This tag aggregates posts that analyze cost‑driving factors in .NET and Azure AI workloads, from choosing the right runtime emulator to comparing Azure OpenAI Service against the GPT‑4 API, and from token‑level efficiency in agentic AI to scaling debugging practices. By understanding these trade‑offs, engineers can lower cloud bills, improve performance, and make informed architectural decisions.
8 articles since Aug 2026
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 master cost engineering for Cloudflare Workers and AWS Lambda with deep pricing analysis, optimization patterns, and real‑world ROI case studies.
Learn how to cut LLM spend in Azure AI Foundry with caching, model shrinkage, KV‑Cache batching, and intelligent routing—practical patterns for .NET teams building production RAG apps.
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 .NET teams can slash LLM inference spend with KV‑cache tricks and smart batching. Real‑world code, Azure OpenAI tips, and production‑ready patterns.
Learn how to evaluate, benchmark, and choose the right legacy runtime emulator for production‑grade modernization, with performance, cost, and debugging considerations.
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.