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市場調查報告書
商品編碼
1803618
以生成式人工智慧應用的向量資料庫市場(按資料庫類型、儲存資料類型、技術、部署模式和產業垂直分類)-2025 年至 2030 年全球預測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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預計生成式人工智慧應用的向量資料庫市場在 2024 年的價值將達到 6.3674 億美元,到 2025 年將成長到 7.5989 億美元,複合年成長率為 20.14%,到 2030 年將達到 19.1472 億美元。
主要市場統計數據 | |
---|---|
基準年2024年 | 6.3674億美元 |
預計2025年 | 7.5989億美元 |
預測年份 2030 | 19.1472億美元 |
複合年成長率(%) | 20.14% |
向量資料庫的出現標誌著生成式人工智慧和大規模語言模型實現演進的關鍵時刻,為處理由文字、圖像和音訊生成的高維向量提供了最佳化的基礎。隨著企業努力解鎖上下文搜尋、建議系統和即時個人化,向量儲存和相似性搜尋引擎已成為至關重要的技術推動者。透過將非結構化資料索引為向量,這些平台顯著加快了語意相關資訊的搜尋速度,從而提升了生成式架構的效能。
過去一年,嵌入式模型和硬體加速器的快速發展推動了資料索引、搜尋和生成式人工智慧系統服務方式的重大轉變。傳統的關係型和文件型儲存正逐漸被以向量為中心的架構所取代,這些架構利用最佳化的索引結構和近似最近鄰演算法。這項進步使企業能夠實現大規模亞毫秒的查詢回應,而這在高維度資料中是前所未有的。
2025 年美國關稅為部署大規模向量資料庫基礎設施的組織帶來了新的成本考量。包括 GPU 和專用加速器在內的高效能運算硬體的關稅上調,增加了本地解決方案的整體擁有成本。為此,一些公司正在加速雲端基礎遷移,以減輕進口關稅的影響,並利用與全球超大規模夥伴關係關係來獲取運算資源,而無需承擔關稅相關成本。
透過多種細分視角進行評估,可以洞察向量資料庫市場,揭示特定的技術偏好和企業需求。首先,從資料庫類型來看,企業必須在開放原始碼框架和專有解決方案之間做出選擇,並在客製化和供應商支援之間取得平衡。考慮到儲存資料類型的多樣性,一個互補的觀點浮現出來,揭示了一系列使用案例,從嵌入圖像進行視覺搜尋,到索引音訊和語音資訊進行轉錄和語音分析,以及傳統的文字語料庫。
向量資料庫格局呈現區域特徵,這些特徵由基礎設施的成熟度、監管環境和公司成熟度決定。在美洲,雲端運算的普及和對人工智慧研究的大力投入,使該地區成為領先科技公司和研究機構試點尖端向量服務的熱點。跨國合作進一步加速了創新,使先進的向量功能能夠快速整合到商業產品中。
在向量資料庫生態系統的前沿,湧現出一批技術領導企業,他們各自透過產品創新和策略聯盟建立了獨特的競爭優勢。一些供應商正在利用與機器學習框架的原生整合,簡化從嵌入生成到語義搜尋的工作流程。另一些供應商則透過在其平台中直接建立高級安全通訊協定和合規性認證來脫穎而出,從而吸引那些對資料管治要求嚴格的企業。
為了最大限度地提升 Vector資料庫投資的策略價值,產業領導者應採取分階段的方法,首先建立與業務目標相符的清晰績效和成本指標。鼓勵企業試用開放原始碼平台和專有平台,以評估靈活性、支援和整體擁有成本之間的權衡。同時,在早期評估階段納入嚴格的安全性和合規性評估,確保資料管治要求不會成為更大規模人工智慧舉措的阻礙。
本報告採用嚴謹的調查方法,將高階主管訪談中獲得的質性洞察與大量二手資料審查得出的量化分析相結合。一手研究包括與高級技術主管、資料架構師和 AI 從業人員的討論,以了解實際部署經驗和未來需求。二級資訊來源包括同行評審論文、開放原始碼計劃庫、供應商技術文件和行業白皮書,檢驗新興趨勢並評估基準效能聲明。
向量資料庫正迅速從實驗性工具轉變為可擴展生成式人工智慧基礎設施的基礎元件。相似性搜尋演算法、向量索引架構和硬體查詢處理的技術進步重新定義了非結構化資料的儲存、存取和使用方式。不斷變化的監管環境和不斷發展的部署模式迫使企業採用兼顧性能、成本和合規性的精細化策略。
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.