
 
 
 
12.1.2
SOUTH AMERICA
 
 
13
COMPETITIVE LANDSCAPE
 
 
 
 
 
13.1
OVERVIEW
 
 
 
 
13.2
KEY PLAYER COMPETITIVE STRATEGIES/RIGHT TO WIN (JANUARY 2021–DECEMBER 2025)
 
 
 
 
13.3
REVENUE ANALYSIS (2021–2025)
 
 
 
 
 
13.4
MARKET SHARE ANALYSIS,
 
 
 
 
 
13.5
BRAND/PRODUCT COMPARISON
 
 
 
 
 
 
13.5.1
SAMSUNG
 
 
 
 
13.5.2
SCALEFLUX
 
 
 
 
13.5.3
MARVELL TECHNOLOGY
 
 
 
 
13.5.4
INTEL CORPORATION
 
 
 
 
13.5.5
ADVANCED MICRO DEVICES
 
 
 
13.6
COMPANY EVALUATION MATRIX: KEY PLAYERS,
 
 
 
 
 
 
13.6.1
STARS
 
 
 
 
13.6.2
EMERGING LEADERS
 
 
 
 
13.6.3
PERVASIVE PLAYERS
 
 
 
 
13.6.4
PARTICIPANTS
 
 
 
 
13.6.5
COMPANY FOOTPRINT: KEY PLAYERS,
 
 
 
 
 
13.6.5.1
COMPANY FOOTPRINT
 
 
 
 
13.6.5.2
REGION FOOTPRINT
 
 
 
 
13.6.5.3
OFFERING FOOTPRINT
 
 
 
 
13.6.5.4
TYPE FOOTPRINT
 
 
 
 
13.6.5.5
END-USE INDUSTRY FOOTPRINT
 
 
13.7
14.7 COMPANY EVALUATION MATRIX: STARTUPS/SMES,
 
 
 
 
 
 
13.7.1
PROGRESSIVE COMPANIES
 
 
 
 
13.7.2
RESPONSIVE COMPANIES
 
 
 
 
13.7.3
DYNAMIC COMPANIES
 
 
 
 
13.7.4
STARTING BLOCKS
 
 
 
 
13.7.5
COMPETITIVE BENCHMARKING: STARTUPS/SMES
 
 
 
 
 
13.7.5.1
DETAILED LIST OF KEY STARTUPS/SMES
 
 
 
 
13.7.5.2
COMPETITIVE BENCHMARKING OF KEY STARTUPS/SMES
 
 
13.8
COMPANY VALUATION AND FINANCIAL METRICS
 
 
 
 
13.9
COMPETITIVE SCENARIO
 
 
 
 
 
13.9.1
PRODUCT LAUNCHES
 
 
 
 
13.9.2
DEALS
 
 
 
 
13.9.3
EXPANSIONS
 
 
14
COMPANY PROFILES
 
 
 
 
 
14.1
KEY PLAYERS
 
 
 
 
 
14.1.1
SAMSUNG
 
 
 
 
14.1.2
SCALEFLUX
 
 
 
 
14.1.3
MARVELL TECHNOLOGY
 
 
 
 
14.1.4
INTEL CORPORATION
 
 
 
 
14.1.5
ADVANCED MICRO DEVICES
 
 
 
 
14.1.6
AIC INC.
 
 
 
 
14.1.7
ARM LIMITED
 
 
 
 
14.1.8
NGD SYSTEMS
 
 
 
 
14.1.9
EIDETICOM
 
 
 
 
14.1.10
ARM
 
 
 
14.2
OTHER PLAYERS
 
 
 
 
 
14.2.1
NVIDIA CORPORATION
 
 
 
 
14.2.2
KALRAY
 
 
 
 
14.2.3
ΝΕΤINT TECHNOLOGIES
 
 
 
 
14.2.4
NYRIAD
 
 
 
 
14.2.5
PHISON ELECTRONICS
 
 
 
 
14.2.6
PLIOPS
 
 
 
 
14.2.7
VIA TECHNOLOGIES, INC.
 
 
 
 
14.2.8
NETAPP
 
 
 
 
14.2.9
ACHRONIX SEMICONDUCTOR CORPORATION
 
 
 
 
14.2.10
CALYPSO SYSTEMS
 
 
 
 
14.2.11
LIGHTBITS LABS
 
 
 
 
14.2.12
IBM
 
 
 
 
14.2.13
MINIO
 
 
 
 
14.2.14
DATADIRECT NETWORKS
 
 
 
 
14.2.15
HAMMERSPACE
 
 
15
RESEARCH METHODOLOGY
 
 
 
 
 
15.1
RESEARCH DATA
 
 
 
 
 
15.1.1
SECONDARY DATA
 
 
 
 
 
15.1.1.1
KEY DATA FROM SECONDARY SOURCES
 
 
 
15.1.2
PRIMARY DATA
 
 
 
 
 
15.1.2.1
KEY DATA FROM PRIMARY SOURCES
 
 
 
 
15.1.2.2
KEY PRIMARY PARTICIPANTS
 
 
 
 
15.1.2.3
BREAKDOWN OF PRIMARY INTERVIEWS
 
 
 
 
15.1.2.4
KEY INDUSTRY INSIGHTS
 
 
15.2
MARKET SIZE ESTIMATION
 
 
 
 
 
15.2.1
BOTTOM-UP APPROACH
 
 
 
 
15.2.2
TOP-DOWN APPROACH
 
 
 
 
15.2.3
BASE NUMBER CALCULATION
 
 
 
15.3
MARKET FORECAST APPROACH
 
 
 
 
 
15.3.1
SUPPLY SIDE
 
 
 
 
15.3.2
DEMAND SIDE
 
 
 
15.4
DATA TRIANGULATION
 
 
 
