Context is Money: Slashing RAG Production Costs with Foundry IQ

Building RAG prototypes is easy, but pushing data retrieval to production at scale gets expensive fast. Between messy document layouts flooding prompts with structural noise and unoptimized queries running redundant search loops, token consumption quickly spirals out of control. This session explores how to build a lean, centralized knowledge plane using Azure AI Foundry IQ. We will look at how to combine layout-aware ingestion (turning messy data into dense, high-fidelity Markdown) with the open-standard Model Context Protocol (MCP) to serve your applications from a single, optimized data endpoint. Through live architectural breakdowns and practical demos, you will see how to leverage server-side semantic caching, enforce identity-based security boundaries, and monitor real-time token telemetry. Walk away with a scalable blueprint to slash your system’s token overhead by up to 50% while maximizing retrieval performance.