Structuring blog post on new Azure and OpenAI APIs

The Responses API is introduced as a more flexible option compared to Chat Completions, supporting chat, assistants, and tools with unified endpoints. To keep things clear, I’ll avoid specific API versions in favor of placeholders, recommending users check the portal. The post will include a comparison table, code examples, and a migration guide. I’ll focus on differentiation like JSON schema support, tool calling, streaming, and alerts for potential gotchas. We’ll keep the tone human and structured for easy understanding, limiting sections to no more than six.

Chat completions vs responses API on Azure

If you’ve built against Azure OpenAI for a minute, you’ve probably met the classic Chat Completions API. It’s battle‑tested, widely documented, and works great for straight chat and instruction‑following. Azure’s newer Responses API takes that foundation and adds stronger tooling, structured outputs, and a more consistent surface across models. Here’s the human take: what’s different, when to use which, and how to switch without breaking your flow.

Azure AI Foundry Model Router: Save money without sacrificing quality

If you’ve shipped any AI‑powered feature, you’ll know two truths:
1️⃣ Not every prompt deserves your priciest, most capable model.
2️⃣ Maintaining a spaghetti mess of model deployments, code branches, and feature flags just to choose the “right” one is… a job nobody wants.

Azure AI Foundry’s model router fixes that. You deploy it once, call one endpoint, and it quietly routes each prompt to the model that best balances quality, latency, and cost — without you hand‑tuning every request.

Below: what it is, why it matters, and exactly how to put it to work from both Python and Power Automate.