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市場調查報告書
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1851661

記憶體內：市場佔有率分析、行業趨勢、統計數據和成長預測（2025-2030 年）

In-Memory Database - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
出版日期: | 出版商: Mordor Intelligence | 英文 120 Pages | 商品交期: 2-3個工作天內

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簡介目錄

全球記憶體內市場預計到 2025 年將達到 70.8 億美元，到 2030 年將達到 136.2 億美元，預測期內複合年成長率為 13.98%。

記憶體資料庫 -Market-IMG1

雲端原生微服務、AI推理引擎和串流分析平台對亞毫秒效能的要求不斷推動企業向以記憶體為中心的架構轉型。 DRAM價格的下降和基於CXL的持久記憶體模組的出現降低了整體擁有成本，使更多工作負載從磁碟系統轉移出去。互聯汽車和工業IoT工廠的邊緣部署進一步刺激了需求，因為本地處理可以避免網路延遲帶來的損失。隨著傳統供應商與超大規模雲端的整合不斷加深，以及開放原始碼分支的蓬勃發展，競爭格局持續變化，為買家提供了避免被供應商鎖定的新途徑。

全球記憶體內市場趨勢與洞察

需要亞毫秒延遲的雲端原生微服務

雲端原生技術的普及重塑了效能基準，容器化微服務需要微秒的資料存取速度。會話儲存、個人化引擎和高頻交易平台已從基於磁碟的資料庫轉向以記憶體為中心的存儲，因為毫秒級的延遲會降低轉換率和交易利潤。 Dragonfly 在 AWS Graviton3E 晶片上實現了每秒 643 萬次的運算速度，凸顯了資料庫層效能的上限。從單體架構遷移到分散式系統的金融機構和數位商務企業已經發現，回應時間的提升轉化為實質的營收成長，凸顯了這個促進因素在短期內的重要性。

DRAM 和持久記憶體成本的下降擴大了總體擁有成本 (TCO) 差距。

儘管DDR4和DDR5記憶體模組的全球現貨價格持續下跌，三星的CXL混合記憶體模組原型產品卻以極具吸引力的成本展現了DRAM等級的延遲和持久性。超大規模營運商將記憶體跨機架共享，從而減少了閒置容量和備份週期。隨著記憶體與固態硬碟陣列之間的溢價逐漸縮小，尤其是在服務等級協定（SLA）視窗要求嚴格的分析工作負載方面，企業紛紛將藍圖轉向記憶體內部署。這種趨勢在亞太地區的製造地尤其明顯，這些工廠將大型歷史資料集遷移到記憶體中，用於即時數位雙胞胎分析。

由於專有格式而導致的供應商鎖定問題令人擔憂

2024 年 Redis 授權協議的變更促使 AWS、Google 和 Oracle 轉而支持 Linux 基金會旗下的 Valkey 分支。那些為多年資料庫計劃製定預算的公司不得不將終止成本考慮在內，並推遲了採購週期。為了降低風險，一些公司採用了多資料庫編配層，但這些抽象層帶來了延遲，部分抵消了記憶體速度的提升。

細分市場分析

到2024年，OLTP（線上事務處理）領域將佔據記憶體內市場45.3%的佔有率，凸顯了銀行、電子商務和ERP系統等高可靠性事務性工作負載的持續需求。由於關鍵任務記錄仍需符合ACID規範，且企業願意為亞毫秒的資料提交支付更高的效能溢價，因此市場需求依然強勁。 OLAP（線上分析處理）的應用主要針對成熟的商業智慧前端，但隨著分析轉向更靈活的引擎，其成長速度有所放緩。

受企業尋求單一平台帶來的便利性驅動，HTAP預計將在2025年至2030年間以21.1%的複合年成長率成長。 GridGain的平台在維持對ANSI SQL-99支援的同時，速度比基於磁碟的系統提升高達1000倍。即時風險計算和供應鏈孿生模型需要並發讀寫訪問，這使得HTAP成為首選架構。這種融合增加了先前營運和分析部門各自獨立的預算，推動記憶體內市場朝向統一設計方向發展。

