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市场调查报告书
商品编码
1803618
以生成式人工智慧应用的向量资料库市场(按资料库类型、储存资料类型、技术、部署模式和产业垂直划分)-2025 年至 2030 年全球预测Vector Databases for Generative AI Applications Market by Database Type, Data Type Stored, Technique, Deployment Mode, Industry - Global Forecast 2025-2030 |
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预计生成式人工智慧应用的向量资料库市场在 2024 年的价值将达到 6.3674 亿美元,到 2025 年将成长到 7.5989 亿美元,复合年增长率为 20.14%,到 2030 年将达到 19.1472 亿美元。
主要市场统计数据 | |
---|---|
基准年2024年 | 6.3674亿美元 |
预计2025年 | 7.5989亿美元 |
预测年份 2030 | 19.1472亿美元 |
复合年增长率(%) | 20.14% |
向量资料库的出现标誌着生成式人工智慧和大规模语言模型实现演进的关键时刻,为处理由文字、图像和音讯生成的高维向量提供了最佳化的基础。随着企业努力解锁上下文搜寻、建议系统和即时个人化,向量储存和相似性搜寻引擎已成为至关重要的技术推动者。透过将非结构化资料索引为向量,这些平台显着加快了语意相关资讯的搜寻速度,从而提升了生成式架构的效能。
过去一年,嵌入式模型和硬体加速器的快速发展推动了资料索引、搜寻和生成式人工智慧系统服务方式的重大转变。传统的关係型和文件型储存正逐渐被以向量为中心的架构所取代,这些架构利用最佳化的索引结构和近似最近邻演算法。这项进步使企业能够实现大规模亚毫秒的查询回应,而这在高维度资料中是前所未有的。
2025 年美国关税为部署大规模向量资料库基础设施的组织带来了新的成本考量。包括 GPU 和专用加速器在内的高效能运算硬体的关税上调,增加了本地解决方案的整体拥有成本。为此,一些公司正在加速云端基础迁移,以减轻进口关税的影响,并利用与全球超大规模伙伴关係关係来获取运算资源,而无需承担关税相关成本。
透过多种细分视角进行评估,可以洞察向量资料库市场,揭示特定的技术偏好和企业需求。首先,从资料库类型来看,企业必须在开放原始码框架和专有解决方案之间做出选择,并在客製化和供应商支援之间取得平衡。考虑到储存资料类型的多样性,一个互补的观点浮现出来,揭示了一系列使用案例,从嵌入图像进行视觉搜寻,到索引音讯和语音资讯进行转录和语音分析,以及传统的文字语料库。
向量资料库格局呈现区域特征,这些特征由基础设施的成熟度、监管环境和公司成熟度决定。在美洲,云端运算的普及和对人工智慧研究的大力投入,使该地区成为领先科技公司和研究机构试点尖端向量服务的热点。跨国合作进一步加速了创新,使先进的向量功能能够快速整合到商业产品中。
在向量资料库生态系统的前沿,涌现出一批技术领导企业,他们各自透过产品创新和策略联盟建立了独特的竞争优势。一些供应商正在利用与机器学习框架的原生集成,简化从嵌入生成到语义搜寻的工作流程。另一些供应商则透过在其平台中直接建立高级安全通讯协定和合规性认证来脱颖而出,从而吸引那些对资料管治要求严格的企业。
为了最大限度地提升 Vector资料库投资的策略价值,产业领导者应采取分阶段的方法,首先建立与业务目标相符的清晰绩效和成本指标。鼓励企业试用开放原始码平台和专有平台,以评估灵活性、支援和整体拥有成本之间的权衡。同时,在早期评估阶段纳入严格的安全性和合规性评估,确保资料管治要求不会成为更大规模人工智慧倡议的阻碍。
本报告采用严谨的调查方法,将高阶主管访谈中获得的质性洞察与大量二手资料审查得出的量化分析相结合。一手研究包括与高级技术主管、资料架构师和 AI 从业人员的讨论,以了解实际部署经验和未来需求。二级资讯来源包括同行评审论文、开放原始码计划库、供应商技术文件和行业白皮书,检验新兴趋势并评估基准效能声明。
向量资料库正迅速从实验性工具转变为可扩展生成式人工智慧基础设施的基础元件。相似性搜寻演算法、向量索引架构和硬体查询处理的技术进步重新定义了非结构化资料的储存、存取和使用方式。不断变化的监管环境和不断发展的部署模式迫使企业采用兼顾性能、成本和合规性的精细化策略。
The Vector Databases for Generative AI Applications Market was valued at USD 636.74 million in 2024 and is projected to grow to USD 759.89 million in 2025, with a CAGR of 20.14%, reaching USD 1,914.72 million by 2030.
