40 Lines of Go That Cut Our LLM Bill by 71%

8.4 relevance

Concrete Go optimization reducing LLM costs by 71%; directly matches AI/cloud infrastructure interests.

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Why It Matters

For a solutions architect optimizing AI costs and latency, this pattern offers a practical, low-code way to slash LLM spend without sacrificing quality, using a simple escalation gate that's easy to implement in any stack.

Key Takeaways

Implement a cheap-first router with output-quality gates instead of prompt classifiers to reduce LLM costs by over 70% with minimal latency impact.

40 Lines of Go That Cut Our LLM Bill by 71%

Summary

A team built a 40-line Go router that cut their LLM bill by 71% by sending requests to a cheap model first (GPT-5.6 Luna at $0.20/M input tokens) and only escalating to a strong model if output fails a gate. The break-even escalation rate is 90%, but they escalate only 15% of the time, making the cheap-first approach cost-effective despite double billing on escalations. The key insight is to judge output quality rather than classify prompt difficulty, which avoids the overhead and inaccuracy of pre-routing classifiers.

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