AI Cost Optimization

Purpose

Design and implement cost optimization strategies for LLM inference: token optimization, semantic caching, inference optimization, model routing, batching, distillation, and budget governance to minimize per-query cost while maintaining output quality. Provide production-ready patterns for cost monitoring, chargeback, and automated budget enforcement.

Agent Protocol

Trigger

User request includes: AI cost, token cost, LLM cost, prompt compression, caching for LLM, semantic cache, KV cache, quantization, model routing, cost optimization, batching, token counting, inference cost, context window, budget, chargeback, showback, cost allocation, FinOps, model distillation, speculative decoding.

Input Context

Required for a full optimization plan:

• Model(s) currently in use and pricing ($/1K tokens)
• Average tokens per query (input + output)
• Daily/monthly query volume
• Current cache setup (if any)
• Latency requirements (SLA in ms)
• Monthly budget and cost to date
• Number of users/teams for cost allocation