Most AI chatbot tutorials reach for Python. FastAPI, LangChain, a quick requests.post — done in 20 minutes. And that's fine for prototyping. But when I wanted to build something I'd actually put behind a real API — something with proper async concurrency, typed errors, and zero GC pauses — I reached for Rust instead. This is a writeup of chatbot , a production-oriented Rust backend that unifies Claude, OpenAI, and Ollama behind a single interface — with a Web UI, CLI mode, and Docker support baked in. Why Rust for an AI Backend? It's a fair question. LLM API calls are network-bound, so why does the backend language even matter? A few reasons: Predictable latency. No GC pauses under load means P99 response times stay stable when you're handling dozens of concurrent conversations. Memory efficiency. Each async task in Tokio is dramatically cheaper than a Python thread. You serve more users per instance. Type safety. Rust's ownership model makes API contracts explicit.…