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Generative AI in .NET: Enterprise Architecture Patterns

Learn generative AI in .NET with proven enterprise patterns: RAG, function calling, IChatClient, resilience and cost control. Start building with C# today. Generative AI in .NET has moved from experiment to production requirement. In 2026, most enterprise teams building on C# are being asked to add chat assistants, document summarization, or intelligent search to existing line-of-business apps. The hard part is not calling a model. The hard part is doing it in a way that survives a compliance review, a traffic spike, and a vendor price change. This guide covers the architecture patterns that work for generative AI in .NET enterprise applications, with runnable C# examples and the pitfalls that catch most teams on their first project. Why Generative AI in .NET Needs Real Architecture A prototype that calls an LLM from a controller action works on a laptop. It fails in production for predictable reasons: Vendor lock-in. Model providers change pricing, deprecate models, and releas...
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AWS vs Azure vs GCP for .NET Developers: Best Cloud 2026

AWS vs Azure vs GCP for .NET developers compared in 2026. See pricing, C# SDK code, serverless and hosting options, then pick the best cloud for your app. The AWS vs Azure vs GCP debate comes up on almost every .NET team, usually right before a migration or a greenfield project kicks off. In 2026 all three clouds run .NET 10 well, publish official C# SDKs, and offer serverless, containers, and managed SQL. That makes the decision harder, not easier, because the differences are now about developer experience, pricing models, and ecosystem fit rather than raw capability. This guide compares AWS, Azure, and Google Cloud specifically from a C# and .NET developer's point of view, with runnable code, pricing notes, and a clear recommendation. AWS vs Azure vs GCP: The Short Answer for .NET Developers If you want the recommendation up front, here it is: Choose Azure if your team lives in Visual Studio, uses Entra ID (Azure AD), Microsoft 365, or SQL Server, or wants the smoothes...
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Clean Architecture in C#: Build Maintainable .NET Apps

Learn clean architecture in C# with a practical ASP.NET Core example. Layers, dependency rule, code samples, and best practices. Start building better apps today. If you have ever opened a five-year-old .NET solution and found Entity Framework queries inside a controller, business rules buried in a Razor view, and a "Utilities" project that every other project references, you already understand why clean architecture in C# matters. Clean architecture is a way of organizing a .NET solution so that your business rules sit at the center, completely independent of frameworks, databases, and user interfaces. The result is an application that is easier to test, easier to change, and far less likely to collapse under its own weight as it grows. This guide walks through the principles behind clean architecture, shows a complete project structure for an ASP.NET Core application, and provides runnable C# code for each layer. Along the way we cover the common mistakes teams make ...
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Cloud Security Best Practices 2026: AWS, Azure, GCP Guide

Learn cloud security best practices for AWS, Azure, and GCP in 2026. Practical C# examples for IAM, secrets, encryption, and logging. Start securing your cloud now. Cloud security best practices are no longer optional reading for .NET developers. In 2026, the vast majority of breaches in AWS, Azure, and Google Cloud are not caused by clever zero-day exploits. They are caused by misconfiguration: an over-permissioned IAM role, a connection string committed to Git, a storage bucket left public, or a logging pipeline nobody ever turned on. This guide walks through the cloud security best practices that matter most across the three major providers, and shows you how to apply them from C# so your applications are secure by default rather than secure by accident. Whether you are deploying an ASP.NET Core API to Azure App Service, running background workers on AWS ECS, or hosting containers on Google Cloud Run, the same principles apply. We will cover identity and access management, sec...
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Event-Driven Architecture in C#: Loosely Coupled Systems

Learn event-driven architecture in C# with runnable examples: domain events, MediatR, message brokers, and best practices. Build loosely coupled .NET systems today. Event-driven architecture in C# is one of the most effective ways to build loosely coupled systems that scale, evolve, and stay maintainable as your codebase grows. Instead of components calling each other directly, they announce that something happened, and any interested component reacts. In this tutorial you will learn what event-driven architecture is, why it matters for .NET developers, and how to implement it step by step: from native C# events, to in-process domain events with MediatR, to cross-service messaging with a message broker like RabbitMQ or Azure Service Bus. Every example is runnable on .NET 8 or later. What Is Event-Driven Architecture in C#? Event-driven architecture (EDA) is a design style where the flow of the program is determined by events : immutable facts that something happened in the past. ...
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ML.NET Image Classification in C#: Train & Deploy a Model

Learn ML.NET image classification in C#. Train a custom image classifier with transfer learning, evaluate it, and deploy it in ASP.NET Core. Start now. ML.NET image classification lets you train a custom image classifier in pure C# without leaving the .NET ecosystem. You don't need Python, you don't need to hand-write a neural network, and you don't need a PhD. In this tutorial you'll build an image classification model in C# using transfer learning, evaluate it properly, save it, and deploy it behind an ASP.NET Core Web API. Along the way we'll cover why each step matters, the best practices that separate a demo from a production model, and the pitfalls that catch most developers the first time. What Is ML.NET Image Classification and Why Use It? ML.NET is Microsoft's open-source, cross-platform machine learning framework for .NET. Its image classification API wraps a TensorFlow-based training pipeline and exposes it through the same MLContext and IDataV...
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