Multimodal Vector Database for Image, Video & Text

LanceDB keeps images, video, audio, text, metadata, and embeddings in one engine. Stop wiring together SQL databases and separate layers for embeddings and blobs. LanceDB collocates everything - simpler governance, less I/O, better performance.

Why teams switch

Compute-storage separation

Storage on object storage. Compute scales independently. Up to 100x savings vs RAM-bound architectures.

One table, not six systems

Images, video, audio, text, metadata, and embeddings in one table. No sync jobs, no pointer-chasing.

Schema evolution without rebuilds

Add embedding columns as models improve. No re-indexing, no migrations, no downtime.

Full-text + hybrid search, native

Dense vectors, sparse vectors, and keyword scores in one query.

Comparison

	Text-Only Vector DB	LanceDB
Cost	Multiple systems - vector DB + blob storage + SQL for metadata.	One system. Object storage pricing.
Scale	Scaling means coordinating multiple systems.	20 PB largest table. 20K+ QPS. Billions of vectors.
Search	Vector search only. Metadata in separate system.	Native vector, full-text, and SQL hybrid in one query.
Data model	Embeddings only. Blobs and metadata elsewhere.	Raw data (blobs), embeddings, and features in one table.
Purpose	Text-only vector search.	Multimodal AI - vision, video, audio, text.
Best for	Text-only RAG.	Multimodal RAG, autonomous vehicles, media search.

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.