Weaviate alternative. Vector search that scales without the setup complexity.

Evaluating the Weaviate vector database? LanceDB runs on object storage with simpler deployment. Skip the complex configuration. Get to production faster.

Why teams switch

Compute-storage separation

Storage on S3/GCS/Azure Blob. Scale compute independently. No clusters sized for peak.

One table, not six systems

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

Schema evolution without rebuilds

New embedding model? Add a column. No re-indexing, no migrations, no downtime.

Full-text + hybrid search, native

Vector, full-text, and SQL queries in one system. No external search engine needed.

Comparison

	Weaviate	LanceDB
Cost	Complex deployment model. Disk-backed performance requires cluster tuning.	Object storage with compute-storage separation. Up to 100x savings.
Scale	Schema-first design can be rigid for evolving workloads.	20 PB largest table. 20K+ QPS. Billions of vectors. Netflix, Uber in production.
Search	Vector + BM25 hybrid via modules.	Native vector, full-text, and SQL hybrid search in one query.
Data model	Schema-first — changes require careful planning.	Schema evolution - add columns without rewriting data.
Operations	K8s modules add deployment complexity.	Embedded to cloud-scale with minimal ops overhead.
Best for	Teams with K8s expertise and stable schemas.	Fast iteration, schema evolution, simpler ops, lower cost.

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.