The Real Costs of Running an LLM in Production
An overview of the costs associated with running LLMs in production, focusing on latency, reliability, and blast radius.
Most tutorials on building AI applications stop at the demo stage, overlooking the complexities that arise with real users. After months of developing an AI layer for a consumer app that relies heavily on model calls, I encountered four main challenges: cost, latency, reliability, and blast radius. Implementing caching and other strategies led to a 40-50% reduction in AI spending without sacrificing answer quality.