到2024年，本地部署將佔總收入的55.4%，因為受監管行業需要完全託管的資料駐留和客製化的高可用性架構。即使公有雲日趨成熟，與本地資料庫緊密整合的傳統企業軟體堆疊仍將持續支撐支出。然而，隨著數位原民企業採用託管服務以避免基礎架構管理，雲端採用率仍在持續加速成長。

受聯網汽車和工業物聯網閘道器的推動，邊緣運算和嵌入式設備的普及預計將以23.2%的複合年成長率成長。現代汽車每年產生約300TB的數據，需要車載處理能力來實現自動駕駛功能。 TDengine針對智慧汽車遙測資料實現了比Elasticsearch高10倍的壓縮，從而降低了上行傳輸頻寬。製造商正在將類似的策略應用於生產線，以實現故障的即時檢測。這種轉變表明，曾經僅限於資料中心的效能提升如今在邊緣運算中也至關重要，從而擴大了記憶體內市場的規模。

記憶體內市場按處理類型（OLTP、OLAP、HTAP）、部署模式（本地部署、其他）、資料模型（SQL、NoSQL、多模型）、組織規模（中小企業、大型企業）、應用程式（即時事務處理、其他）、最終用戶垂直行業（銀行、金融服務和保險、南美市場、IT和通訊、其他地區（北美、電信、歐洲）以及地區（北美電信、歐洲市場細分市場。

區域分析

預計到2024年，亞太地區將實現32.2%的最大營收成長，並維持17.1%的複合年成長率。中國、日本和印度的國家工業4.0計畫推動了工廠自動化，這需要記憶體內歷史資料庫來實現亞秒的MES回饋迴路。通用汽車在其MES 4.0部署中連接了超過10萬種營運技術，展示了邊緣部署的規模。像Nautilus Technologies這樣的本土供應商已經開發了自己的關係引擎，從而降低了對海外智慧財產權的依賴。

在北美，一個成熟又充滿創新精神的市場正在崛起，Oracle核心領域包括金融服務、超大規模雲端運算和自動駕駛汽車研發。 Oracle 和 Google 深化了合作，在 Google Cloud 上原生運作Oracle資料庫服務，並將企業級 SQL 功能與人工智慧加速器結合。該地區的創業融資支持了 Dragonfly 等新興企業，進一步加劇了競爭格局。

在歐洲，遵守 GDPR 的資料主權法規已成為重中之重，推動了混合雲端的普及，並促使企業傾向於將本地叢集與本地資料中心的託管服務相結合。為了滿足歐盟的居住要求， Oracle已將其 Database@Azure 產品擴展到更多歐盟地區。此外，在嚴格的隱私框架下，歐洲醫療保健領域也開始採用 HTAP資料庫來支援人工智慧診斷。

在中東和非洲，智慧城市對光纖和5G骨幹網路的投資推動了工業物聯網（IIoT）試驗，而即時分析的需求日益成長。在南美洲，採礦業和數位銀行引領了這一趨勢，低延遲詐騙偵測使得高階記憶體資料庫系統成為必要。儘管這兩個地區的絕對支出仍然不高，但這兩位數的成長擴大了全球記憶體內市場的多元化。

