Native Vector Search. No Elasticsearch Overhead.

Running Elasticsearch and layering vector search on top? JVM tuning, shard strategy, index templates, re-indexing. LanceDB is an AI-native storage engine with native vector search.

Why teams switch

Compute-storage separation

Storage on object storage. Compute scales independently. No JVM clusters.

One table, not six systems

Embeddings, metadata, and raw data together. No Elasticsearch cluster plus lake copy.

Schema evolution without rebuilds

Add columns without re-indexing indexes or rebalancing shards.

Full-text + hybrid search, native

Vector, full-text, and SQL queries in one system. Not kNN bolted onto Lucene.

Comparison

	Elasticsearch	LanceDB
Cost	JVM clusters for peak load. 24/7 node costs.	Object storage with compute-storage separation. Up to 100x savings.
Scale	Scale by adding nodes. Shard management required.	20 PB largest table. 20K+ QPS. Billions of vectors.
Search	Full-text native. Vector via kNN.	Native vector, full-text, and SQL hybrid search in one query.
Data model	Index-centric. Vectors inherit cluster behavior.	Raw data, embeddings, and features in one table.
Purpose	Text/log search with vector features.	Built for vector and AI workloads.
Best for	Text search with some vector.	Vector-first 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.