10 Things No One Tells You About Deploying LLMs in a Startup

Deploying large language models in a startup is far more chaotic than polished demos suggest. Engineers must translate product expectations into technical reality while juggling prompt behavior, unpredictable costs, safety guardrails, and constantly changing model behavior. Success requires strong logging, careful monitoring, and disciplined trade-offs between accuracy, speed, and cost. Behind every seemingly magical AI product is a messy, ongoing process of experimentation, debugging, and adaptation