Stop Stitching Retrieval Systems Together.
Agents break when context is incomplete. And most systems make it incomplete by design.
Vectors in one system, metadata in another, raw data somewhere else. Every query means stitching results or maintaining pipelines just to make data usable.
LanceDB lets you query vectors, text, and structured data together, directly from the same table your agents rely on.
Vectors in one system, metadata in another, raw data somewhere else. Every query means stitching results or maintaining pipelines just to make data usable.
LanceDB lets you query vectors, text, and structured data together, directly from the same table your agents rely on.
One Query, All Your Context.
Agents need complete, relevant context, not partial results from disconnected systems.
Search embeddings, filter metadata, and retrieve raw data in a single query.
No joins across systems. No sync layers.
Just one query that returns everything your agents need.
Search embeddings, filter metadata, and retrieve raw data in a single query.
No joins across systems. No sync layers.
Just one query that returns everything your agents need.
Stop Building Pipelines Just to Retrieve Data.
Before an agent can fetch context, you have to make that context usable.
Generate embeddings, sync them into a search system, and join results back with metadata. Each step adds latency, cost, and inconsistency.
LanceDB removes that entire layer. Your data is queryable the moment it exists.
No duplicate storage. No sync jobs.
Generate embeddings, sync them into a search system, and join results back with metadata. Each step adds latency, cost, and inconsistency.
LanceDB removes that entire layer. Your data is queryable the moment it exists.
No duplicate storage. No sync jobs.
Real Queries, Not Stitched Workflows.
Real queries aren’t just vector search.
You run vector search, then filter, then rerank results to get something usable. Usually across different systems.
That means multiple steps, multiple systems, and inconsistent results.
LanceDB runs those steps in one query, directly on the same table.
No stitching. No tradeoffs.
Just queries that match how you actually retrieve context.
You run vector search, then filter, then rerank results to get something usable. Usually across different systems.
That means multiple steps, multiple systems, and inconsistent results.
LanceDB runs those steps in one query, directly on the same table.
No stitching. No tradeoffs.
Just queries that match how you actually retrieve context.
Get Results Your Agents Can Actually Use.
Queries return the actual data, not pointers that require another fetch.
Images, video, embeddings, and metadata come back together, ready for immediate use.
No second lookup. No missing context.
Just complete responses your agents can act on.
Images, video, embeddings, and metadata come back together, ready for immediate use.
No second lookup. No missing context.
Just complete responses your agents can act on.
Built For Production-Scale Retrieval.
Serve context at scale, without moving data out of object storage.
Handle billions of rows and high query throughput with stateless compute that scales independently of your data.
100,000+ queries per second.
10+ GB/s throughput.
Billions of rows in a single table.
Handle billions of rows and high query throughput with stateless compute that scales independently of your data.
100,000+ queries per second.
10+ GB/s throughput.
Billions of rows in a single table.
up to 70%
Model FLOPS Utilization
100K+
Queries per second
100B+
Rows in a single table
Retrieval That Evolves With Your Data.
Your data changes constantly. Your retrieval layer has to keep up.
Add new embeddings. Add new metadata. Update schemas without rebuilding your system.
No reindexing cycles. No migrations.
Your retrieval stays aligned with your data, and your agents always see the latest context.
Add new embeddings. Add new metadata. Update schemas without rebuilding your system.
No reindexing cycles. No migrations.
Your retrieval stays aligned with your data, and your agents always see the latest context.
Better Context.
One table.
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