Developer Cuts LLM API Bill 70% by Fixing Call Patterns, Not Code

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A developer running several side projects, including a Slack summarizer and a document search tool, was spending about $200 a month on LLM APIs. Switching models and compressing prompts barely reduced costs, so they focused instead on how requests were structured. They found that a large static system prompt was resent on every call, and that a three-step flow was needlessly repeating that input. Merging the draft and polish stages into a single call cut costs by roughly 30%. Adding a Redis cache that reuses recent summaries for unchanged Slack threads reduced costs further, bringing the monthly bill to about $60.

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