From Vector Database to Vector Lakebase - Zilliz blog

Blog<br>From Vector Database to Vector Lakebase

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From Vector Database to Vector Lakebase<br>May 11, 202611 min read

Robert Guo

Content<br>Why do the unified data foundation and three workload modes really matter?<br>The Key Vector Lakebase Features<br>Tiered Real-Time Serving Solutions<br>On-Demand Search<br>External Data Lake Search<br>Full-Spectrum Search<br>Unified Lake-Native Storage<br>Primary Use Cases of Vector Lakebase<br>Try Zilliz Vector Lakebase<br>Start Free, Scale Easily<br>Try the fully-managed vector database built for your GenAI applications.<br>Try Zilliz Cloud for Free

Today, we're launching the public preview of Zilliz Vector Lakebase — the next chapter for Zilliz Cloud. Vector Lakebase is the next step beyond vector databases. It is a semantic-centric data platform where open storage and elastic compute converge for AI workloads.

Vector Databases are purpose-built for real-time serving.

Vector Lakebase builds on an S3-based unified data foundation to power AI and agents across three workload modes:

real-time retrieval for latency-critical production serving,

iterative discovery for interactive and multi-step exploration,

batch analytics for offline mining and dataset optimization.

All scaling from gigabytes to petabytes.

Why do the unified data foundation and three workload modes really matter?

In short: because AI systems are no longer just a single-query retrieval problem. They operate as a continuous loop of serving, learning, and improving.

As this figure shows, the data foundation for AI and agent applications usually has three parts: raw multimodal data at the bottom, semantic data for online serving (such as text, vectors, and labels), and feedback data collected from production systems (such as user behavior, logs, agent notes, and statistics).

Many mature agent applications already have this kind of data foundation. The real pain point is that these different types of data are often scattered across multiple pipelines and systems, without a unified and structured data plane to support the workflow loop:

online serving (dark blue) → knowledge and feedback accumulation (light blue and orange) → insight discovery (green) → dataset and strategy improvement (purple) → better online serving .

As the picture also shows, a vector database alone is no longer enough, because it mainly supports real-time retrieval and serving-oriented data writes (the two blue paths). In this loop, the other two access modes — interactive discovery and batch analytics — are just as important.

For example, AI developers (either manually or through agentic systems) often need to explore feedback data and the underlying corpus to understand why serving quality is poor. They may also run large-scale semantic deduplication and clustering on newly crawled data, then mine edge clusters to discover new training data candidates.

These workloads are very different from traditional big data processing. The core computation is semantic rather than numerical. The data mainly consists of vectors, text, labels, and semantic metadata, while the core operations include vector search, full-text search, reranking, semantic clustering, and related semantic retrieval tasks.

Because of this, interactive discovery and batch analytics are naturally aligned with vector databases at both the data and compute layers. In many cases, online serving and offline processing even share the same underlying data foundation.

For example, teams may cluster and analyze high-value user tasks offline while simultaneously checking whether the supporting knowledge or strategies in the serving system show sparsity or quality issues.

Overall, any fragmented data architecture or isolated infrastructure islands slow down this loop — which can be fatal in the rapidly evolving race for AI capabilities. Vector Lakebase accelerates this loop through a straightforward but efficient approach: providing a zero-copy semantic data plane that can be efficiently accessed by all three workload modes — real-time retrieval, interactive discovery, and batch analytics.

The Key Vector Lakebase Features

Zilliz Vector Lakebase supports this workflow loop through five core capabilities:

Tiered Serving Solutions

Flexible serving tiers optimized for different real-time workloads — delivering ultra-high performance, balanced efficiency, and cost-effective scaling across massive datasets.

On-Demand Search

Designed for large-scale workloads where latency is less critical and compute remains idle most of the time — including infrequent search, data exploration, and batch analytics.

External Data Lake Search

Add state-of-the-art indexing and large-scale search capabilities directly to your existing lake data.

Full-Spectrum Search<br>From vector and text to JSON and geospatial—combined with hybrid retrieval, filtering, and reranking for expressive multi-modal queries.

Unified Lake-Native Storage

Unified storage for both serving...