Lakebase Search
A database retrieval enhancement product introduced by Databricks that natively integrates production-grade full-text search and vector search capabilities into Postgres. It supports hybrid retrieval modes and is optimized for AI agent workloads.
Timeline (last 90 days)
Vendor selection should not rely solely on official benchmarks; it is recommended to conduct small-scale validation on real business data, observing recall quality, P99 latency, and monthly costs under actual traffic.
Databricks officially claims that Lakebase Search costs only one-quarter of running pgvector directly when handling the same workload.
Lakebase Search adopts a pay-as-you-go model with zero compute cost when idle.
Natively embedding both retrieval engines within the same database allows the merge logic to be completed inside the engine, eliminating the overhead of orchestrating two independent services at the application layer and handling network round-trips and result serialization.
Databricks announced that Lakebase Search has officially entered the GA (General Availability) stage.
Officially, Lakebase Search has reached new levels in price-performance, latency, and recall rate on the VectorDBBench benchmark.
Embedding retrieval capabilities within the database layer allows developers to complete the full pipeline from raw data to retrieval results with a single SQL statement.
Technical details of Lakebase Search, including specific performance metrics, supported embedding models, and indexing algorithms, are yet to be further disclosed by the official team.
Lakebase Search brings full-text search and vector search capabilities directly into the Postgres ecosystem.
All Facts (8)
Vendor selection should not rely solely on official benchmarks; it is recommended to conduct small-scale validation on real business data, observing recall quality, P99 latency, and monthly costs under actual traffic.
50%UnverifiedDatabricks officially claims that Lakebase Search costs only one-quarter of running pgvector directly when handling the same workload.
50%UnverifiedLakebase Search adopts a pay-as-you-go model with zero compute cost when idle.
50%UnverifiedNatively embedding both retrieval engines within the same database allows the merge logic to be completed inside the engine, eliminating the overhead of orchestrating two independent services at the application layer and handling network round-trips and result serialization.
50%UnverifiedOfficially, Lakebase Search has reached new levels in price-performance, latency, and recall rate on the VectorDBBench benchmark.
50%UnverifiedEmbedding retrieval capabilities within the database layer allows developers to complete the full pipeline from raw data to retrieval results with a single SQL statement.
50%UnverifiedDatabricks announced that Lakebase Search has officially entered the GA (General Availability) stage.
50%UnverifiedLakebase Search brings full-text search and vector search capabilities directly into the Postgres ecosystem.
50%