# Zilliz lays out vector database and lakebase differences

> Source: <https://www.blocksandfiles.com/ai-ml/2026/06/19/zilliz-lays-out-vector-database-and-lakebase-differences/5258689>
> Published: 2026-06-19 16:01:00+00:00

AI/ML

# Zilliz lays out vector database and lakebase differences

Zilliz has produced a Milvus [Vector Lakebase](https://www.blocksandfiles.com/ai-ml/2026/06/15/milvus-invents-vector-lakebase/5255542 ) FAQ to help position its vector database and vector lakebase offerings in your mental landscape. We reproduce it here.

**Is Zilliz moving away from vector databases?**

No. Real-time vector search stays the core serving engine – it just now lives inside a bigger system, the way OLTP databases stayed essential inside the lakehouse. Milvus remains an open-source vector database, and Zilliz Cloud's production vector search keeps getting faster and cheaper. More details [here](https://zilliz.com/blog/why-we-built-vector-lakebase).

**What does "lake-native" mean (vs. cloud-native)?**

Cloud-native describes software built to run elastically on cloud infrastructure. *Lake-native* means the data's primary home is open formats on cloud object storage (a data lake) – not loaded into a separate database as the source of truth. Data persists in the lake; compute attaches to it on demand. [Vector Lakebase](https://zilliz.com/blog/from-vector-database-to-vector-lakebase) is both.

**What is "unified lake-native storage"?**

One storage layer, on object storage, that serves both low-latency online search *and* large-scale analytics over the same files – so you don't keep one copy for serving and another for processing. Zilliz built it on its Loon storage engine and the open Vortex format. More details [here](https://zilliz.com/blog/from-vector-database-to-vector-lakebase).

**Is Vector Lakebase available for self-hosted Milvus?**

Real-time vector search is available in open-source Milvus, which you can self-host. The Vector Lakebase capabilities – on-demand compute, external data lake search, and tiered serving – are delivered as part of the managed Zilliz Cloud platform. More details [here](https://zilliz.com/blog/why-we-built-loon-a-storage-engine-for-ai-data-that-never-stops-changing).

**Do discovery and batch workloads slow down real-time serving?**

No. Each compute mode runs on its own elastic compute against the shared data: production serving uses resident, preloaded clusters, while discovery and batch run on separate on-demand or offline compute. Improvements flow back to serving atomically as a new snapshot, so serving never reads half-built data.

**External Data Lake Search **–** does it copy my data?**

No. External Collection creates a zero-copy logical mapping to your existing Lance, Iceberg, Parquet, or Vortex tables and builds the search indexes on top. Your data stays in your lake under your governance; updates are picked up via incremental sync (a refresh call), not a continuous change stream.

**Who generates the vectors, and where do they live?**

Your source data stays in your lake and is converted into vector embeddings using embedding models. Zilliz builds and manages the index layer (vector, full-text, JSON) that makes it searchable, rather than taking a second copy.