The worst class of production bugs don't crash anything. They just silently degrade. One common pattern: an LLM provider has a partial outage that returns 200 OK with empty or nonsensical responses. No error, no alert, no 5xx. Just silence dressed as success.
That's the hidden cost of production AI. Not the API bills, not the latency. The failures that look like normal operation until a user tells you something's wrong.
I run a production LLM pipeline that scores 10,000+ job listings daily. I work with OpenAI, Anthropic, Gemini, DeepSeek, and Groq at various points in the stack. Here's what I've learned about building fallback chains that actually work.
Why Single-Provider Architectures Are a Liability
Most teams start with one LLM provider. It works fine in development. Then production traffic hits and you discover the failure modes that don't show up in your test suite.
