Deploying LLM Pipelines Without Breaking the Bank

The Cost Problem in Production AI

Moving from prototype to production often brings a ten-to-fifty-fold increase in inference costs. Token usage scales with traffic, and without careful architecture, monthly bills can quickly exceed the value the system generates.

Semantic Caching

Many production queries are semantically similar even when lexically different. A semantic cache that maps embeddings of incoming queries to previous responses can eliminate thirty to sixty percent of redundant inference calls with minimal impact on response quality.

Model Routing

Not every request requires a frontier model. A lightweight classifier can route simple queries to smaller, cheaper models while reserving expensive models for genuinely complex tasks. This tiered approach typically reduces costs by forty percent or more.