Output tokens cost 5x more per token than input tokens, making response length optimization the hidden lever for massive LLM cost savings most teams ignore.
Context window management is the hidden cost driver in AI applications. Strategic tiered routing and progressive loading can reduce costs by 40-70%.
New research shows context-aware dynamic routing can reduce AI infrastructure costs by 31% through energy-efficient model selection using adaptive algorithms.
Meta prompting enables LLMs to generate optimized prompts themselves, achieving 65% token reductions while maintaining performance through reusable templates.
AI API costs are the fastest-growing line item in most engineering budgets. Here’s how to track, understand, and optimise your spend across OpenAI, Anthropic, and Google AI.
Most teams use GPT-4o for everything. Here’s a data-driven framework for identifying which API calls can safely use cheaper models — saving thousands per month.