Discover how to build hybrid AI workflows using Azure Foundry Local and Azure AI Foundry Cloud, enabling seamless collaboration between local and cloud-based large language models. This guide walks you through the full setup, configuration, and routing process—based on Microsoft’s official documentation—to help you create scalable, secure, and privacy-focused AI applications. Learn how to synchronize inference workloads, manage model endpoints, and implement hybrid runtime patterns for real-world enterprise scenarios.
Microsoft Azure
Mastering Azure Foundry Local: Powerful Features + Comparison with Ollama & Other Local LLM Tools
Explore Azure Foundry Local, Microsoft’s latest on-device AI runtime for running large language models locally. Learn how it compares to Ollama and other local model tools, discover installation and setup steps, and build a working sample application using Foundry Local’s SDK and REST API. Perfect for developers seeking privacy-focused, hybrid AI solutions with enterprise scalability.
Core Components of Azure AI Search
Azure AI Search is powered by three core components: Index, Data Sources, and Indexers. The Index is the heart of search, storing structured fields for fast retrieval. Data Sources supply the raw content, while Indexers act as the pipeline, moving and enriching data into the index. Together, they form the foundation of a scalable, AI-driven search system that powers everything from enterprise knowledge bases to intelligent chatbots.
Knowledge Share 