pg_embedding is a Postgres extension that provides Hierarchical Navigable Small Worlds (HNSW) indexes for vector similarity search, significantly improving query performance compared to traditional IVFFlat indexing. The latest version of pg_embedding now supports on-disk HNSW index construction, cosine similarity, Manhattan distance, and Euclidean distance, enabling faster queries and reduced storage requirements. This new release allows users to take full advantage of Neon features, including autoscaling, scaling to zero, read replicas, and serverless architecture, making it ideal for building scalable AI and LLM applications. By persisting the index on disk, users can sacrifice some performance but gain scalability and flexibility, while leveraging read replicas and the Neon serverless driver to reduce query latencies and costs.