What is Zilliz Vector Lakebase?
Zilliz Vector Lakebase is a fully managed, enterprise-grade vector database service powered by Milvus, designed to unify real-time vector search, lake-scale discovery, and AI data operations. Built by the creators of Milvus, it addresses the challenges of large-scale vector search with high reliability, performance, and cost efficiency. The platform is production-tested across over 10,000 enterprises over eight years, capable of handling 100 billion-plus entities and 10,000-plus queries per second with consistent latency and predictable performance.
Key features include a unified storage architecture for both serving and analytics, built on Vortex, an open next-generation format that enables up to 10x faster and cheaper random reads compared to Lance, with per-column format flexibility. All data and indexes are stored on S3, with hot cache and on-demand compute to reduce costs by up to 90%. The system supports multiple data types—vectors, text, JSON, and geospatial—combined with hybrid retrieval, filtering, and reranking for expressive multi-modal queries.
Benefits include real-time retrieval for applications like semantic search, recommendation systems, and retrieval-augmented generation (RAG). Use cases span enterprise AI, entity search, clinical data management, and multilingual RAG systems. Technical details include integration with popular AI frameworks, support for billion-scale similarity search, and a developer hub with comprehensive documentation. Zilliz Cloud offers a fully managed experience, freeing engineering teams to focus on core product development while ensuring high availability and operational simplicity. With its lakehouse-like approach, it provides a single source of truth for real-time serving, iterative discovery, and batch analytics, each optimized for cost and performance at hundred-billion data scale.
Use Cases
Key Features
unified storage architecture real time vector search hybrid retrieval multi modal queries S3 based storage hot cache compute cost reduction enterprise grade reliability high QPS performance per column format flexibilityWho is it for?
AI engineers, data scientists, ML platform teams, enterprise architects, search engineers, recommendation system developers, RAG application builders
