Native Vector Search. No OpenSearch Overhead.
Pushing OpenSearch into RAG or recommendations? JVM nodes to tune, shards to juggle, re-indexing when mappings change. LanceDB is an AI-native serverless vector database on the open Lance format.
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Tomorrow's AI is being built on LanceDB today
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 OpenSearch cluster plus lake copy.
Schema evolution without rebuilds
Add columns without re-indexing. No shard rebalancing.
Full-text + hybrid search, native
Vector, full-text, and SQL queries in one system. Not k-NN plugin.
Comparison
OpenSearch
LanceDB
Cost
JVM nodes for peak load. Paying for idle.
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 k-NN plugin.
Native vector, full-text, and SQL hybrid search in one query.
Data model
Index-centric. Vectors inherit shard behavior.
Raw data, embeddings, and features in one table.
Purpose
Text/log search engine with vector plugin.
Built for vector and AI workloads.
Best for
Log analytics with some vector search.
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.
Talk to Engineering
Or try LanceDB OSS — same code, scales to Cloud.
Schedule a Technical Consultation