Pgvector brings vector search to Postgres but faces significant operational challenges in production, especially with large datasets and real-time ingestion.

Index builds for both IVFFlat and HNSW are memory-intensive and time-consuming, making them disruptive to database operations and difficult to manage alongside transactional workloads. Filtering and query planning for vector searches are less optimized compared to dedicated vector databases, often resulting in poor search relevance and performance under complex queries.