RAG With Autoscaling: Better Performance With Lower Costs For pgvector

Neon's autoscaling feature for Postgres databases optimizes performance and cost-efficiency by dynamically adjusting resources based on demand, particularly useful for handling the high memory and CPU requirements of Hierarchical Navigable Small World (HNSW) index builds. This autoscaling ability mitigates the need for constant overprovisioning by using disk swaps to extend memory when necessary, making it possible to efficiently manage large index builds even with limited resources. By scaling up during resource-intensive operations like vector similarity searches and scaling down during regular operations, Neon ensures a cost-effective and efficient user experience. The feature is available across all pricing plans, including the free version, and is particularly beneficial for applications that rely on vector similarity searches for tasks like Retrieval-Augmented Generation (RAG) in large language models.