Azure AI Search at Scale: Building RAG Applications with Enhanced Vector Capacity

In the rapidly evolving landscape of Generative AI, the Retrieval-Augmented Generation (RAG) pattern has emerged as the gold standard for grounding Large Language Models (LLMs) in private, real-time data. However, as organizations move from proof of concept (PoC) to production, they encounter a significant hurdle: scaling . Scaling a vector store isn’t just about adding more storage; it’s about maintaining low latency, high recall, and cost efficiency while managing millions of high-dimensional embeddings. Azure AI Search (formerly Azure Cognitive Search) has recently undergone major infrastructure upgrades, specifically targeting enhanced vector capacity and performance.