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Hybrid Vector Database

Combine traditional database capabilities with advanced vector search to power modern AI applications. Hybrid vector databases store and query both structured data and high-dimensional vectors, enabling semantic search, recommendation engines, and generative AI use cases within a unified platform.

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Key Benefits

AI-Optimized Performance
AI-Optimized Performance
Native support for vector embeddings and similarity search algorithms enables rapid retrieval of semantically similar content, powering AI applications like recommendation systems, semantic search, and RAG-based generative AI with millisecond response times.
Real-Time Intelligence
Real-Time Intelligence
High-performance query engine delivers instant results for recommendation engines and personalization systems, processing millions of vector comparisons per second to provide relevant suggestions as users interact with applications.
Unified Data Management
Unified Data Management
Single platform handles structured relational data, unstructured documents, and vector embeddings simultaneously, eliminating the complexity of managing separate databases and simplifying application architecture.
Limitless Scalability
Limitless Scalability
Distributed architecture scales horizontally to handle billions of vectors and petabytes of data, maintaining query performance as AI workloads grow without requiring infrastructure redesign or application changes.
Innovation Enablement
Innovation Enablement
Purpose-built for emerging AI use cases including generative AI, semantic search, and multimodal applications, providing the foundation needed to adopt next-generation AI capabilities as they evolve.

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