其他福利：

  • Excel格式的市場預測（ME）表
  • 3個月的分析師支持

目錄

第1章 引言

  • 研究假設和市場定義
  • 調查範圍

第2章調查方法

第3章執行摘要

第4章 市場情勢

  • 市場概覽
  • 市場促進因素
    • 需要亞毫秒延遲的雲端原生微服務
    • DRAM 和持久記憶體每 GB 價格的下降拉大了其與磁碟的總擁有成本差距。
    • 在銀行、金融服務和電訊採用流分析技術進行詐欺偵測和網路服務品質最佳化
    • 用於加速醫療保健領域 AI/ML 模型服務的 HTAP 架構
    • 邊緣運算用例（聯網汽車、工業物聯網）需要嵌入式IMDB
  • 市場限制
    • 由於專有記憶體內格式而導致的供應商鎖定問題令人擔憂
    • 40TB以上叢集高可用性設計的複雜性
    • 限制全球複製的資料主權法律（例如，中國的《資料安全法》、歐盟的《一般資料保護條例》）
  • 價值鏈分析
  • 監管或技術環境
  • 波特五力分析
    • 新進入者的威脅
    • 買方/消費者的議價能力
    • 供應商的議價能力
    • 替代品的威脅
    • 競爭對手之間的競爭
  • 宏觀經濟因素如何影響市場

第5章 市場規模與成長預測

  • 按處理類型
    • OLTP
    • OLAP
    • 混合事務/分析處理（HTAP）
  • 透過部署模式
    • 本地部署
    • 雲
    • 邊緣/嵌入式
  • 按資料模型
    • 關係型（SQL）
    • NoSQL（鍵值、文件、圖）
    • 多重模型
  • 按組織規模
    • 小型企業
    • 主要企業
  • 透過使用
    • 即時交易處理
    • 營運分析和商業智慧儀錶板
    • AI/ML模型服務
    • 快取和會話存儲
  • 按最終用戶行業分類
    • BFSI
    • 資訊科技/通訊
    • 零售與電子商務
    • 醫療保健與生命科學
    • 製造和工業IoT
    • 媒體與娛樂
    • 政府/國防
    • 其他（能源、教育等）
  • 按地區
    • 北美洲
      • 美國
      • 加拿大
      • 墨西哥
    • 歐洲
      • 德國
      • 法國
      • 英國
      • 北歐國家
      • 其他歐洲地區
    • 亞太地區
      • 中國
      • 台灣
      • 韓國
      • 日本
      • 印度
      • 亞太其他地區
    • 南美洲
      • 巴西
      • 墨西哥
      • 阿根廷
      • 其他南美洲國家
    • 中東和非洲
      • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 土耳其
      • 其他中東地區
      • 非洲
      • 南非
      • 其他非洲地區

第6章 競爭情勢

  • 市場集中度
  • 策略趨勢
  • 市佔率分析
  • 公司簡介
    • SAP SE
    • Oracle Corp.
    • Microsoft Corp.
    • IBM Corp.
    • Redis Ltd.（Redis Enterprise）
    • Aerospike Inc.
    • VoltDB Inc.
    • Couchbase Inc.
    • DataStax Inc.
    • Hazelcast Inc.
    • MemVerge Inc.
    • Altibase Corp.
    • GridGain Systems Inc.
    • Raima Inc.
    • McObject LLC
    • Pivotal（VMware Tanzu GemFire）
    • Amazon Web Services（Amazon ElastiCache & MemoryDB）
    • Google Cloud（AlloyDB, Memorystore）
    • Alibaba Cloud（ApsaraDB Tair）
    • Huawei Cloud（GaussDB IM）
    • Tencent Cloud（Tendis）

第7章 市場機會與未來展望

簡介目錄
Product Code: 62385

The global In-Memory Database market size stood at USD 7.08 billion in 2025 and is expected to reach USD 13.62 billion by 2030, advancing at a 13.98% CAGR over the forecast period.

In-Memory Database - Market - IMG1

Sub-millisecond performance requirements from cloud-native microservices, AI inference engines, and streaming analytics platforms continued to push enterprises toward memory-centric architectures. Lower DRAM prices and the arrival of CXL-based persistent memory modules have reduced the total cost of ownership, encouraging more workloads to migrate from disk-backed systems. Edge deployments in connected vehicles and Industrial IoT plants further expanded demand because local processing avoids network latency penalties. Competitive dynamics remained fluid as traditional vendors deepened integrations with hyperscale clouds while open-source forks gained momentum, giving buyers new paths to avoid vendor lock-in.

