The Azure tag aggregates posts that detail how to move AI projects from prototype to production on Microsoft’s cloud, illustrate serverless gate patterns, and expose pitfalls like Redis cache failures in payment workflows. These topics show engineers how to leverage Azure services, design resilient pipelines, and avoid costly runtime bugs.
32 articles since Aug 2026
Learn why advanced shell scripting for .NET build automation beats ad‑hoc scripts. Get production‑grade patterns, caching tricks, and security tips for CI/CD pipelines.
Learn how to design, scale, and monitor ML.NET inference pipelines in .NET Aspire using MCP, Azure OpenAI, Redis caching, and event‑driven patterns for production‑grade AI services.
Explore why Deploying Multi‑Agent RAG on Azure AI Foundry behaves differently in US and India regions, with concrete architecture, code, and production‑ready guidance.
Learn how to design, implement, and tune rate limiting and resource governance for MCP servers, with .NET patterns, Azure integration, and real‑world case studies.
Explore the real engineering trade‑offs of API Gateways vs Service Meshes for .NET Microservices, with production patterns, performance numbers, and a decision matrix for Azure, Istio, Linkerd and more.
Explore a production‑grade microservices-based photo feed architecture that uses event‑driven pipelines, CQRS, and streaming to deliver real‑time photo sharing at massive scale.
Learn how to migrate a legacy ASP.NET Core REST service to a WebMCP‑backed AI endpoint. This .NET case study covers architecture, code, performance gains, and production pitfalls.
Learn how to build a highly available vector search service while navigating CAP trade‑offs, with concrete .NET patterns, Azure AI Search integration, and production‑ready guidance.
A senior‑engineer guide to comparing Azure Blob, Amazon S3, and Google Cloud Storage for .NET workloads – performance, cost, security, and migration tactics.
Learn how to generate, store, and query vector embeddings in .NET at scale. This guide covers model selection, FAISS/.NET integration, Azure Cognitive Search vector, and production‑grade patterns.
Explore why latency spikes in multi‑agent RAG pipelines differ between US and India regions, and get concrete architecture, code, and checklist to keep your Azure AI Foundry workloads snappy.
Learn how to design, test, and operate MCP server versioning and backward compatibility at scale, with concrete patterns, CI/CD pipelines, and real‑world case studies.
Learn how to architect, secure, and scale Deploying Multi‑Agent RAG with Semantic Kernel on Azure AI Foundry for latency‑critical customer support across US and Indian cloud regions.
Compare Azure AI Foundry and Azure OpenAI Service on model management, agent orchestration, cost, security, and .NET integration—what you need to choose for production workloads.
Learn how to instrument, store, and alert on LLM API usage costs in .NET. Real‑world patterns, code snippets, and production tips keep your AI spend under control.
Learn how to build a production‑grade, scalable video streaming architecture using microservices, CDNs, and analytics—optimized for latency, cost, and reliability.
Learn how to architect, secure, and scale Deploying Multi‑Agent RAG workflows with Semantic Kernel on Azure AI Foundry—complete with code, observability, and production‑ready checklists.
Learn how senior engineers can reliably deploy agentic AI workloads on Azure AI Foundry using Kubernetes, with concrete architecture, code, cost, and failure‑mode guidance.
Explore the anatomy of MCP server architecture, from Model Context Protocol fundamentals to .NET tooling, Azure integration, and production‑grade prompt engineering.
Learn how to implement the outbox pattern with EF Core, achieve transactional event publishing, and avoid distributed transaction pitfalls in .NET production systems.
Learn how to engineer advanced BPE tokenizer customization for domain‑specific corpora, integrate it into .NET LLM pipelines, and optimize performance for production workloads.
Learn how to instrument, monitor, and troubleshoot MCP server tool calls with OpenTelemetry, Azure Monitor, and Semantic Kernel for reliable, production‑grade observability.
Explore a production‑ready multi-tenant MCP server design that balances isolation, security, and Azure‑scale for modern SaaS platforms.
Explore how AI orchestration transforms enterprise .NET systems, with Azure AI Foundry, microservice patterns, and concrete production tips for scaling intelligent agents.
Learn how to architect, implement, and operate a production‑grade agent harness in ASP.NET Core using Semantic Kernel, MCP, and Azure services – the five‑layer approach that scales.
Explore the architectural trade‑offs, performance nuances, and .NET integration strategies of Semantic Kernel vs LangChain to make informed engineering decisions.
A deep dive into architecting a production‑grade Healthcare Document Processing Pipeline—covering AI extraction, FHIR integration, vector search, and compliance at scale.
Explore a production‑grade design for a real-time shipment tracking platform, covering scalable state management, event‑driven pipelines, and Azure‑native microservices.
Explore a production‑grade Agentic AI Customer Support Platform Architecture, from multi‑agent orchestration in .NET to Azure AI Foundry integration, with concrete code, scaling tactics, and hard‑won lessons.
Learn how to implement a free server AI regression gate in Python, integrate it with Azure OpenAI, and deploy it serverlessly. A step‑by‑step guide for senior engineers.
Discover the concrete architectural, reliability, and cost changes required when moving an AI system from a prototype to a production‑ready .NET service. Learn the hidden pitfalls, scaling patterns, and deployment best practices.
Learn how to guarantee payment transaction consistency and avoid double charges with robust idempotency patterns, scaling tricks, and production‑ready observability for modern payment platforms.