Optimizing LLM & AI Token Spend in Production Systems
A comprehensive operational playbook on token tracking, prompt caching, and cost governance for AI-driven applications.
A comprehensive operational playbook on token tracking, prompt caching, and cost governance for AI-driven applications.
Understanding Token Economics
Token costs vary dramatically across providers and models. GPT-4 class models can cost 30-60x more per token than smaller models like GPT-3.5. The key insight is that not every request needs a frontier model — intelligent routing based on query complexity can reduce costs by 40-60% without quality degradation.
Prompt Caching Strategies
Repetitive system prompts and few-shot examples represent a massive opportunity for cost reduction. By implementing prefix caching, organizations can reduce input token costs by up to 90% for cached content. Most major providers now support this natively.
Building a TokenOps Practice
A mature TokenOps practice includes real-time token monitoring by team and product, automated budget alerts with graceful degradation, model routing based on query classification, prompt optimization through versioning and A/B testing, and regular waste audits to identify unused or redundant API calls.
Measuring Success
Track cost-per-transaction rather than raw token volume. This metric normalizes for usage growth and gives you a true measure of efficiency improvements over time.
Finomics Team
Finomics Team
Cloud cost optimization, SaaS spend intelligence, and FinOps product insights from the Finomics team.