Zilliz, the Milvus AI vector database supplier, has introduced Vector Lakebase, extending cloud-resident, real-time vector search with an external data lake connector, batch analytics, and interactive discovery. Milvus stores vector embeddings for large language model (LLM) and AI agent retrieval-augmented generation (RAG) workloads, and is an open-source vector database purpose-built for 100-billion-scale vector search, with 44,000+ GitHub stars and over 100 million Docker pulls, used by more than 10,000 enterprises and AI-native startups worldwide, including MiniMax, OpenEvidence, Filevine, Exa, Salesforce, and Read AI. Charles Xie, Zilliz founder and CEO, stated that Vector Lakebase provides "one data foundation where the same vectors can serve a production query, anchor a discovery session, and power a multi-petabyte training-data pipeline — without copies, migration, or a parallel stack," building on an S3-based unified data foundation. Robert Guo, VP of Product at Zilliz, said Vector Lakebase delivers a unified storage layer on Vortex, tiered serving for the production path, and on-demand compute for everything else. Vortex is a Linux Foundation project and open-source columnar file format optimized for AI and analytics workloads, providing ~100x faster random access, 10-20x faster scans, 5x faster writes, with similar or better compression than Parquet or Lance. Guo noted