KEY MARKET STATISTICS | |
---|---|
Base Year [2024] | USD 636.74 million |
Estimated Year [2025] | USD 759.89 million |
Forecast Year [2030] | USD 1,914.72 million |
CAGR (%) | 20.14% |
The advent of vector databases marks a pivotal moment in the evolution of generative AI and large language model implementations, offering an optimized foundation for handling high-dimensional embeddings generated from text, images, and audio. As enterprises strive to unlock contextual search, recommendation systems, and real-time personalization, vector storage and similarity search engines have become indispensable technological enablers. By indexing unstructured data as vectors, these platforms dramatically accelerate retrieval of semantically relevant information, thereby enhancing the performance of generative architectures.
In response, technology leaders are investing in scalable vector infrastructures that seamlessly integrate with existing data ecosystems and advanced compute resources. This strategic transition is driven by the need to reduce latency in inference requests and support the ever-growing complexity of multimodal AI workloads. Looking ahead, organizations that embrace vector database solutions will be ideally positioned to harness the next wave of AI innovation, turning raw data into intelligent, context-aware experiences.
Over the past year, rapid advancements in embedding models and hardware accelerators have converged to create a seismic shift in how data is indexed, searched, and served to generative AI systems. Traditional relational and document stores are increasingly giving way to vector-centric architectures that exploit optimized index structures and approximate nearest neighbor algorithms. This progression has enabled organizations to achieve sub-millisecond query responses at scale, a level of performance previously unattainable for high-dimensional data.
Concurrently, the open source community and proprietary vendors are introducing hybrid offerings that combine vector indexing, storage, and query orchestration within unified platforms. Integrations with orchestration frameworks and container ecosystems have further simplified deployment across cloud and on-premise environments, facilitating experimentation and production rollouts. As these forces coalesce, we are witnessing a profound transformation of the data management paradigm, elevating vector databases from niche tools to core pillars of modern AI stacks.
The tariff measures implemented by the United States in 2025 have introduced new cost considerations for organizations deploying vector database infrastructures at scale. Increased duties on high-performance compute hardware, including GPUs and specialized accelerators, have elevated the total cost of ownership for on-premise solutions. In response, some enterprises have accelerated their shift toward cloud-based deployments to mitigate the impact of import levies, leveraging global hyperscaler partnerships to access compute resources without the burden of tariff-related expenses.
At the same time, hardware and software vendors have adjusted supply chain strategies, forging regional alliances and establishing localized manufacturing hubs to navigate import restrictions. This dynamic has fostered greater resilience across vendor ecosystems while prompting end users to reevaluate deployment models. Ultimately, the tariff environment has catalyzed a broader discussion around total economic cost, driving deeper collaboration between procurement, finance, and IT teams when selecting vector database platforms.
Insight into the vector database market emerges when evaluated through multiple segmentation lenses that reveal specific technology preferences and enterprise requirements. First, when viewed by database type, organizations must decide between open source frameworks and proprietary solutions, balancing customization with vendor support commitments. A complementary perspective arises by examining the variety of data types stored, where use cases range from embedding images for visual search to indexing speech and audio information for transcription and call analytics alongside traditional text corpora.
Further clarity is achieved by segmenting according to core techniques: similarity search drives real-time recommendation engines, vector indexing structures facilitate rapid neighbor queries, and vector storage solutions ensure persistence and efficient retrieval of large-scale embedding collections. Deployment mode also plays a critical role, with cloud platforms offering elastic scale and global reach, contrasted by on-premise environments that address security, latency, and compliance mandates. Finally, industry focus delineates distinct value propositions, from advancing autonomous systems in automotive to strengthening fraud detection in banking, financial services, and insurance-covering asset managers, banks, and insurance firms-and extending into healthcare diagnostics, telecommunications and IT innovation, manufacturing optimization, and dynamic retail experiences.
