메모리 중심 컴퓨팅 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the memory-centric computing market size is projected to expand from USD 11.36 billion in 2025 and USD 13.72 billion in 2026 to USD 32.16 billion by 2031, registering a CAGR of 18.57% between 2026 to 2031.

This report is Segmented by Component (In-Memory Databases and Data Grids, and More), Deployment (On-Premises and Private Cloud, and More), Application (Real-Time Analytics and Business Intelligence, and More), End User (BFSI, IT and Telecommunications, and More), Data Architecture (Pure In-Memory Architecture, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Memory-Centric Computing Market Trends and Insights

Proliferation of AI-Native Workloads and Vector Search

AI-native applications have pushed the memory-centric computing market closer to the center of enterprise infrastructure planning because inference, retrieval, and agent coordination all depend on low-latency state access. Google Research described memory-centric computing as a structural redesign of database architecture, with disaggregated memory pools offering a path to scale performance-sensitive data systems more efficiently. That shift matters because agentic systems not only read data quickly, they also need a durable multi-session state that simple cache layers cannot reliably provide. Aerospike reinforced this direction in March 2026 when it launched LangGraph integration for NoSQL Database 8 to provide durable, low-latency memory support for stateless agentic AI workflows. As more enterprises move from AI experimentation to production deployment, the memory-centric computing market is benefiting from workloads that need persistence, concurrency, and fast recovery in the same stack. This is why platform selection is increasingly tied to how well vendors support agent state, retrieval pipelines, and transaction-grade responsiveness rather than raw caching speed alone.

Escalating Need for Low-Latency Decision Engines in Digital Banking and Fraud Control

The memory-centric computing market is also advancing because real-time financial decisioning no longer leaves room for deferred processing windows. A 2025 study in the International Journal of Fundamental Mathematics Research found that real-time banking fraud detection pipelines require prediction latency below 50 milliseconds per transaction, and it noted that in-memory feature stores can serve pre-computed values in single-digit milliseconds by removing disk I/O. Volt Active Data published an architecture benchmark showing that a Tier-1 bank ran more than 2,000 production rules within a 50ms authorization budget while handling throughput above 10,000 transactions per second. This operating model changes the buying logic because compliance, customer experience, and fraud prevention now depend on the same low-latency foundation. It also helps explain why regulated institutions continue to treat in-memory platforms as core operational infrastructure rather than optional performance software. In the memory-centric computing market, financial workloads remain important because they reward stable latency, rapid rule changes, and high transaction concurrency at the same time.

High Hardware and Infrastructure Cost for Large-Scale Memory Fabric Deployment

High hardware cost still limits how quickly the memory-centric computing market can move into the mid-market segment. Full memory-heavy deployments require more expensive capacity planning than flash- or disk-led architectures, and that increases approval friction for buyers with fixed infrastructure budgets. The challenge becomes larger when organizations need CXL-capable server refreshes and related integration work before they can scale production use. Microsoft Azure's support for CXL-based memory expansion shows that the technology is becoming commercially viable, but it also underlines the reality that adoption depends on compatible next-generation hardware. In practice, this keeps some enterprises in hybrid designs longer than they would prefer, especially when procurement committees weigh performance benefits against multi-year capital planning. The memory-centric computing market, therefore, continues to grow, but cost discipline still shapes the pace at which buyers move from pilot deployments to broad infrastructure replacement.

Other drivers and restraints analyzed in the detailed report include:

1. Growth of High-Density Data Ingestion from Cloud-Native and Streaming Applications
2. Shift Toward Persistent Memory and Storage-Class Memory for Performance Efficiency
3. Data Gravity, Replication Overhead, and Inter-Cluster Latency Constraints

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

In-Memory Databases and Data Grids held 42.28% of the component segment in 2025, which made them the largest building block inside the memory-centric computing market. Their lead reflects the fact that transaction-heavy use cases in BFSI, IT, and telecommunications still need stable, low-latency reads and writes that general storage systems do not deliver consistently. These platforms also benefit from being deeply embedded in operational workflows where reliability matters as much as response time. In-Memory Caching and Application Acceleration Platforms are projected to record the fastest growth at 18.99% through 2031, as application teams place distributed cache layers between microservices and back-end databases to absorb spikes more efficiently. Stream Processing and Event Processing Platforms continue to gain share because more enterprises now treat event pipelines as a live operating layer rather than a reporting channel. Apache Flink 2.0 supported this shift by adding vector search and LLM inference capabilities directly in streaming SQL, which pulled advanced event processing closer to in-memory execution.

The structure of the component segment is becoming more competitive because functional boundaries are no longer as clear as they once were. Caching vendors are moving toward broader state management, while stream processors are increasingly handling workloads that were once reserved for purpose-built in-memory databases. That overlap widens the strategic scope of the memory-centric computing market because buyers can now enter through application acceleration, data grids, or real-time streams instead of through a core database replacement alone. Enterprises that manage several point tools are also looking for consolidation, since separate platforms for cache, streaming, and grid operations can create extra operational cost and governance complexity. In this setting, unified offerings have a stronger selling position because they reduce integration overhead while keeping latency performance within acceptable limits. The memory-centric computing industry is therefore seeing platform convergence at the component level, even though best-of-breed specialists still hold strong positions in demanding use cases.