Global In-Memory Database Market Trends and Insights

Cloud-Native Microservices Demanding Sub-Millisecond Latency

Cloud-native adoption reshaped performance baselines as containerized microservices needed data access in microseconds. Session stores, personalization engines, and high-frequency trading platforms shifted from disk-backed databases to memory-centric stores because every millisecond of delay reduced conversion rates or trading profit. Dragonfly demonstrated 6.43 million operations per second on AWS Graviton3E silicon, highlighting the ceiling now expected from database tiers. Financial institutions and digital commerce operators that migrated monoliths to distributed systems saw response-time improvements translate into tangible revenue gains, reinforcing the driver's near-term importance.

Falling DRAM and Persistent Memory Costs Widening TCO Gap

Global spot pricing of DDR4 and DDR5 modules continued to slide, while Samsung's CXL Memory Module Hybrid prototype showed DRAM-class latency with persistence, creating a compelling cost profile. Hyperscale operators pooled memory across racks, reducing stranded capacity and backup cycles. Enterprises pivoted roadmaps toward in-memory deployment because the premium over SSD arrays narrowed, especially for analytics workloads with tight SLA windows. The effect is visible in Asia-Pacific manufacturing hubs where large historian datasets are moved into memory for real-time digital-twin analytics.

Vendor Lock-in Concerns Around Proprietary Formats

Redis's license change in 2024 heightened buyer wariness of proprietary formats, spurring AWS, Google, and Oracle to back the Valkey fork under the Linux Foundation. Enterprises budgeting multi-year database projects factored in exit costs, slowing purchase cycles. To mitigate risk, some adopted multi-database orchestration layers, but those abstractions introduced latency penalties that partially offset memory-speed gains.

Other drivers and restraints analyzed in the detailed report include:

  1. Streaming Analytics Adoption in BFSI and Telecom
  2. HTAP Architectures Accelerating AI/ML Model Serving
  3. High-Availability Design Complexity for Large Clusters

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

Segment Analysis

The OLTP segment held 45.3% of the In-Memory Database market share in 2024, underscoring continued reliance on high-integrity transactional workloads across banking, e-commerce, and ERP systems. Demand persisted because mission-critical records still required ACID compliance, with enterprises paying a performance premium for sub-millisecond commits. OLAP deployments addressed established business-intelligence front ends but grew slowly as analytics shifted toward more flexible engines.

HTAP climbed with a 21.1% CAGR forecast from 2025 to 2030 as firms sought single-platform simplicity. GridGain's platform showed up to 1,000X speed-ups over disk-based systems while retaining ANSI SQL-99 support. Real-time risk calculations and supply-chain twins needed simultaneous read-write access, making HTAP the preferred architecture. The convergence unlocked incremental budget from departments earlier siloed between operations and analytics, pushing the In-Memory Database market toward unified designs.

On-premise installations captured 55.4% of 2024 revenue because regulated sectors required full control over data residency and tailored HA architectures. Legacy enterprise software stacks tightly integrated with on-premise databases, anchoring spending even as public clouds mature. Cloud deployments, nonetheless, have advanced as digital-native firms adopted managed services to avoid infrastructure administration.

Edge and embedded deployments displayed a 23.2% CAGR outlook, fueled by connected cars and IIoT gateways. Modern vehicles generate around 300 TB annually, which demands in-vehicle processing for autonomous features. TDengine achieved 10X compression over Elasticsearch in smart-vehicle telemetry, cutting bandwidth for upstream transfers. Manufacturers applied similar strategies on production lines to detect defects instantly. The shift signaled that performance gains once reserved for data centers were now indispensable at the edge, expanding the In-Memory Database market footprint.

