LLM App Cost Optimization: Output Tokens and Caching
AggeK · reddit · 2026-07-15
After integrating precise request cost tracking into an LLM app, a developer shared core insights and optimization practices regarding API expenses:
- Output Tokens are the Major Cost: Since the price per output token is much higher than input, limiting maxtokens saves more money than trimming system prompts.
- Leverage Prompt Caching: Cache reads are extremely cheap. Keeping system prompts stable (avoiding volatile info like timestamps) can drastically cut costs. However, cache writes are more expensive, making it cost-effective only when the prefix is genuinely reused.
- Engineering Implementation Tips: Monetary calculations should use integers (millionths of a dollar) instead of floating-point numbers to prevent cumulative rounding errors. Billing should be based on a unified model price registry to ensure data consistency across all stages.
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