Managed Cloud and SaaS held 45.66% share in 2025 and also recorded the highest projected CAGR at 19.16%, which made it the strongest delivery model in the memory-centric computing market. That dual position shows how buyers increasingly prefer operational elasticity over fixed memory-fabric investments, especially when they want faster implementation and lower platform administration burden. Public cloud remains the next major path because hyperscalers continue to expand managed in-memory service portfolios across enterprise accounts. On-premises and private cloud still matter, particularly in regulated workloads where data sensitivity, sovereignty, or infrastructure control carry more weight than rapid migration. The DSAG Investitionsreport 2026 found that 70% of surveyed German-speaking enterprises had SAP S/4HANA migration running or completed, and that pattern supports durable demand for private and managed environments built around SAP HANA. Oracle and Microsoft also expanded Oracle Database@Azure in March 2025 with Exadata Database Service on Exascale Infrastructure, reducing minimum infrastructure costs by up to 95% versus dedicated Exadata platforms.

The deployment story is no longer a simple shift from on-premises to cloud, because hybrid models now define a large share of enterprise rollouts. In June 2026, Nokia, SAP, and Microsoft signed a multi-year agreement to run Nokia's SAP S/4HANA landscape on Microsoft Azure under RISE with SAP, which shows how large enterprises are using cloud-managed in-memory environments for major transformation programs. This kind of migration suggests that the memory-centric computing market will keep growing through coexistence models, not only through direct displacement of private infrastructure. Many enterprises still want on-premises control for sensitive data while linking those environments to managed platforms for analytics, resilience, and scaling. That keeps deployment competition broad, since vendors must serve full cloud migration, hosted private environments, and hybrid extension paths at the same time. The memory-centric computing industry is therefore being shaped as much by flexible consumption models as by raw technology differentiation.

Complete Report Scope:

• By Component
  • In-Memory Databases and Data Grids
  • In-Memory Caching and Application Acceleration Platforms
  • Stream Processing and Event Processing Platforms
• By Deployment Mode
  • On-Premises and Private Cloud
  • Public Cloud
  • Managed Cloud and SaaS
• By Application
  • Real-Time Analytics and Business Intelligence
  • Digital Applications, Caching, and Personalization
  • Fraud Detection, Risk Management, and Financial Trading
  • IoT, Edge Analytics, and Telemetry Processing
  • AI/ML Applications and Decision Automation
• By End User
  • BFSI
  • IT and Telecommunications
  • Retail, E-Commerce, and Digital Platforms
  • Healthcare and Life Sciences
  • Manufacturing and Automotive
  • Government and Public Sector
  • Other End Users
• By Data Architecture
  • Pure In-Memory Architecture
  • Hybrid In-Memory Architecture with Persistent Storage
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • India
    • Southeast Asia
    • Rest of Asia-Pacific
  • South America
  • Middle East and Africa

Geography Analysis

North America held 42.34% of the memory-centric computing market share in 2025, which kept it as the largest regional contributor by revenue. The region benefits from a dense concentration of financial services technology buyers, hyperscale cloud infrastructure, and enterprise software vendors that already package in-memory capabilities into larger platform portfolios. The United States remains the center of adoption because fraud detection, algorithmic trading, and real-time personalization are already well funded and commercially scaled there. Canada adds support through financial services and government analytics demand, while Mexico is gaining relevance through nearshoring-linked manufacturing intelligence deployments. This installed base gives the memory-centric computing market a strong regional foundation where both specialist vendors and large integrated providers can commercialize new capabilities quickly.

Europe remains important because enterprise application modernization and regulatory discipline are shaping demand at the same time. Germany is the region's largest market, and DSAG reported in 2026 that 70% of surveyed German-speaking enterprises had SAP S/4HANA migration running or completed, which directly supports in-memory infrastructure demand because every S/4HANA deployment relies on SAP HANA. The United Kingdom and France also remain large markets because of sustained investment in financial services and public sector digital programs. In June 2026, Nokia, SAP, and Microsoft formalized a multi-year agreement to run Nokia's SAP S/4HANA landscape on Microsoft Azure, which highlighted the scale of enterprise in-memory migration activity taking place in Europe. Italy and the rest of Europe are growing more gradually, with demand centered on banking and automotive manufacturing rather than broad-based cloud migration.

Asia-Pacific is projected to record the fastest CAGR at 19.46% through 2031, which gives it the strongest expansion profile in the memory-centric computing market size over the forecast period. Demand in the region is being driven by digital banking growth in India and Southeast Asia, private 5G industrial deployments in Japan and South Korea, and continued scale in Chinese e-commerce and digital payments. South Korea has a dual advantage because industrial edge demand is rising while the country also remains close to semiconductor and memory hardware innovation. Japan continues to support adoption through industrial IoT, precision manufacturing, and enterprise analytics modernization. South America remains smaller but is improving as hyperscale investment reduces latency barriers in Brazil and nearby markets, while the Middle East and Africa is gaining traction from smart city programs and financial sector modernization initiatives aligned with national diversification agendas.

