Learn Azure AI Search with C# — index data, run vector and hybrid queries, and add RAG to your .NET app. Start building intelligent search today. If your application's search box still runs a LIKE '%term%' query against SQL Server, your users are quietly suffering. They type "cheap laptop for uni" and get zero results because your catalogue says "affordable notebook for students." Azure AI Search with C# fixes exactly this problem: it combines classic keyword search, vector embeddings, and semantic reranking into a single managed service that you can drive from .NET with a few dozen lines of code. In this tutorial you'll build a working search index from scratch, run keyword, vector, and hybrid queries, and finish with a Retrieval Augmented Generation (RAG) pattern that grounds an LLM in your own data. This guide targets .NET 9 and the Azure.Search.Documents v11 SDK. Every snippet is runnable. We'll explain why each design choice matter...
Learn how to build a multi-cloud strategy for .NET apps across AWS, Azure & GCP with C# code examples, best practices, and pitfalls. Start today! A multi-cloud strategy means running your application across two or more public clouds — AWS, Azure, and GCP — instead of betting everything on a single provider. For .NET teams, this used to sound like a luxury reserved for Fortune 500 architecture boards. In 2026, it's increasingly a baseline requirement: enterprise customers demand deployment flexibility, regulators in finance and healthcare ask about vendor concentration risk, and one region-wide outage can cost more than a year of engineering effort. The good news is that modern .NET (now .NET 8/9/10 era) is genuinely cross-platform and container-first, which makes it one of the best ecosystems for going multi-cloud without rewriting your app three times. In this guide, you'll learn how to design a cloud agnostic architecture for .NET applications, see runnable C# code...