Neon, a serverless Postgres provider, offers enhanced scalability for AI applications using the pgvector extension, which recently introduced a parallel index build feature. This feature significantly accelerates the creation of Hierarchical Navigable Small World (HNSW) indexes by a factor of 30, thanks to its ability to utilize multiple CPU resources. The pgvector extension is pivotal for vector similarity searches in semantic search and Retrieval Augmented Generation (RAG) applications. Despite the challenge of high memory requirements and lengthy build times for HNSW indexes, the new parallel index build functionality in pgvector 0.6.0 addresses these issues effectively, maintaining high recall rates with minimal impact on query accuracy. Neon's architecture, which separates storage and compute, allows users to dynamically allocate resources for index building and subsequently scale down to optimize costs. This flexibility makes Neon and pgvector an ideal combination for developers aiming to efficiently manage and scale database resources for AI applications.