Funnel-with-gate workflow for reducing LLM costs without degrading quality. Starts with diagnosing the cost surface using cloud-finops (specifically the finops-for-ai and finops-anthropic references), then applies context-optimization to reduce context per call, context-compression to compress what can't be reduced, prompt-engineering for shorter prompts and better model selection, and uses agent-
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LLM Cost Optimization
The failure mode in LLM cost optimization isn't cutting costs — it's cutting costs by degrading quality without noticing. The gate at agent-evaluation is non-negotiable: any optimization that makes the product worse is a failure, not a savings. This workflow treats cost and quality as a single optimization problem with two axes, not as cost alone.
LLM costs are fundamentally different from traditional cloud costs: they're incurred at the moment a decision is made, n
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