Elastic(@elastic):Here are 5 distance metrics in vector search. But how do you choose the right one? • L1 (Manhattan): sum of absolute differences, exact kNN only with no HNSW support • L2 (Euclidean): straight-line distance, the safe default for most models • Cosine similarity: angle between vectors, magnitude ignored • Dot product: same ranking as cosine on normalized vectors, less compute • Max inner product: dot product without the normalization constraint Most teams default to cosine and move on. That works until your model outputs non-normalized vectors, and suddenly dot product or max inner product is the better fit. Scoring formulas and config details in the blog.

2026.06.03 16:00

Here are 5 distance metrics in vector search. But how do you choose the right one? • L1 (Manhattan): sum of absolute differences, exact kNN only with no HNSW support • L2 (Euclidean): straight-line distance, the safe default for most models • Cosine similarity: angle between vectors, magnitude ignored • Dot product: same ranking as cosine on normalized vectors, less compute • Max inner product: dot product without the normalization constraint Most teams default to cosine and move on. That works until your model outputs non-normalized vectors, and suddenly dot product or max inner product is the better fit. Scoring formulas and config details in the blog.