GET Serves Cache, POST Runs Inference: Cost Safety for a Public LLM Endpoint

I built a site that gives deliberately wrong answers using an LLM. No login. No user API key. Anyone can hit the endpoint. amtaitfy.com is a toy site that gives intentionally wrong answers, generated by AI. This narrows the engineering problem: Make abuse bounded Make costs predictable Make casual attacks boring The core architectural decision is simple: GET serves cache only. POST is the only path that triggers fresh AI inference. Everything else is defense in depth. Threat model In scope: Acci