What distance metrics does Qdrant support, and what does each one measure?

Qdrant supports Dot product, Cosine similarity, Euclidean distance, and Manhattan distance for dense vector spaces. The metric determines how the index ranks vectors, so it must match the geometry expected by the embedding or representation model.

Cosine measures angular similarity and is common for text embeddings when vector direction matters more than magnitude. Dot product combines alignment with magnitude, so vector norm can affect ranking. Euclidean measures straight-line distance, while Manhattan measures the sum of absolute coordinate differences.

The trade-off is not simply speed. A metric can materially change retrieval semantics. I would normally follow the embedding model's training or retrieval guidance rather than picking a metric based on a generic rule such as 'text always means cosine.'

A version caveat is important: Qdrant's currently documented dense metrics include Manhattan, which was added in v1.7. Older deployments may not support it. Qdrant also supports sparse-vector search with different semantics, so dense-vector metric rules should not be generalized to every vector type.