From Vector Database to Vector Lakebase

May, 2026

From Vector Database to Vector Lakebase

A production AI system runs more than one workload at once. Live serving needs single-digit-millisecond retrieval at 1000+ QPS. Behind it, the same data is mined for offline work — semantic deduplication, clustering, dataset curation, and quality analysis on feedback and logs — at one to three orders of magnitude larger scale, and idle most of the time. Most teams stitch this together across separate systems, separate pipelines, and separate storage. Every time the data model evolves, the complexity compounds.

Vector Lakebase is the next chapter of Zilliz Cloud. One S3-based data plane holds your multimodal data, vectors, and indexes once, and three compute modes — real-time serving, iterative discovery, and batch analytics — read it zero-copy. Vector search isn't replaced; it's extended onto a foundation built for the rest of the AI data loop.

What's inside the guide

One data plane, three workload modes — and why a vector database alone no longer covers the AI loop
The five capabilities that define a Vector Lakebase, including on-demand search at ~1/15 the cost of serverless and zero-copy indexing over your existing lake
Where it's already running in production — agent knowledge bases, web-scale search, corpus deduplication, training-set preparation

Authored by Robert Guo, VP of Product at Zilliz and one of the architects of Milvus.

Get the Whitepaper