Vector Search as a Service Global Market Report 2025

The vector search as a service size market has grown exponentially in recent years. It will grow from $1.53 billion in 2024 to $1.98 billion in 2025 at a compound annual growth rate (CAGR) of 29.5%. The growth during the historic period can be attributed to rising demand for fast data retrieval, increasing adoption of artificial intelligence, growing need for personalized search experiences, expansion of cloud computing infrastructure, and greater investment in big data analytics.

The vector search as a service market size is expected to see exponential growth in the next few years. It will grow to $5.51 billion in 2029 at a compound annual growth rate (CAGR) of 29.2%. The growth during the forecast period can be attributed to rising adoption of machine learning algorithms, increasing emphasis on enterprise digital transformation, growing internet penetration and mobile adoption, expansion of data-driven decision making, and greater focus on real-time information access. Key trends in the forecast period include technological advancements in semantic search, innovations in vector embedding techniques, developments in scalable search architectures, research in multimodal data processing, and the development of advanced search applications.

The growing adoption of cloud computing is expected to drive the expansion of the vector search as a service market in the coming years. Cloud computing refers to the delivery of computing resources, including servers, storage, databases, networking, software, and analytics over the internet, enabling on-demand access, scalability, and flexible resource management without the need for on-premises infrastructure. The rise in cloud adoption is driven by its ability to provide scalable and flexible digital infrastructure, allowing organizations to efficiently manage and process large volumes of data while supporting advanced AI and machine learning workloads. Vector search as a service leverages cloud computing to deliver efficient and scalable semantic search capabilities, improving data retrieval accuracy and relevance. For example, in March 2024, according to Flexera, a US-based computer software company, multi-cloud usage increased from 87% last year to 89% this year, reflecting broader cloud adoption trends that provide the infrastructure necessary for scalable vector search as a service deployments. Therefore, the growing adoption of cloud computing is driving the growth of the vector search as a service market.

Key companies operating in the vector search as a service market are focusing on direct integration of model inference into cloud platforms to enhance search accuracy, reduce latency, and streamline machine learning workflows. Direct integration of model inference into vector databases enables seamless embedding generation, indexing, and retrieval within a unified environment, eliminating the need for external inference systems and improving operational efficiency. For instance, in July 2025, Qdrant Solutions GmbH, a Germany-based technology company, launched Qdrant Cloud Inference, enhancing its cloud platform with managed embedding generation and vector search capabilities. This launch represents a significant advancement beyond traditional vector storage and retrieval by integrating model inference directly into Qdrant’s managed ecosystem. The new feature allows users to create embeddings on demand without separate AI infrastructure, optimizing workflows for semantic search and AI-based applications. Through this innovation, Qdrant aims to simplify end-to-end vector data management and drive wider adoption of AI-powered retrieval and recommendation systems.

In June 2024, OpenAI Inc., a US-based artificial intelligence (AI) research and deployment company, acquired Rockset for an undisclosed amount. Through this acquisition, OpenAI Inc. aims to strengthen its data processing, analytics, and retrieval infrastructure to make AI systems, such as ChatGPT, more helpful, accurate, and responsive for users and enterprises. Rockset Inc. is a US-based company that provides vector search as a service.

Major players in the vector search as a service market are Amazon Web Services Inc., Alphabet Inc, Microsoft Corporation, Alibaba Group Holding Limited., International Business Machines Corporation, Databricks Inc., OpenAI Inc., MongoDB Inc., Elastic N.V., Searce Inc., Redis Ltd., Yugabyte Inc., Pinecone Systems Inc., Zilliz Technology Co. Ltd., Clarifai Inc., Jina AI GmbH, Weaviate B.V., Qdrant Solutions GmbH, Marqo Pty Ltd., Typesense Inc.

North America was the largest region in the vector search as a service market in 2024. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in vector search as a service report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East and Africa. The countries covered in the vector search as a service market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

Note that the outlook for this market is being affected by rapid changes in trade relations and tariffs globally. The report will be updated prior to delivery to reflect the latest status, including revised forecasts and quantified impact analysis. The report’s Recommendations and Conclusions sections will be updated to give strategies for entities dealing with the fast-moving international environment.

The rapid escalation of U.S. tariffs and the resulting trade tensions in spring 2025 are significantly impacting the information technology sector, particularly in hardware manufacturing, data infrastructure, and software deployment. Higher duties on imported semiconductors, circuit boards, and networking equipment have raised production and operational costs for tech firms, cloud service providers, and data centers. Companies relying on globally sourced components for laptops, servers, and consumer electronics are facing longer lead times and increased pricing pressures. In parallel, tariffs on specialized software tools and retaliatory measures from key international markets have disrupted global IT supply chains and reduced overseas demand for U.S.-developed technologies. To navigate these challenges, the sector is accelerating investments in domestic chip fabrication, diversifying supplier bases, and adopting AI-driven automation to enhance operational resilience and cost efficiency.

The vector search as a service market research report is one of a series of new reports that provides vector search as a service market statistics, including vector search as a service industry global market size, regional shares, competitors with a vector search as a service market share, detailed vector search as a service market segments, market trends and opportunities, and any further data you may need to thrive in the vector search as a service industry. This vector search as a service market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

Vector search as a service is a cloud-based solution that enables efficient similarity search and retrieval by representing data as high-dimensional vectors. It allows users to perform semantic searches across unstructured data, such as text, images, and videos, using machine learning algorithms. This service is commonly used to enhance relevance and accuracy in search, recommendation, and personalization applications.

The key components of vector search as a service are software and services. Software refers to a cloud-based solution that facilitates efficient similarity search and retrieval of high-dimensional vector data, enabling users to integrate semantic search, recommendation, and AI-driven applications without managing the underlying infrastructure. It can be deployed via cloud or on-premises and serves small and medium enterprises (SMEs) as well as large enterprises. These services are applied across various use cases, including recommendation systems, semantic search, image and video search, natural language processing, fraud detection, and other applications. The solutions cater to multiple end-user industries, including banking, financial services and insurance (BFSI), healthcare, retail and e-commerce, media and entertainment, information technology (IT) and telecommunications, and other sectors.

The vector search as a service market consists of revenues earned by entities by providing services such as data indexing, model training, cloud hosting, database management, application programming interface (API) integration, system maintenance, performance optimization, and data security. The market value includes the value of related goods sold by the service provider or included within the service offering. Only goods and services traded between entities or sold to end consumers are included.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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