Zilliz, creator of Milvus AI vector database, has launched Vector Lakebase. The cloud-native solution augments real-time vector search with external data lake connectivity, batch analytics and interactive data discovery.
Milvus powers vector embedding storage for LLM and AI agent RAG workflows as Zilliz’s core offering. The open-source database supports 100-billion-scale vector search, with 44,000+ GitHub stars and over 100 million Docker pulls. It serves 10,000+ global enterprises and AI startups including MiniMax, Salesforce and Read AI.
Zilliz CEO Charles Xie states production vector search remains the firm’s core competency, with continuous speed and cost-efficiency upgrades across Milvus and Zilliz Cloud. Vector Lakebase represents the next technological leap, delivering a unified data layer that enables shared vector data for production queries, data exploration and petabyte-scale model training without data duplication or migration.
Built on S3-compatible unified storage, Vector Lakebase leverages the open Vortex engine. Zilliz VP of Product and Milvus core architect Robert Guo explains the product unifies disjointed workloads, covering real-time agent memory processing and offline semantic deduplication via tiered serving and on-demand compute resources.
Vortex, a Linux Foundation project formerly SpiralDB, is a high-performance columnar format for AI and analytics. It delivers drastically faster random access, scanning and writing performance with superior or equivalent compression versus Parquet and Lance.
Modern AI operates on iterative serve-learn-improve cycles. LLMs and AI agents generate operational feedback data, empowering batch analysis, training dataset refinement and agent behavior optimization.AI teams require deep exploration of raw and feedback data to diagnose service quality issues, run large-scale data deduplication and clustering, and identify high-value training data from newly crawled datasets.
Traditional architectures separate vector database production data and analytical feedback data. Vector Lakebase unifies these workflows with a zero-copy semantic data plane, concurrently supporting real-time retrieval, interactive discovery and batch analytics through five core capabilities.
Tiered Serving Solutions: Multi-tier real-time workload architecture balancing high performance, operational efficiency and elastic scaling for massive datasets.
On-Demand Search: Flexible compute for low-priority, latency-tolerant workloads including data exploration and batch processing to optimize resource utilization.
External Data Lake Search: Equips existing customer data lakes with advanced indexing and high-scale vector search capabilities.
Full-Spectrum Search: Supports vector, text, JSON and geospatial queries with hybrid retrieval, filtering and reranking for comprehensive multi-modal analysis.
Unified Lake-Native Storage: Consolidates serving and analytics workloads on Vortex-based storage with flexible formatting and robust data modeling.
Its three-tier serving system — Performance-Optimized, Capacity-Optimized and Tiered-Storage — adopts tailored indexing and data placement strategies to deliver flexible performance-cost tradeoffs for diverse enterprise scenarios.
Vector Lakebase is exclusively available on Zilliz Cloud (no on-prem deployment). Its External Collection feature builds zero-copy logical mapping with customer data lakes to enable high-performance indexing and full-spectrum search.
It supports mainstream table and file formats including Lance, Iceberg, Parquet and Vortex, with incremental synchronization and on-demand data refresh functions.
Now in public preview, Vector Lakebase supports Serverless, Dedicated and BYOC deployments across 30+ cloud regions on AWS, Google Cloud and Azure. New work-email users can claim $100 Zilliz Cloud credits on the official website.
Beijing Qianxing Jietong Technology Co., Ltd.
Sandy Yang/Global Strategy Director
WhatsApp / WeChat: +86 13426366826
Email: yangyd@qianxingdata.com
Website: www.qianxingdata.com/www.storagesserver.com
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