Azure OpenAI

Connecting Claude and Other LLMs to a Custom .NET MCP Server – A Production‑Ready Guide

Learn how to architect, secure, and optimize a custom .NET MCP server for Claude, Azure OpenAI, and other LLMs. Real‑world code, performance tricks, and production pitfalls included.

Mastering tokenization and BPE for .NET developers building LLM apps

Explore practical tokenization and BPE techniques for .NET developers building LLM apps, with Azure OpenAI integration, performance tricks, and production‑ready patterns.

Azure AI Foundry vs Azure OpenAI Service: Observability in Production

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.

Cutting Inference Costs: kv-cache and batching for inference serving in .NET

Learn how .NET teams can slash LLM inference spend with KV‑cache tricks and smart batching. Real‑world code, Azure OpenAI tips, and production‑ready patterns.

MCP Server vs Function Calling .NET AI Integrations: What Really Changes in Production

Explore how MCP Server and Azure OpenAI function calling reshape .NET AI integrations, from latency and state handling to multi‑agent orchestration and cost optimization.

When to Build a Single LLM Orchestration Layer

Guardrails and Red‑Teaming for LLM Features in .NET Applications – A Production‑Ready Playbook

Learn how to harden .NET LLM services with guardrails, red‑team adversarial testing, and Azure OpenAI best practices—turning prototypes into secure, scalable production systems.

Context length cost for .NET developers: Why your prompts are draining the budget

Explore how attention mechanics inflate the context length cost for .NET developers, see real‑world Azure OpenAI pricing, and get production‑ready strategies to keep token spend under control.

Observability for LLM Apps in ASP.NET Core: Trace First, Metrics

Learn how to instrument, trace, evaluate, and close the feedback loop for large‑language‑model applications built with ASP.NET Core. Real‑world patterns, code, and production tips for Azure OpenAI, Semantic Kernel, and OpenTelemetry.

Using evals as release gates for LLM changes in .NET CI/CD pipelines

Learn how to embed evaluator‑driven release gates into your .NET CI/CD flow, ensuring LLM updates meet accuracy, safety, and latency standards before they hit production.

Fine‑Tune vs Prompt vs RAG Decision Framework for .NET Teams – Choose the Right LLM Strategy

Learn a practical decision framework for .NET teams to pick fine‑tuning, prompt engineering, or Retrieval Augmented Generation. Reduce risk, control cost, and hit latency targets.

Azure OpenAI Service vs GPT‑4 API for .NET Microservices: A Deep‑Dive for Architects

Explore the real‑world trade‑offs of Azure OpenAI Service vs GPT‑4 API for .NET microservices, with cost, performance, security, and production‑ready patterns.

LLM Cost Control in .NET: Debugging Billing Surprises in Production

Learn how to slash Azure OpenAI spend in .NET services with proven caching, model‑shrinking, and routing patterns—real‑world code, metrics, and a 60% cost‑cut case study.

Azure OpenAI integration with .NET RAG: Debugging 429s in production

Learn how to harden Azure OpenAI authentication and scale a .NET Retrieval‑Augmented Generation service for production, with real‑world code, architecture, and cost tips.