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.
12 articles since Aug 2026
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.