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6/29/2026
The original title is "How I Cut LLM API Costs by 60% With 2 Lines of Code"

The original title is "How I Cut LLM API Costs by 60% With 2 Lines of Code"

Original: How I Cut LLM API Costs by 60% With 2 Lines of Code

Short summary

A developer shares how their LLM API bill tripled despite flat revenue growth, then identifies and fixes four patterns causing waste: bloated system prompts, lack of semantic caching, unnecessary context history, and zero cost visibility. The post introduces Tokoscope, a lightweight wrapper providing prompt compression, dual-layer caching, and token-level cost attribution by feature and user, achieving 60-70% cost reduction.

  • •System prompts accumulate redundancy over time; condensing can reduce tokens by 80%.
  • •Semantic caching catches near-duplicate requests; exact-match caching misses 80% of duplicates.
  • •Full conversation history wastes tokens; only the last 3-4 turns actually influence model output.

Generated with AI, which can make mistakes.

#ai-tools#ai-agents#industry-adoption
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