Start with pgvector: Why You'll Outgrow It Faster Than You Think

The advice to "start with pgvector, graduate later" for vector databases is prevalent, but an analysis of over 110 community threads reveals that this approach is more nuanced and often limited by specific conditions. While pgvector offers seamless integration with Postgres, allowing for vector search without additional infrastructure, its effectiveness is contingent upon six conditions: managing a vector dataset under 1M vectors, not requiring accurate metadata filtering, having embeddings tightly coupled to relational data, not needing hybrid search, relying heavily on Postgres for business logic, and having a small team with manageable search logic in SQL. Most applications quickly outgrow pgvector in terms of features rather than scale, necessitating a shift to dedicated vector stores like Qdrant, which offer advantages such as efficient metadata filtering, native hybrid search, and scalability beyond 10M vectors. Despite the challenges of synchronizing data between dedicated vector stores and Postgres, these issues are manageable with well-established patterns, and opting for dedicated solutions can alleviate the limitations of pgvector when not all conditions are met.