In-Memory Database Market is Segmented by Processing Type (OLTP, OLAP, and HTAP), Deployment Mode (On-Premise, and More), Data Model (SQL, Nosql, and Multi-Model), Organization Size (SMEs, and Large Enterprises), Application (Real-Time Transaction Processing, and More), End-User Industry (BFSI, Telecommunications and IT, and More), and Geography (North America, Europe, Asia-Pacific, South America, and Middle East and Africa).

Geography Analysis

Asia-Pacific recorded the largest regional revenue at 32.2% in 2024 and maintained a 17.1% CAGR outlook. National Industry 4.0 programs in China, Japan, and India spurred factory automation that required in-memory historian databases for sub-second MES feedback loops. General Motors linked more than 100,000 operational technology connections in its MES 4.0 rollout, illustrating the scale of edge deployments. Local vendors such as Nautilus Technologies' advanced indigenous relational engines, reducing reliance on foreign IP.

North America formed a mature but innovation-rich market centered on financial services, hyperscale clouds, and autonomous-vehicle R&D. Oracle and Google deepened their partnership to run Oracle Database services natively on Google Cloud, marrying enterprise SQL capabilities with AI accelerators. The region's venture funding supported emerging players such as Dragonfly, intensifying competitive churn.

Europe prioritized data-sovereignty compliance under GDPR, driving hybrid cloud adoption and favoring on-premise clusters combined with managed services in local data centers. Oracle expanded Database@Azure coverage to additional EU regions to satisfy residency rules. The continent also saw healthcare deployments of HTAP databases to power AI diagnostics under strict privacy frameworks.

The Middle East and Africa invested in smart-city fiber and 5G backbones, leading to pilot IIoT deployments that require real-time analytics. South America gained traction in mining operations and digital banking, where low-latency fraud detection justified premium memory-centric systems. Though absolute spend in these two regions remained modest, double-digit growth expanded the In-Memory Database market's global diversity.

  1. SAP SE
  2. Oracle Corp.
  3. Microsoft Corp.
  4. IBM Corp.
  5. Redis Ltd. (Redis Enterprise)
  6. Aerospike Inc.
  7. VoltDB Inc.
  8. Couchbase Inc.
  9. DataStax Inc.
  10. Hazelcast Inc.
  11. MemVerge Inc.
  12. Altibase Corp.
  13. GridGain Systems Inc.
  14. Raima Inc.
  15. McObject LLC
  16. Pivotal (VMware Tanzu GemFire)
  17. Amazon Web Services (Amazon ElastiCache & MemoryDB)
  18. Google Cloud (AlloyDB, Memorystore)
  19. Alibaba Cloud (ApsaraDB Tair)
  20. Huawei Cloud (GaussDB IM)
  21. Tencent Cloud (Tendis)

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 Cloud-native micro-services demanding sub-millisecond latency
    • 4.2.2 Falling DRAM and persistent-memory USD/GB widening TCO gap vs. disk
    • 4.2.3 Streaming analytics adoption in BFSI and telecom for fraud and network QoS
    • 4.2.4 HTAP architectures accelerating AI/ML model-serving in healthcare
    • 4.2.5 Edge-compute use-cases (connected vehicles, IIoT) requiring embedded IMDB
  • 4.3 Market Restraints
    • 4.3.1 Vendor lock-in concerns around proprietary in-memory formats
    • 4.3.2 High-availability design complexity for >40 TB clusters
    • 4.3.3 Data-sovereignty laws (e.g., China CSL, EU GDPR) limiting global replication
  • 4.4 Value Chain Analysis
  • 4.5 Regulatory or Technological Outlook
  • 4.6 Porter's Five Forces Analysis
    • 4.6.1 Threat of New Entrants
    • 4.6.2 Bargaining Power of Buyers/Consumers
    • 4.6.3 Bargaining Power of Suppliers
    • 4.6.4 Threat of Substitute Products
    • 4.6.5 Intensity of Competitive Rivalry
  • 4.7 Impact of macroeconomoic factors on the market

