Cosine similarity is useful when you care more about whether two representations point in a similar direction than how large the vectors are. That makes it a common choice for semantic search, clustering, and nearest-neighbor retrieval.
When embeddings are normalized first, cosine similarity becomes a reliable way to compare meaning across short text snippets, documents, or product metadata.