The original title is: "Prompt Caching Explained: How to Cut LLM Costs by 30–99%"

Short summary

The article explains two complementary LLM caching strategies—exact-match caching (stores full responses for identical requests, 100% token savings on hits) and prefix caching (reuses shared system prompts across varied requests, 50-90% savings on cached portion). A worked example shows a customer support bot cutting monthly costs from $15,000 to $6,750 by layering both approaches. Practical guidance covers when to avoid caching, prompt normalization techniques to boost hit rates, and no-code setup via LLM Gateway's dashboard.

• •Exact-match caching returns stored responses for identical requests at zero token cost; prefix caching reuses shared prompt prefixes across different user messages
• •Layering both strategies on a 50K requests/day support bot cuts monthly costs from $15K to $6.75K (55% savings)
• •Hit rates depend on prompt hygiene: normalize input, use temperature: 0 for deterministic tasks, and keep variable data out of cacheable prefixes

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