The reason your pgvector benchmark is lying to you

Pgvector benchmarks deserve scrutiny because they often measure isolated query performance in controlled environments that don't reflect production realities. What matters to us isn't raw QPS on synthetic datasets—it's how vector search integrates with your transactional workload, maintains consistency, and scales under real operational pressure. I've seen teams adopt pgvector based on impressive throughput numbers, only to discover network latency, connection pooling bottlenecks, and index maintenance overhead become their actual constraints. The broader trend here is that we're embedding AI capabilities into existing Postgres stacks rather than building separate specialized systems, which is pragmatic but demands honest performance testing. My recommendation: benchmark pgvector within your actual application context using representative data volumes and query patterns. Test concurrent workloads, measure end-to-end latency including round-trip time, and stress-test your indexing strategy under write pressure. Skip the synthetic benchmarks and validate against your operational requirements first.