Shipping LLM features without a support-ticket disaster

A client's support bot generated a four-figure OpenAI bill in six hours last quarter, before we were involved. A retry loop in a background job kept re-firing on the same failed request, and nothing was in place to notice or stop it. Nobody caught it until the invoice arrived. That's the failure mode every guardrail below exists to prevent, and it's why we treat "integrate an LLM" and "ship an LLM feature safely" as two different projects with two different timelines. Token cost guardrails come first: a hard cap per user tier, rate limiting, and a fallback queue, so a runaway script hits a wall instead of a bill. Second is output validation — Pydantic or Zod schemas that reject anything the model returns that doesn't match the shape your database expects, before it ever gets written. Third is monitoring that actually watches the right numbers: latency, prompt-to-completion token ratios, and confidence scores, logged well enough that a drift in output quality shows up on a dashboard days before it shows up in a support ticket. None of this is exotic engineering. It's the same operational discipline you'd apply to any other system that can silently cost you money while looking like it's working.