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

This post analyzes the limitations of pgvector for vector search, identifying six conditions under which it is suitable and explaining why most applications fail at least two of these conditions quickly. It details pgvector's shortcomings in handling large datasets (>1M vectors), accurate metadata filtering, hybrid search (lack of BM25), and the complexities of data synchronization with relational databases. It contrasts these limitations with the advantages of dedicated vector stores like Qdrant, such as efficient filtering, native hybrid search, and independent scalability.