Vector search is essential for generative AI and RAG, but it struggles with exact alphanumeric IDs. Hybrid search, a combination of semantic vector search and exact keyword full-text search, addresses this limitation. Previously, achieving BM25 ranking with AlloyDB or Cloud SQL required a separate full-text search backend, causing complexity and data silos. Now, a native BM25 index is available in preview for AlloyDB and Cloud SQL for PostgreSQL 17+, powered by the open-source pg_textsearch extension. This eliminates the need for redundant systems, providing industry-standard keyword ranking directly within the database. The native BM25 index offers C-optimized, fast scoring and eliminates data duplication and synchronization lag. AlloyDB also offers supercharged vector search with ScaNN and HNSW index types. Unlike PostgreSQL's ts_rank, BM25 provides superior relevance scoring by considering inverse document frequency and term frequency saturation. Hybrid search merges semantic understanding with precise keyword matching, ensuring that specific, relevant results are prioritized. Both AlloyDB and Cloud SQL now support native BM25 and offer methods to integrate it with vector search for a unified hybrid search experience.