Should Your Prompt Store Pick Your Model

Langfuse with Microsoft.Extensions.AI has an appealing story: update prompts without redeploying. A prompt fetches its config blob—model, tokens, temperature—which the code passes straight to the LLM. It works. But it puts a boundary in what I'd suggest might be better placed elsewhere — and moving it is a small enough change to be worth exploring. This post is about where to move that line in a .NET codebase using Microsoft.Extensions.AI against OpenAI or Azure OpenAI, with Langfuse as the source of prompts. What the current setup buys you Let me be fair to it first, because the coupling is a deliberate design, not an accident. Langfuse's prompt config is an optional JSON object versioned alongside the prompt. That means someone can open the Langfuse UI, change the model or a parameter, and ship it — no code change, no redeploy. Combined with labels (pointers to specific versions that your code references), a rollback is just moving the production label back to an earlier version. For prompt content iteration, that story is genuinely good, and there is a real audience of people who want model config coupled to prompt versions more tightly so each version is fully self-describing and reproducible. So this is a trade-off, not a bug. The question is whether the thing you are optimizing for — non-engineers tuning prompts without a deploy — is worth what the coupling costs. Why I think this deserves consideration Three points stand out. It is an untyped blob feeding provider selection. The Langfuse config is arbitrary JSON without schema enforcement. On the other end, whatever LLM plumbing you use will treat that model string as authoritative. A missing key, a stray max_tokens , or a gpt4o typo might not fail at build time or deploy time — it could fail on a live request, or silently do something unintended. You have a loosely-typed value driving an infrastructure decision, and the mistake may not surface until traffic hits it. It conflates two change lifecycles with di