Graph Database Vector Search Global Market Report 2025

The graph database vector search market size has grown exponentially in recent years. It will grow from $2.39 billion in 2024 to $2.95 billion in 2025 at a compound annual growth rate (CAGR) of 23.8%. The growth in the historic period can be attributed to increasing adoption of graph-based analytics, rising demand for real-time data processing, expansion of big data infrastructure, growing need for relationship-driven insights, and increasing focus on fraud detection and risk management.

The graph database vector search market size is expected to see exponential growth in the next few years. It will grow to $6.85 billion in 2029 at a compound annual growth rate (CAGR) of 23.4%. The growth in the forecast period can be attributed to increasing demand for vector-enabled data platforms, rising need for semantic and contextual search, growing investment in data management modernization, expanding integration of graph and vector search capabilities, and increasing focus on intelligent data retrieval and analytics. Key trends in the forecast period include advancements in vector search technology, innovations in multi-model and hybrid databases, growing research and development in contextual data processing, emergence of cloud-native graph vector platforms, and continuous improvement in open-source graph and vector frameworks.

The rising adoption of cloud-based solutions is expected to propel the growth of the graph database vector search market going forward. Cloud-based solutions refer to the delivery of computing resources such as servers, storage, databases, networking, software, and analytics over the internet to provide faster innovation, flexible resources, and economies of scale. The adoption of cloud computing is driven by scalability, allowing businesses to easily adjust computing resources based on demand and reduce infrastructure costs. Graph database vector search supports cloud-based solutions by enabling efficient management and retrieval of complex, high-dimensional data. It allows advanced semantic and relationship-aware queries, improving application intelligence and accelerating AI-driven insights in cloud environments. For instance, in December 2023, according to Eurostat, a Luxembourg-based government organization, 45.2% of enterprises across the European Union purchased cloud computing services, with 77.6% of large enterprises, 59% of medium-sized enterprises, and 41.7% of small businesses adopting cloud services. Therefore, the rising adoption of cloud-based solutions is driving the growth of the graph database vector search market.

Key companies operating in the graph database vector search market are focusing on integrating native vector search into core graph engines, including native vector index capabilities that store and query vector embeddings alongside property graph data to enable combined semantic and relationship-aware queries. Native vector index capabilities refer to database features that accept high-dimensional embeddings, maintain a vector index for nearest-neighbor search, and expose those searches through the graph query language so applications can join semantic similarity results with explicit graph traversals. For instance, in August 2023, Neo4j Inc., a US-based graph database company, launched native vector search capability. This integration embeds vector indexing and search directly within the Neo4j database, allowing developers to combine vector-based similarity search with the contextual power of connected data. It includes the ability to build and query vector indexes alongside existing graph data, enabling more accurate and explainable responses from generative AI models by grounding them in a rich network of relationships.

In February 2025, International Business Machines Corporation (IBM), a US-based technology and consulting company, acquired DataStax Inc. for an undisclosed amount. Through this acquisition, IBM aims to enhance its capabilities in managing and analyzing vast amounts of unstructured data by integrating DataStax’s NoSQL and vector database technologies, AstraDB, and DataStax Enterprise, both powered by Apache Cassandra. DataStax Inc. is a US-based data management company specializing in graph database vector search solutions.

Major players in the graph database vector search market are Amazon Neptune, Google Cloud Vertex AI, Microsoft Azure Cosmos DB, Alibaba Cloud Graph Database, Tencent Cloud, Oracle Corporation, SAP HANA Graph Database, MongoDB Inc., Redis Labs Inc., Neo4j Inc., YugabyteDB Inc., ArangoDB GmbH, Stardog Union Inc., Memgraph Ltd., Haveli Investments L.P., Cuadrilla Capital LLC, Weaviate B.V., Milvus, TerminusDB Ltd., and TigerGraph Inc.

North America was the largest region in the graph database vector search market in 2024. The regions covered in graph database vector search report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East and Africa. The countries covered in the graph database vector search 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 graph database vector search market research report is one of a series of new reports that provides graph database vector search market statistics, including the graph database vector search industry global market size, regional shares, competitors with the graph database vector search market share, detailed graph database vector search market segments, market trends, and opportunities, and any further data you may need to thrive in the graph database vector search industry. This graph database vector search market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenarios of the industry.

The graph database vector search market refers to an advanced data management technology that integrates graph database structures with vector-based similarity search to manage complex and interconnected data effectively. It enables storage, retrieval, and analysis of relationships between entities while supporting semantic and contextual search through vector embeddings. This combination improves performance in applications such as AI-driven insights, natural language processing, and knowledge graphs by delivering faster, more accurate, and context-aware query results.

The key components of graph database vector search are software and services. Software refers to systems and tools that manage graph data and execute vector similarity searches, enabling intelligent retrieval of interconnected and semantically related data. It is deployed through on-premises and cloud modes. The technology finds applications in recommendation systems, fraud detection, knowledge graphs, social network analysis, semantic search, and others, and is used by end-users such as banking, financial services, and insurance (BFSI), healthcare, retail and e-commerce, information technology and telecommunications, media and entertainment, manufacturing, and other sectors.

The graph database vector search market includes revenues earned by entities through data integration services, database management services, system implementation services, consulting services, and maintenance and support services. 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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