Vector search at scale. Without the RAM tax.

LanceDB runs on object storage with low-latency random access. You get the performance — without costs that scale linearly with your data.

Tomorrow's AI is being built on LanceDB today

Why teams switch

Cost-effective scale

Lance runs on object storage, not memory. 20 PB largest table. Up to 100x cost savings.

One table, not six systems

Raw data, embeddings, and features together. No sync jobs. No drift.

Add columns, skip the rebuild

New embedding model doesn't mean re-indexing. Add columns without rewriting data.

Comparison

Other VectorDBs LanceDB
Cost at scale RAM-bound — costs grow with data Object storage with low-latency random access. Up to 100x savings.
Scale Limited public benchmarks 20 PB largest table. 20K+ QPS. Netflix, Uber, Exa in production.
Data Handling Vectors only — raw data lives elsewhere Raw data, embeddings, and features in one table
Workloads Search only Search, analytics, feature engineering, training

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
Vector search at scale. Without the RAM tax.

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.

noize

Move Forward with Confidence

Validate your architecture with a team that understands enterprise scale.