Zilliz Vector Lakebase
by Zilliz
Unified vector lakebase merging real-time vector search, analytics and data lake queries on one copy of data
Zilliz Vector Lakebase is a unified AI data platform that combines real-time vector search, interactive discovery and large-scale batch analytics on a single lake-native data foundation, aimed at data and AI platform teams running retrieval at enterprise scale. It extends Milvus — the open-source vector database with 44,000+ GitHub stars — into a managed Zilliz Cloud service with a 99.99% uptime SLA.
Zilliz announced Vector Lakebase on 10 June 2026 as a major Zilliz Cloud release that pairs its production vector database with a shared, lake-native data foundation, eliminating the copies and parallel stacks that typically separate serving from analytics. The architecture keeps vector data primarily in cloud object storage and builds on Vortex, an open Arrow-compatible columnar format used as the default storage layer for Loon, the manifest-based storage engine in Milvus 3.0 — a design Zilliz says cuts read amplification by more than 90% for low-latency access on object storage. Five capabilities define the platform: Tiered Real-Time Serving across Performance-Optimized, Capacity-Optimized and Tiered-Storage production tiers with a 99.99% uptime SLA; On-Demand Search with pay-as-you-go compute that Zilliz's internal benchmarks put at roughly one-fifteenth the cost of serverless alternatives; External Data Lake Search offering zero-copy indexing over existing Lance, Iceberg, Parquet and Vortex tables; Full-Spectrum AI Search spanning vectors, text, JSON and geospatial data in hybrid retrieval; and Unified Lake-Native Storage so on-demand and batch jobs bill only when compute is active. Founder and CEO Charles Xie describes it as "one data foundation where the same vectors can serve a production query, anchor a discovery session, and power a multi-petabyte training-data pipeline — without copies, migration, or a parallel stack." Vector Lakebase is in public preview on Zilliz Cloud across more than 30 regions on AWS, Google Cloud and Microsoft Azure, alongside Serverless, Dedicated and BYOC deployment options, with $100 in credits for new work-email signups. Zilliz, headquartered in Redwood City, California, is the creator of Milvus — 44,000+ GitHub stars, 100M+ Docker pulls, Apache 2.0 licensed and a graduated LF AI & Data project — and says it serves more than 10,000 enterprises and AI teams including Zillow, Salesforce, OpenEvidence, Exa, Filevine, MiniMax and Read AI.
At a Glance
- Category
- Data & Analytics
- Pricing
- Usage-based, Contact for pricing
- Target Market
- CTOs, CIOs, Data Engineering Leaders, Data Scientists, Enterprise Developers
- Headquarters
- Redwood City, United States
- Customers
- 10,000+ enterprises and AI teams
Key Features
- ✓Tiered Real-Time Serving
Three production tiers — Performance-Optimized, Capacity-Optimized and Tiered-Storage — backed by a 99.99% uptime SLA.
- ✓On-Demand Search
Pay-as-you-go compute that Zilliz internal benchmarks place at roughly 1/15 the cost of serverless alternatives.
- ✓External Data Lake Search
Zero-copy indexing and search directly on existing Lance, Iceberg, Parquet and Vortex tables without duplicating source data.
- ✓Full-Spectrum AI Search
Hybrid retrieval across vector, full-text, JSON and geospatial data in a single query path.
- ✓Unified lake-native storage on Vortex
An open Arrow-compatible columnar format underpinning Milvus 3.0's Loon engine, cutting read amplification by over 90%.
- ✓Flexible deployment
Available as Serverless, Dedicated and BYOC across 30+ regions on AWS, Google Cloud and Microsoft Azure, with self-hosted Milvus on Kubernetes, Docker or air-gapped environments.
Capabilities
Use Cases
- •Production RAG and semantic search
Serves low-latency vector queries for retrieval-augmented generation against a single logical copy of enterprise data.
- •Multi-petabyte training-data pipelines
Runs large-scale batch analytics over the same vectors that serve production traffic, with no migration between systems.
- •Exploratory data discovery
Interactive discovery sessions query the same lake-native store used for serving, billed only when compute is active.
Ideal For
Best For
- ✓Running RAG and semantic search at multi-petabyte scale without duplicating data
- ✓Consolidating vector serving, interactive discovery and batch analytics onto one platform
- ✓Querying vectors directly over existing Iceberg, Parquet and Lance data lake tables
Integrations
Deployment
Market & Ratings
10,000+ enterprises and AI teams
Market Analysis
Pros
- ✓Removes the copy-and-sync tax between vector serving and analytics
- ✓Open-source Milvus core avoids hard lock-in and supports air-gapped self-hosting
- ✓Zero-copy search over existing lake tables shortens time to value
- ✓Named enterprise customers including Zillow and Salesforce
Cons
- ✗Vector Lakebase is still in public preview, not general availability
- ✗Tier pricing is not published, so cost modeling requires vendor contact
- ✗The ~1/15 cost claim comes from Zilliz's own internal benchmarks
- ✗Faces pressure from multi-model databases folding vector search into general-purpose engines
Pricing
Public preview credits
$0
- ✓$100 in free credits for new work-email signups
- ✓Access to Vector Lakebase public preview on Zilliz Cloud
On-Demand Search
Usage-based
- ✓Pay-as-you-go compute
- ✓Billed only when compute is active
- ✓Reported ~1/15 the cost of serverless alternatives in internal benchmarks
Dedicated / BYOC
Contact for pricing
- ✓Performance-Optimized, Capacity-Optimized and Tiered-Storage tiers
- ✓99.99% uptime SLA
- ✓30+ regions on AWS, Google Cloud and Azure
- ✓Bring-your-own-cloud deployment
New signups with a work email receive $100 in credits. On-demand and batch jobs bill only when compute is active; specific tier rates are not published. Milvus itself remains free and Apache 2.0 licensed for self-hosting.
Sources
This page was written from 2 sources, 2 on domains other than zilliz.com.
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