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

  • 5.1 By Processing Type
    • 5.1.1 OLTP
    • 5.1.2 OLAP
    • 5.1.3 Hybrid Transactional/Analytical Processing (HTAP)
  • 5.2 By Deployment Mode
    • 5.2.1 On-premise
    • 5.2.2 Cloud
    • 5.2.3 Edge/Embedded
  • 5.3 By Data Model
    • 5.3.1 Relational (SQL)
    • 5.3.2 NoSQL (Key-Value, Document, Graph)
    • 5.3.3 Multi-model
  • 5.4 By Organization Size
    • 5.4.1 Small and Medium Enterprises (SMEs)
    • 5.4.2 Large Enterprises
  • 5.5 By Application
    • 5.5.1 Real-time Transaction Processing
    • 5.5.2 Operational Analytics and BI Dashboards
    • 5.5.3 AI/ML Model Serving
    • 5.5.4 Caching and Session Stores
  • 5.6 By End-user Industry
    • 5.6.1 BFSI
    • 5.6.2 Telecommunications and IT
    • 5.6.3 Retail and E-commerce
    • 5.6.4 Healthcare and Life Sciences
    • 5.6.5 Manufacturing and Industrial IoT
    • 5.6.6 Media and Entertainment
    • 5.6.7 Government and Defense
    • 5.6.8 Others (Energy, Education, etc.)
  • 5.7 By Geography
    • 5.7.1 North America
      • 5.7.1.1 United States
      • 5.7.1.2 Canada
      • 5.7.1.3 Mexico
    • 5.7.2 Europe
      • 5.7.2.1 Germany
      • 5.7.2.2 France
      • 5.7.2.3 United Kingdom
      • 5.7.2.4 Nordics
      • 5.7.2.5 Rest of Europe
    • 5.7.3 Asia-Pacific
      • 5.7.3.1 China
      • 5.7.3.2 Taiwan
      • 5.7.3.3 South Korea
      • 5.7.3.4 Japan
      • 5.7.3.5 India
      • 5.7.3.6 Rest of Asia-Pacific
    • 5.7.4 South America
      • 5.7.4.1 Brazil
      • 5.7.4.2 Mexico
      • 5.7.4.3 Argentina
      • 5.7.4.4 Rest of South America
    • 5.7.5 Middle East and Africa
      • 5.7.5.1 Middle East
      • 5.7.5.1.1 Saudi Arabia
      • 5.7.5.1.2 United Arab Emirates
      • 5.7.5.1.3 Turkey
      • 5.7.5.1.4 Rest of Middle East
      • 5.7.5.2 Africa
      • 5.7.5.2.1 South Africa
      • 5.7.5.2.2 Rest of 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 for key companies, Products and Services, and Recent Developments)
    • 6.4.1 SAP SE
    • 6.4.2 Oracle Corp.
    • 6.4.3 Microsoft Corp.
    • 6.4.4 IBM Corp.
    • 6.4.5 Redis Ltd. (Redis Enterprise)
    • 6.4.6 Aerospike Inc.
    • 6.4.7 VoltDB Inc.
    • 6.4.8 Couchbase Inc.
    • 6.4.9 DataStax Inc.
    • 6.4.10 Hazelcast Inc.
    • 6.4.11 MemVerge Inc.
    • 6.4.12 Altibase Corp.
    • 6.4.13 GridGain Systems Inc.
    • 6.4.14 Raima Inc.
    • 6.4.15 McObject LLC
    • 6.4.16 Pivotal (VMware Tanzu GemFire)
    • 6.4.17 Amazon Web Services (Amazon ElastiCache & MemoryDB)
    • 6.4.18 Google Cloud (AlloyDB, Memorystore)
    • 6.4.19 Alibaba Cloud (ApsaraDB Tair)
    • 6.4.20 Huawei Cloud (GaussDB IM)
    • 6.4.21 Tencent Cloud (Tendis)

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

  • 7.1 White-space and Unmet-Need Assessment