Master Azure OpenAI cost optimization with these 7 proven architectural decisions, covering token budgeting, semantic caching, and smart model routing.
production AI
8 Articles
8
Azure OpenAI rate limits become a real concern the moment an AI application moves from development into production. Learn how to manage RPM and TPM to prevent throttling.
Discover why AI agents fail silently in production and learn how to implement complete observability using Azure Monitor and OpenTelemetry.
Learn how to eliminate AI agent hallucinations in production using Pydantic structured outputs and deterministic Python routing.
When you first build a multi-agent system, the initial architectural focus is usually on logic: getting the Researcher Agent to talk to the Writer…
In the rapidly evolving landscape of multi-agent architectures (such as those built with LangGraph or Microsoft’s AutoGen), developers quickly hit a…
When I first compared azure openai vs openai, I assumed the difference was mostly branding.Same models. Same…
When Prompt Engineering Wasn’t Enough The model was smart.The prompts were detailed.The outputs were… inconsistent.Sometimes it answered…