1. Microsoft Corporation
2. Oracle Corporation
3. SAP SE
4. IBM Corporation
5. Amazon Web Services, Inc.
6. Google LLC
7. Redis Ltd.
8. GridGain Systems, Inc.
9. GigaSpaces Technologies Ltd.
10. Hazelcast Inc.
11. TIBCO Software Inc.
12. Software AG
13. SAS Institute Inc.
14. Datastax, Inc.
15. MemVerge, Inc.
16. Hewlett Packard Enterprise Company
17. Fujitsu Limited
18. KX Systems, Inc.
19. Volt Active Data, Inc.
20. Aerospike, Inc.
21. Exasol AG

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Proliferation of AI-Native Workloads and Vector Search
  • 4.2.2 Escalating Need For Low-Latency Decision Engines in Digital Banking and Fraud Control
  • 4.2.3 Growth of High-Density Data Ingestion From Cloud-Native and Streaming Applications
  • 4.2.4 Shift Toward Persistent Memory and Storage-Class Memory for Performance Efficiency
  • 4.2.5 Expansion of Edge Analytics in 5G, Industrial IoT, and Real-Time Telemetry
  • 4.2.6 Rising Replacement Demand From Legacy Disk-Heavy Analytical Stacks
• 4.3 Market Restraints
  • 4.3.1 High Hardware and Infrastructure Cost for Large-Scale Memory Fabric Deployment
  • 4.3.2 Data Gravity, Replication Overhead, and Inter-Cluster Latency Constraints
  • 4.3.3 Vendor Lock-In Risks in Proprietary In-Memory Platforms and Appliances
  • 4.3.4 Shortage of Specialized Memory Systems Architects and Runtime Engineers
• 4.4 Supply-Chain Analysis
• 4.5 Technological Outlook
• 4.6 Regulatory Landscape
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Bargaining Power of Suppliers
  • 4.7.2 Bargaining Power of Buyers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Component
  • 5.1.1 In-Memory Databases and Data Grids
  • 5.1.2 In-Memory Caching and Application Acceleration Platforms
  • 5.1.3 Stream Processing and Event Processing Platforms
• 5.2 By Deployment Mode
  • 5.2.1 On-Premises and Private Cloud
  • 5.2.2 Public Cloud
  • 5.2.3 Managed Cloud and SaaS
• 5.3 By Application
  • 5.3.1 Real-Time Analytics and Business Intelligence
  • 5.3.2 Digital Applications, Caching, and Personalization
  • 5.3.3 Fraud Detection, Risk Management, and Financial Trading
  • 5.3.4 IoT, Edge Analytics, and Telemetry Processing
  • 5.3.5 AI/ML Applications and Decision Automation
• 5.4 By End User
  • 5.4.1 BFSI
  • 5.4.2 IT and Telecommunications
  • 5.4.3 Retail, E-Commerce, and Digital Platforms
  • 5.4.4 Healthcare and Life Sciences
  • 5.4.5 Manufacturing and Automotive
  • 5.4.6 Government and Public Sector
  • 5.4.7 Other End Users
• 5.5 By Data Architecture
  • 5.5.1 Pure In-Memory Architecture
  • 5.5.2 Hybrid In-Memory Architecture with Persistent Storage
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 Europe
    • 5.6.2.1 Germany
    • 5.6.2.2 United Kingdom
    • 5.6.2.3 France
    • 5.6.2.4 Italy
    • 5.6.2.5 Rest of Europe
  • 5.6.3 Asia-Pacific
    • 5.6.3.1 China
    • 5.6.3.2 Japan
    • 5.6.3.3 South Korea
    • 5.6.3.4 India
    • 5.6.3.5 Southeast Asia
    • 5.6.3.6 Rest of Asia-Pacific
  • 5.6.4 South America
  • 5.6.5 Middle East and Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 Microsoft Corporation
  • 6.4.2 Oracle Corporation
  • 6.4.3 SAP SE
  • 6.4.4 IBM Corporation
  • 6.4.5 Amazon Web Services, Inc.
  • 6.4.6 Google LLC
  • 6.4.7 Redis Ltd.
  • 6.4.8 GridGain Systems, Inc.
  • 6.4.9 GigaSpaces Technologies Ltd.
  • 6.4.10 Hazelcast Inc.
  • 6.4.11 TIBCO Software Inc.
  • 6.4.12 Software AG
  • 6.4.13 SAS Institute Inc.
  • 6.4.14 Datastax, Inc.
  • 6.4.15 MemVerge, Inc.
  • 6.4.16 Hewlett Packard Enterprise Company
  • 6.4.17 Fujitsu Limited
  • 6.4.18 KX Systems, Inc.
  • 6.4.19 Volt Active Data, Inc.
  • 6.4.20 Aerospike, Inc.
  • 6.4.21 Exasol AG

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-space and Unmet-Need Assessment