The Native S3 Vector Database
LanceDB runs vector search directly on S3. Store Lance tables in your own buckets. S3 is the storage layer and source of truth. Reduce costs by up to 200x while serving millions of tables and tens of billions of rows.
Tomorrow's AI is being built on LanceDB today
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Why teams switch
Compute-storage separation
Storage on object storage. Compute scales independently. Query services scale up, down, or to zero.
One table, not six systems
Blobs, embeddings, and metadata stored together in Lance tables on S3. No separate vector index to sync.
Schema evolution without rebuilds
New features are appends, not migrations. No full table rewrites.
Full-text + hybrid search, native
Vector, full-text, and SQL queries in one system. Built for S3, not bolted on.
Comparison
Traditional Vector DB + S3
LanceDB
Cost
Duplicate storage - S3 for blobs + vector DB for embeddings.
Single storage layer on S3. Up to 100x savings.
Scale
Vector DB limited by RAM. S3 unlimited but needs ETL.
20 PB largest table. Direct S3 access. No ETL.
Search
Vector DB for similarity. S3 needs separate retrieval.
Native vector, full-text, and SQL hybrid on S3 data.
Data model
Vector DB has embeddings. S3 has blobs. Sync required.
Raw data, embeddings, and features in one table on S3.
Purpose
Separate systems for storage and search.
Unified storage and search on your object storage.
Best for
Traditional workloads with separate blob storage.
S3-native vector workloads at scale.
The Power of the Lance Format
Vector Search
- Fast scans and random access from the same table — no tradeoff
- Zero-copy access for high throughput without serialization overhead
Multi-Modal
- Raw data, embeddings, and metadata in one table — not pointers to blob storage
- No separate metadata store to keep in sync
Enterprise-Grade Requirements
Security
Granular RBAC, SSO integration, and VPC deployment options.
Governance
Data versioning and time-travel capabilities for auditability.
Support
Dedicated technical account management and guaranteed SLAs.
