Future-proof your database investment with a resilient, scalable solution that can support AI-driven needs
Leverage built-in functions to run similarity search across vectors and build recommendation systems
Store VECTOR
embeddings within CockroachDB using pgvector
-compatible semantic
Efficiently manage and query large vector data sets by partitioning them with secondary indexes
Lower query latency and cost of ownership on a fault-tolerant foundation
Significantly reduce the latency associated with query planning, especially for complex queries
Generic query plans are fully optimized once and cached which eliminates the need for re-optimization on subsequent executions
Lower the overall CPU usage for workloads that involve complex JOINs and/or index lookups
Now there’s less wait time in between versions with the introduction of a quarterly release cycle
Spin up your first cluster today or speak with an expert.