Cutting LLM Expenses and Response Times by 70% Through Bifrost's Semantic Caching
When deploying Large Language Models in production environments, development teams encounter what can be described as an "Iron Triangle" of competing priorities: expense, speed, and output quality. While maintaining quality standards is typically essential, the other two factors grow proportionally with user adoption, creating mounting challenges. Each interaction with API providers such as OpenAI, Anthropic, or Google Vertex carries both monetary and temporal costs that can span multiple second