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.
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.
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