 
15.5
FACTOR ANALYSIS
 
 
 
 
15.6
RESEARCH ASSUMPTIONS
 
 
 
 
15.7
RESEARCH LIMITATIONS AND RISK ASSESSMENT
 
 
 
16
APPENDIX
 
 
 
 
 
16.1
DISCUSSION GUIDE
 
 
 
 
16.2
KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
 
 
 
 
16.3
CUSTOMIZATION OPTIONS
 
 
 
 
16.4
RELATED REPORTS
 
 
 
 
16.5
AUTHOR DETAILS
 
 
 

Methodology

The study involved four major activities in estimating the size of the computational storage market. Exhaustive secondary research was done to collect information on the market, peer market, and parent market. The next step was to validate these findings, assumptions, and sizing with industry experts across value chains through primary research. The bottom-up approach was employed to estimate the overall market size. After that, market breakdown and data triangulation were used to estimate segment and subsegment market sizes.

Secondary Research

In the secondary research process, sources such as annual reports, press releases, investor presentations of companies, white papers, and articles by recognized authors were referred to. Secondary research was done to obtain key information about the market’s supply chain, the market’s value chain, the pool of key market players, and market segmentation according to industry trends, region, and developments from both market and technology perspectives.

Primary Research

Extensive primary research has been conducted after understanding and analyzing the computational storage market scenario through secondary research. Several primary interviews have been conducted with key opinion leaders from both demand- and supply-side vendors across four major regions—North America, Europe, Asia Pacific, and the RoW. Approximately 25% of primary interviews have been conducted with the demand side and 75% with the supply side. This primary data has been collected through telephonic interviews, questionnaires, and emails.

Computational Storage Market Size, and Share

Note: “Others” include sales, marketing specialists, and product managers. The 3 tiers of the companies are defined based on their total revenue as of 2024; tier 1: revenue greater than USD 1 billion, tier 2: revenue between USD 500 million and USD 1 billion, and tier 3: revenue less than USD 500 million.

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Market Size Estimation

In the complete market engineering process, top-down and bottom-up approaches and several data triangulation methods have been used to estimate and forecast the market size for the overall market segments and subsegments listed in this report. Extensive qualitative and quantitative analyses have been conducted across the complete market engineering process to present key information/insights throughout the report.
The key players in the market have been identified through secondary research, and their market shares in the respective regions have been determined through primary and secondary research. This entire procedure includes studying the annual and financial reports of the top players, as well as extensive interviews with industry experts (such as CEOs, VPs, directors, and marketing executives) to provide key insights (both quantitative and qualitative) on the computational storage market. All percentage shares, splits, and breakdowns have been determined using secondary sources and verified through primary sources. All the possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data. This data has been consolidated and supplemented with detailed inputs and analysis from MarketsandMarkets and presented in this report.

Computational Storage Market : Top-Down and Bottom-Up Approach

Computational Storage Market Top Down and Bottom Up Approach

In the top-down approach, the overall market size has been used to estimate the size of the individual markets (mentioned in the market segmentation) through percentage splits from secondary and primary research. For the calculation of specific market segments, the most appropriate immediate parent market size has been used to implement the top-down approach. The bottom-up approach has also been implemented for the data obtained from the secondary research to validate the market size of various segments. Each company’s market share has been estimated to verify the revenue shares used earlier in the bottom-up approach. With the data triangulation procedure and validation of the data through primaries, the overall parent market size and each individual market size have been determined and confirmed in this study.

Data Triangulation

After arriving at the overall market size through the above process, the overall market has been split into several segments. To complete the overall market engineering process and arrive at the exact statistics for all the segments, market breakdown, and data triangulation procedures have been employed, wherever applicable. The data has been triangulated by studying various factors and trends from both the demand and supply sides. The market has also been validated using both top-down and bottom-up approaches.

Market Definition

Computational storage refers to an advanced storage architecture that integrates processing capabilities directly within the storage device, allowing data to be processed where it is stored rather than being transferred to a separate CPU. This approach reduces latency, offloads workloads from the central processor, and improves overall system efficiency, especially in data-intensive applications such as AI, big data analytics, and high-performance computing. By combining storage and computation, computational storage enables faster data handling, real-time analytics, and optimized performance for enterprises and cloud data centers. It represents a shift from traditional storage solutions toward intelligent, workload-aware storage systems.

Key Stakeholders

  • Raw material suppliers
  • Storage manufacturers
  • Semiconductor manufacturers
  • Research institutes and government organizations
  • Traders, distributors, and suppliers of electronic devices
  • Logic device manufacturers
  • Semiconductor industry players

Report Objectives

  • To define, describe, and forecast the computational storage market based on offering, type, end-use industry, and region
  • To forecast the market size across four major regions, namely North America, Europe, Asia Pacific, and the Rest of the World (RoW), along with their respective countries, in terms of value
  • To describe and forecast the computational storage market size in terms of volume
  • To present detailed information regarding the major factors influencing the market (drivers, restraints, opportunities, and challenges)
  • To provide an analysis of the market ecosystem, case studies, patents, technologies, pricing analysis, Porter’s five forces, and regulations pertaining to the market
  • To offer a comprehensive overview of the value chain of the computational storage market ecosystem
  • To critically analyze micromarkets with respect to individual growth trends, prospects, and contributions to the total market
  • To strategically profile the key players and comprehensively analyze their market shares and core competencies
  • To assess the opportunities in the market for stakeholders and describe the competitive landscape of the market
  • To analyze competitive developments, such as collaborations, acquisitions, partnerships, and product developments, in the market
  • To analyze the impact of AI/GenAI, the 2025 US tariff, and the macroeconomic outlook for each region covered under the study

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Growth opportunities and latent adjacency in Computational Storage Market

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