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市場調查報告書
商品編碼
1872360
向量資料庫解決方案:全球市場佔有率和排名、總收入和需求預測(2025-2031年)Vector Database Solution - Global Market Share and Ranking, Overall Sales and Demand Forecast 2025-2031 |
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2024 年全球向量資料庫解決方案市場規模估計為 38.92 億美元,預計到 2031 年將成長至 157.25 億美元,在 2025 年至 2031 年的預測期內,複合年成長率將達到 22.6%。
向量資料庫解決方案是資料庫系統。向量資料是指具有大小和方向的數據,例如幾何資料、地理空間資料和時間序列資料。傳統的關聯資料庫更適合處理純量數據,而向量資料庫專注於高效處理向量數據,並提供更快、更靈活的查詢和分析功能。
隨著人工智慧、影像識別、大規模語言模型(LLM)和建議系統等技術對高維向量資料的管理和搜尋效能提出了越來越高的要求,向量資料庫解決方案正迅速成為下一代資料基礎設施的關鍵組成部分。這些解決方案主要分為兩類:雲端基礎託管部署和本地私有部署。前者適用於中小型企業和個人開發者,具有彈性擴充性、訪問便利、快速迭代開發等優勢。後者則適用於對資料主權和隱私合規性要求嚴格的大型企業,提供更強大的效能最佳化和客製化整合。在應用層級,向量資料庫廣泛應用於自然語言搜尋、圖像和影片內容搜尋、個人化推薦、知識問答系統等場景,為個人使用者提供更自然的互動體驗,也為企業提供更智慧的資料搜尋和分析能力。
全球領先的向量資料庫解決方案供應商包括 Zilliz、Faiss 和 Redis,前三大公司佔據了全球約 40% 的市場佔有率。北美是最大的市場,約佔 37% 的市場佔有率。按產品類型分類,雲端基礎是最大的細分市場,約佔 65% 的市場佔有率。同時,企業是最大的下游供應商,約佔 80% 的市佔率。
隨著多模態人工智慧模型的日益普及,向量化資料的種類和數量將持續成長,向量資料庫也將從「加速引擎」演變為「認知基礎設施」。有鑑於此趨勢,資料庫廠商必須迅速應對以下挑戰:首先,建構支援GPU的異質運算框架,以高效索引和近似超過十億級的搜尋向量。其次,加強與RAG架構(例如大規模語言模型ChatGPT和Claude)的原生整合,使其成為人工智慧代理的記憶體中心。第三,從安全性、合規性和可解釋性的角度出發,支援存取控制清單(ACL)、審核日誌和端對端加密等功能,以滿足企業級部署需求。第四,推進標準化介面和生態系統整合,與主流資料湖和MLOps平台無縫對接,建構通用知識搜尋基礎設施。
簡而言之,向量資料庫正從“AI搜尋工具”演變為“智慧資料系統的核心樞紐”,其未來發展與巨量資料生態系統的演進路徑息息相關。唯有持續投入並突破底層索引技術、異質運算最佳化以及雲端原生架構創新,製造商才能真正脫穎而出,在未來的智慧資料基礎架構中搶佔主導。
本報告旨在透過按地區/國家、類型和應用進行分析,全面概述全球向量資料庫解決方案市場,重點關注總收入、市場佔有率和主要企業的排名。
向量資料庫解決方案的市場規模、估算和預測均以銷售收入為指標,以 2024 年為基準年,並包含 2020 年至 2031 年的歷史資料和預測資料。定量和定性分析將幫助讀者制定業務和成長策略,評估市場競爭格局,分析公司在當前市場中的地位,並就向量資料庫解決方案做出明智的商業決策。
市場區隔
公司
按類型分類的細分市場
應用領域
按地區
The global market for Vector Database Solution was estimated to be worth US$ 3892 million in 2024 and is forecast to a readjusted size of US$ 15725 million by 2031 with a CAGR of 22.6% during the forecast period 2025-2031.
Vector Database Solution is a database system designed and optimized to process and store vector data. Vector data refers to data with magnitude and direction, such as geometric data, geospatial data, time series data, etc. Traditional relational databases are usually more suitable for processing scalar data, while vector databases focus on efficiently processing vector data, providing faster and more flexible query and analysis capabilities.
As technologies such as artificial intelligence, image recognition, large language models (LLMs) and recommendation systems place higher and higher demands on high-dimensional vector data management and search performance, vector database solutions are rapidly becoming a key component of the next-generation data infrastructure. This type of solution is mainly divided into two categories: cloud-based hosting deployment and local private deployment: the former is suitable for small and medium-sized enterprises or individual developers, with the advantages of elastic expansion, convenient access and rapid iteration, while the latter is more suitable for large enterprises with strict requirements on data sovereignty and privacy compliance, and can achieve stronger performance optimization and customized integration. At the application level, vector databases are widely used in scenarios such as natural language retrieval, image and video content search, personalized recommendation, and knowledge question-and-answer systems, bringing a more natural interactive experience to individual users and providing enterprises with smarter data retrieval and analysis capabilities.
The core vendors of global vector database solutions include Zilliz, Faiss and Redis, and the top three vendors account for about 40% of the global market share. North America is the largest market, accounting for about 37% of the market share. In terms of product types, cloud-based is the largest segment, accounting for about 65% of the share. At the same time, in terms of downstream, enterprises are the largest downstream field, accounting for about 80% of the share.
In the future, with the widespread application of multimodal AI models, the type and volume of vectorized data will continue to grow, pushing vector databases from "acceleration engines" to "cognitive infrastructure". Under this trend, database vendors need to make early arrangements in the following aspects: First, build a GPU-friendly heterogeneous computing support framework to improve the efficiency of vector indexing and approximate search above one billion levels; second, strengthen native integration with RAG architectures such as large language models (such as ChatGPT and Claude) to become the memory center of AI Agent; third, around security, compliance and explainability, develop support for ACL, audit logs, end-to-end encryption and other functions to meet enterprise-level deployment requirements; fourth, promote standardized interfaces and ecological integration, seamlessly connect with mainstream data lakes and MLOps platforms, and form a general knowledge retrieval infrastructure.
In short, vector databases are evolving from "AI search tools" to "key hubs of intelligent data systems", and their future development will be deeply tied to the evolution path of the big model ecosystem. Only by continuously investing in breakthroughs in underlying indexing technology, heterogeneous computing optimization, and cloud-native architecture innovation can manufacturers truly stand out and seize the commanding heights of future intelligent data infrastructure.
This report aims to provide a comprehensive presentation of the global market for Vector Database Solution, focusing on the total sales revenue, key companies market share and ranking, together with an analysis of Vector Database Solution by region & country, by Type, and by Application.
The Vector Database Solution market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2024 as the base year, with history and forecast data for the period from 2020 to 2031. With both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Vector Database Solution.
Market Segmentation
By Company
Segment by Type
Segment by Application
By Region
Chapter Outline
Chapter 1: Introduces the report scope of the report, global total market size. This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.
Chapter 2: Detailed analysis of Vector Database Solution company competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 3: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 5: Revenue of Vector Database Solution in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.
Chapter 6: Revenue of Vector Database Solution in country level. It provides sigmate data by Type, and by Application for each country/region.
Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.
Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.
Chapter 9: Conclusion.