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Zilliz lays out vector database and lakebase differences

Zilliz released a Milvus Vector Lakebase FAQ to clarify its vector database and vector lakebase product lineup, reproduced below.

Is Zilliz abandoning vector databases?

No. Real-time vector search remains its core serving engine, embedded within a broader system much like OLTP databases inside lakehouses. Milvus stays an open-source vector database, and production vector search on Zilliz Cloud continues to gain speed and cost efficiency. Further details available via the linked page.

What differentiates “lake-native” from “cloud-native”?

Cloud-native software runs elastically on cloud infrastructure. Lake-native means data’s primary store is open-format files on cloud object storage (a data lake), rather than a standalone database as the single source of truth. Data persists in the lake, with compute attached on demand. Vector Lakebase combines both architectures.

What is unified lake-native storage?

A single object storage layer supporting low-latency online search and large-scale analytics on identical source files, eliminating duplicate data copies for serving and processing. Zilliz built this atop its Loon storage engine and open Vortex format; full details are linked.

Can self-hosted Milvus access Vector Lakebase features?

Open-source Milvus supports self-hosted real-time vector search. Advanced Vector Lakebase capabilities—on-demand compute, external data lake search, tiered serving—are exclusive to managed Zilliz Cloud. More information can be found on the dedicated page.

Will discovery and batch workloads hinder real-time serving performance?

No. Independent elastic compute pools operate against shared data: dedicated preloaded clusters handle production serving, while separate on-demand/offline resources run discovery and batch jobs. All updates apply atomically via snapshots, so serving clusters never read incomplete datasets.

Does External Data Lake Search duplicate user data?

No. External Collection creates zero-copy logical mappings to existing Lance, Iceberg, Parquet or Vortex tables and builds search indexes atop them. Original data remains governed within your lake; incremental refreshes pull updates instead of persistent change data streams.

Who generates vectors, and where are embeddings stored?

Source data resides in your lake and gets converted to vector embeddings via embedding models. Zilliz constructs and manages search index layers (vector, full-text, JSON) without creating secondary full data copies.

Beijing Qianxing Jietong Technology Co., Ltd.
Sandy Yang/Global Strategy Director
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Pub Time : 2026-06-22 10:04:56 >> News list
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