Why use Qdrant instead of a traditional relational or key-value database for vector similarity search?

A traditional relational database is optimized around structured predicates and indexes such as B-trees, while a key-value store is optimized around direct key lookups. Neither is naturally designed to answer a query such as 'return the 20 records whose embeddings are closest to this 1536-dimensional vector.'

Qdrant is purpose-built for vector retrieval. It maintains vector indexes such as HNSW so search can explore a much smaller candidate graph instead of calculating a distance against every vector. It also combines vector search with payload filtering, which is important in production systems such as RAG, recommendations, and semantic search.

The trade-off is architectural complexity: introducing Qdrant means operating another datastore and keeping vector data synchronized with the system of record. If the dataset is small or the application already depends heavily on PostgreSQL, pgvector can be the better choice because it keeps relational data and vector search in one transactional ecosystem.

A common beginner mistake is to assume that a vector database is simply a faster place to store arrays. The important difference is the indexing and query execution model. Qdrant can also perform exact search when needed; ANN is a performance choice, not a requirement for every query.