Embeddings enable semantic similarity search by representing text as high-dimensional vectors. Keyword search, however, returns results based on lexical matches and is often sufficient for simple retrieval tasks such as FAQ matching, deterministic filtering, metadata lookup, or rule-based routing. When embeddings are used for these low-complexity scenarios, organizations pay for compute to generate embeddings, storage for vector columns, and compute-heavy cosine similarity searches — without improving accuracy or user experience. In Snowflake, this can also increase warehouse load and query runtime.