The vector database landscape exhibits distinct regional characteristics shaped by infrastructure readiness, regulatory frameworks, and enterprise maturity levels. In the Americas, widespread cloud adoption and robust investment in AI research have positioned the region as a hotbed for piloting cutting-edge vector services, particularly among leading technology corporations and research institutions. Cross-border collaboration further accelerates innovation, enabling rapid integration of advanced vector capabilities into commercial products.
Europe, the Middle East, and Africa present a diverse tapestry of adoption scenarios, where stringent data protection regulations coexist with aggressive national AI initiatives. This confluence drives demand for on-premise or hybrid deployments that satisfy privacy mandates while supporting high-performance similarity search applications across sectors such as automotive engineering and healthcare imaging. In the Asia-Pacific region, expanding digital transformation investments, coupled with government-sponsored AI modernization programs, are fueling exponential growth in vector database deployments. Regional vendors and local research labs are collaborating to deliver tailored solutions for e-commerce personalization, financial analytics, and smart city infrastructures.
A cohort of technology leaders is emerging at the forefront of the vector database ecosystem, each carving out unique competitive differentiators through product innovation and strategic partnerships. Several vendors are capitalizing on native integrations with machine learning frameworks to streamline the workflow from embedding generation to semantic retrieval. Others are differentiating by embedding advanced security protocols and compliance certifications directly into their platforms, appealing to enterprises with rigorous data governance requirements.
Collaborations with hardware manufacturers and cloud providers are amplifying the performance profiles of vector indexes, resulting in purpose-built appliances and optimized managed services. Meanwhile, alliance networks are enabling rapid go-to-market strategies, with some companies co-developing tailored solutions for specialized industries such as healthcare imaging analytics and real-time retail recommendations. These strategic moves underscore the dynamic competitive landscape and the critical role of cross-sector collaboration in scaling generative AI use cases globally.
To maximize the strategic value of vector database investments, industry leaders should adopt a phased approach that begins with establishing clear performance and cost metrics aligned with business objectives. Organizations are advised to pilot both open source and proprietary platforms to assess trade-offs in flexibility, support, and total cost of ownership. Concurrently, integrating rigorous security and compliance assessments into early evaluation stages ensures that data governance requirements do not impede larger AI initiatives.
As deployments scale, fostering strong collaboration between data science, IT operations, and business stakeholders becomes essential. This cross-functional alignment enables continuous performance benchmarking and iterative refinement of vector index architectures. In parallel, investing in skill development and upskilling programs helps teams master emerging tools and best practices. Finally, cultivating strategic partnerships with platform vendors and hardware providers can accelerate innovation cycles, enabling organizations to stay ahead of evolving generative AI demands.
This report leverages a rigorous research methodology that blends qualitative insights from executive interviews with quantitative analysis derived from extensive secondary data review. Primary research involved discussions with senior technology officers, data architects, and AI practitioners to capture real-world deployment experiences and future requirements. Secondary sources included peer-reviewed papers, open source project repositories, vendor technical documentation, and industry whitepapers to validate emerging trends and benchmark performance claims.
Data triangulation methods were applied to cross-verify findings, ensuring both depth and reliability in the analysis. In addition, expert advisory panels provided continuous feedback on evolving vector indexing techniques, deployment patterns, and regulatory developments. This comprehensive framework underpins the credibility of the insights presented, delivering actionable intelligence tailored for decision-makers navigating the complex vector database landscape.
Vector databases have swiftly transitioned from experimental tools to foundational components of scalable generative AI infrastructures. The technological advancements in similarity search algorithms, vector indexing architectures, and hardware-accelerated query processing have redefined how unstructured data is stored, accessed, and utilized. Amid shifting regulatory landscapes and evolving deployment models, organizations are compelled to adopt nuanced strategies that balance performance, cost, and compliance considerations.
By examining segmentation criteria-from database type and data modality to deployment mode and industry vertical-decision-makers gain clarity on solution fit and value delivery. Regional dynamics further highlight how infrastructure maturity and regulatory frameworks shape adoption patterns, while corporate strategy insights underscore the importance of strategic alliances and continuous innovation. Ultimately, embracing these insights equips leaders to harness vector database technologies as a catalyst for generative AI success and sustainable competitive advantage.