Top Vector Database Companies

The global vector database market is projected to grow from USD 2,652.1 million in 2025 to USD 8,945.7 million by 2030, at a compound annual growth rate (CAGR) of 27.5% during the forecast period.

Vector databases have become a foundational layer of modern AI infrastructure, enabling the fast and accurate retrieval of high-dimensional embeddings used across various applications, including language, vision, and multimodal domains. The market is accelerating as enterprises adopt RAG pipelines, personalized search, recommendation engines, and intelligent automation at scale. Core capabilities, including vector generation and indexing, similarity search, hybrid filtering, and scalable storage, enable organizations to retrieve context-rich information with low latency, even across massive datasets.

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As AI workloads continue to grow rapidly, vector databases are being engineered to support higher dimensionality, multimodal inputs, and real-time inference. Advances in approximate nearest neighbor search, distributed indexing, and hardware-accelerated retrieval are improving performance while reducing computational overhead. Vendors are also strengthening data governance, access controls, and privacy-preserving techniques to support regulated industries adopting an embedding-driven architecture. By combining speed, accuracy, and scalable design, vector databases now play a pivotal role in powering enterprise AI applications, enabling organizations to deliver context-aware insights, automate decision-making, and adapt quickly to evolving model and data demands.

Microsoft (US), Elastic (US), MongoDB (US), Google (US), AWS (US), Redis (US), Alibaba Cloud (US), DataStax (US), SingleStore (US), Pinecone (US), Zilliz (US), KX (US), Marqo.ai (US), ActiveLoop (US), Supabase (US), Jina AI (Germany), Typesense (US), Weaviate (Netherlands), GSI Technology (US), Kinetica (US), Qdrant (Germany), ClickHouse (US), OpenSearch(US), Vespa.ai (Norway), and LanceDB (US) are the leading market players. These players can concentrate on forming new alliances and relationships. Major firms have employed various strategies to enhance their market dominance, including partnerships, contracts, mergers and acquisitions, as well as the launch of new products.

In October 2025, Elastic introduced DiskBBQ, a new disk-friendly vector search algorithm for Elasticsearch that reduces memory use, improves query speed, and lowers infrastructure costs. By combining Hierarchical K-means clustering and Better Binary Quantization, DiskBBQ enables faster, more scalable vector search on large datasets.

Microsoft

Microsoft is a global technology company that develops software, hardware, and cloud services for consumers, enterprises, and public sector organizations. The company’s long-standing presence in personal and enterprise computing is anchored by Windows and Microsoft Office, which continue to serve as core productivity and operating system platforms for millions of users worldwide. Building on this foundation, Microsoft 365 extends these capabilities to the cloud, offering collaboration, security, and AI-enhanced productivity tools, including Copilot.

Microsoft has steadily expanded into cloud computing through Azure, its flagship platform that provides a broad set of services across computing, storage, databases, analytics, machine learning, security, and integration. Azure’s role in hybrid and multi-cloud strategies has strengthened Microsoft’s position in enterprise IT, supported by offerings such as Windows Server and System Center for data center and workload management. The company also integrates advanced vector search and AI capabilities within services like Azure AI Search and Azure DocumentDB, allowing organizations to build modern applications with embedded intelligence and efficient data retrieval.

In hardware, Microsoft produces Surface devices, Xbox gaming consoles, and various accessories that complement its software ecosystem. The company also supports developers through GitHub, Visual Studio, and a broad portfolio of tools and frameworks.

Overall, Microsoft’s business strategy centers on delivering integrated platforms that combine cloud infrastructure, AI, productivity software, and device ecosystems, enabling organizations to modernize operations and build scalable digital solutions without overstating its market role.

Elastic

Elastic, known as the Search AI Company, enables organizations to find real-time answers across their data through its Elastic Search AI Platform. Built on Elasticsearch, the company extends its expertise in search, observability, and security to deliver vector database capabilities that power AI-driven and semantic search applications.

At its core, Elasticsearch, as a Vector Database, and the Elasticsearch Relevance Engine (ESRE) enable the storage of dense vector embeddings alongside traditional indexed data. Elastic supports similarity search and hybrid queries that combine vector, keyword, metadata, and filters in a single operation, allowing results to be ranked by both precision and meaning. It offers multiple similarity metrics, including cosine similarity, dot product, Euclidean distance, and the newer max inner product similarity.

Elastic’s Learned Sparse Encoder (LSE) supports both sparse and dense embeddings and integrates with third-party models, including OpenAI, Hugging Face, and Cohere. This makes Elastic a complete vector data platform—spanning vector generation, storage, and retrieval.

The company’s distributed architecture provides enterprise-grade scalability and performance, supporting billions of vectors and efficient approximate nearest neighbor (ANN) search. Through Elastic Cloud, it delivers a managed, serverless vector database with elastic scaling and built-in governance. Seamless integration with the Elastic Stack (Beats, Logstash, Kibana) ensures unified observability, analytics, and security across environments.

Elastic’s vector capabilities support key use cases, including semantic and enterprise search, retrieval-augmented generation (RAG), recommendation systems, and multimodal similarity search across text, image, and audio. Continuous innovation in sparse-dense hybrid embeddings and similarity scoring further enhances performance and contextual accuracy.

Adopted by leading global enterprises, Elastic combines open-source flexibility, distributed architecture, and integrated AI capabilities to offer a unified, production-ready vector database platform for building scalable, intelligent search and generative AI applications.

Market Ranking

The vector database market is competitive, with Microsoft, Elastic, MongoDB, Google, and AWS collectively accounting for approximately 47–48% of the total market share.

Microsoft (US) is a leading force in the vector database market, leveraging the depth of its Azure ecosystem to deliver enterprise-grade vector search, indexing, and retrieval capabilities. Through Azure AI Search, Cosmos DB with integrated vector support, and tight alignment with Azure OpenAI models, Microsoft enables high-performance similarity search, multimodal retrieval, and large-scale embedding management. Its strength lies in seamless integration with cloud-native services, global infrastructure, and security frameworks—allowing enterprises to build RAG systems, recommendation engines, and intelligent search applications with reliability, governance, and end-to-end operational maturity. Continuous innovation in hybrid cloud, model optimization, and index acceleration positions Microsoft as a dominant provider for organizations deploying AI at scale.

Redis (US) is rapidly emerging as a strong contender in the vector database landscape, driven by its lightweight architecture, real-time performance, and developer-friendly Redis Stack. Redis delivers millisecond-level vector search through in-memory indexing, making it well-suited for latency-sensitive applications such as personalization engines, fraud detection, and conversational AI. Its ease of deployment across cloud, on-premises, and edge environments gives teams the flexibility to operationalize vector workloads without complex infrastructure. With growing support for hybrid queries, ANN search, and integration into RAG frameworks, Redis provides an agile, high-speed alternative to heavier vector database platforms—positioning it as a rising player for teams prioritizing speed, simplicity, and rapid iteration.

Related Reports:

Vector Database Market By Vector Database Solution (Vector Generation & Indexing, Vector Search & Query Processing, Vector Storage & Retrieval), AI Language Processing, Computer Vision, Recommendation Systems - Global Forecast to 2030

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