How I Cut My LLM API Costs by 70% Without Touching My Code
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technical depth 7
novelty 7
actionability 9
community 6
strategic 6
personal 9
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Practical cost-cutting strategies for LLM APIs are highly actionable and relevant to AI/ML workflows.
Summary
A developer cut LLM API costs by 70% without modifying application code by inserting a thin Node.js proxy that routes requests to cheaper models (Gemini Flash, Claude Haiku) based on prompt complexity and caches identical prompts. The proxy exposes an OpenAI-compatible API, so the app continues sending POST /v1/chat/completions while the proxy handles model selection, reducing average cost per request from $0.04 to $0.0025.