Instacart serves 14 million daily users and manages billions of products, necessitating advanced search capabilities that go beyond keyword matching to understand user intent. The search system must reflect real-time inventory changes, leading to significant workloads on the database. Instacart transitioned from Elasticsearch and FAISS to a hybrid architecture using Postgres and pgvector, improving search performance and reducing write workloads by tenfold. This normalization allowed for better storage of machine learning features and improved flexibility. Moving compute closer to storage with NVMe resulted in a twofold increase in search performance. Instacart's migration to pgvector eliminated data duplication and operational complexity, enhancing search quality and user satisfaction, evidenced by a 6% decrease in searches with zero results.