A vector database stores vectors and searches for nearby items efficiently. It also manages metadata filters, updates, deletion, replication, and approximate nearest neighbor indexes.
A relational database can store vectors, and extensions such as pgvector add strong similarity search. Plain SQL without a vector index usually compares every row, which becomes slow at large scale. The issue is specialized indexing, not that relational databases are unable to hold arrays.
Use a relational database with vector support when data, transactions, and operational simplicity fit one system. Use a dedicated vector engine when scale, latency, filtering, distributed indexing, or vector-specific operations exceed what the existing database handles well.
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