Vector Search on Object Storage. Scale Without the RAM Tax.
Most vector databases keep everything in RAM. LanceDB stores data in object storage. Quantized indexes fit in memory. Full-fidelity vectors fetched from storage for reranking. Memory-like search performance, object storage cost.
Tomorrow's AI is being built on LanceDB today
Why teams switch
Compute-storage separation
Data on object storage. Compute scales with query load, not data size.
One table. Actual data
Embeddings, metadata, and raw files in the same table. Not links. Blobs.
Write the column, not the table
Add columns without rewriting existing data. Zero-copy schema evolution.
Hybrid search, native
Vector, full-text, SQL in one query. No round trips.
Comparison
| Legacy Vector Database | LanceDB | |
|---|---|---|
| Cost | RAM-bound. $3-5/GB/month at scale. | Object storage. $0.02/GB/month. |
| Scale | Limited by RAM. | 20 PB largest table. 20K+ QPS. |
| Search | Vector search. Full-text via integration. | Vector, full-text, SQL in one query. |
| Data model | Embeddings only. Raw data elsewhere. | Embeddings, metadata, blobs in one table. |
| Purpose | HNSW graph mutation. Slow writes. | IVF partitions. Writes don't block reads. |
| Best for | Small, static datasets. | Production 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.
Talk to Engineering
Or try LanceDB OSS — same code, scales to Cloud.