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市场调查报告书
商品编码
1872360
向量资料库解决方案:全球市场份额和排名、总收入和需求预测(2025-2031年)Vector Database Solution - Global Market Share and Ranking, Overall Sales and Demand Forecast 2025-2031 |
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2024 年全球向量资料库解决方案市场规模估计为 38.92 亿美元,预计到 2031 年将成长至 157.25 亿美元,在 2025 年至 2031 年的预测期内,复合年增长率将达到 22.6%。
向量资料库解决方案是资料库系统。向量资料是指具有大小和方向的数据,例如几何资料、地理空间资料和时间序列资料。传统的关联资料库更适合处理纯量数据,而向量资料库专注于高效处理向量数据,并提供更快、更灵活的查询和分析功能。
随着人工智慧、影像识别、大规模语言模型(LLM)和建议系统等技术对高维向量资料的管理和搜寻效能提出了越来越高的要求,向量资料库解决方案正迅速成为下一代资料基础设施的关键组成部分。这些解决方案主要分为两类:云端基础託管部署和本地私有部署。前者适用于中小型企业和个人开发者,具有弹性扩充性、访问便利、快速迭代开发等优势。后者则适用于对资料主权和隐私合规性要求严格的大型企业,提供更强大的效能优化和客製化整合。在应用层级,向量资料库广泛应用于自然语言搜寻、图像和影片内容搜寻、个人化推荐、知识问答系统等场景,为个人使用者提供更自然的互动体验,也为企业提供更智慧的资料搜寻和分析能力。
全球领先的向量资料库解决方案供应商包括 Zilliz、Faiss 和 Redis,前三大公司占据了全球约 40% 的市场份额。北美是最大的市场,约占 37% 的市场份额。按产品类型划分,云端基础是最大的细分市场,约占 65% 的市场份额。同时,企业是最大的下游供应商,约占 80% 的市占率。
随着多模态人工智慧模型的日益普及,向量化资料的种类和数量将持续增长,向量资料库也将从「加速引擎」演变为「认知基础设施」。有鑑于此趋势,资料库厂商必须迅速应对以下挑战:首先,建构支援GPU的异质运算框架,以高效索引和近似超过十亿级的搜寻向量。其次,加强与RAG架构(例如大规模语言模型ChatGPT和Claude)的原生集成,使其成为人工智慧代理的记忆体中心。第三,从安全性、合规性和可解释性的角度出发,支援存取控制清单(ACL)、审核日誌和端对端加密等功能,以满足企业级部署需求。第四,推进标准化介面和生态系统集成,与主流资料湖和MLOps平台无缝对接,建构通用知识搜寻基础设施。
简而言之,向量资料库正从“AI搜寻工具”演变为“智慧资料系统的核心枢纽”,其未来发展与大数据生态系统的演进路径息息相关。唯有持续投入并突破底层索引技术、异质运算优化以及云端原生架构创新,製造商才能真正脱颖而出,在未来的智慧资料基础架构中抢占主导。
本报告旨在透过按地区/国家、类型和应用进行分析,全面概述全球向量资料库解决方案市场,重点关注总收入、市场份额和主要企业的排名。
向量资料库解决方案的市场规模、估算和预测均以销售收入为指标,以 2024 年为基准年,并包含 2020 年至 2031 年的历史资料和预测资料。定量和定性分析将帮助读者制定业务和成长策略,评估市场竞争格局,分析公司在当前市场中的地位,并就向量资料库解决方案做出明智的商业决策。
市场区隔
公司
按类型分類的细分市场
应用领域
按地区
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.