A team I worked with shipped their first LLM feature in two weeks. Six weeks later, they got a $47,000 OpenAI bill — for a free tier product. The post-mortem found three things: one tenant ran a script that retried failed requests indefinitely, another had a buggy prompt that asked the model to "respond in ten thousand tokens," and a third was just abusive — they had discovered the API key was effectively unlimited and were running batch jobs through it. There was no rate limit. No per-tenant budget. No cost ceiling. No audit trail. Just direct SDK calls from the application code straight to OpenAI. If your team is shipping LLM features the same way, this post is for you. We will walk through a runnable Spring Boot LLM Gateway that sits between your clients and the provider, enforcing API keys, rate limits, token budgets, caching, and audit logging — the controls you need before going to production, not after. Full source code, Docker Compose stack, and 9 execution screenshots are at exesolution.com .…