How to Cut Your LLM API Bill by 70% Using Prompt Caching, Off-Peak Hours, and Smart Routing
Most production AI applications overpay for compute by sending every query, regardless of complexity, to the most expensive model. By implementing smart tiered routing, caching static context, and taking advantage of scheduled batch windows, you can slash your monthly API spend by 70% or more.
Tier 1
Smart Difficulty Classification: Send incoming prompts through an ultra-fast, cheap model (like Gemini Flash-Lite or Groq Llama) to classify task complexity. Route simple tasks (formatting, regex, translation) to budget models, and only forward complex architectural questions to Claude Sonnet or GPT-6.
Tier 2
Maximize Prompt Caching: Anthropic, Google, and DeepSeek offer 75% to 90% discounts on cached prompt tokens. Structure your API calls so your large system prompts and documentation appear at the very beginning of the payload without dynamic timestamps breaking the cache.
Tier 3
Take Advantage of Off-Peak Pricing: Providers like DeepSeek offer significant price cuts during off-peak hours (typically 16:30 to 00:30 UTC). Route asynchronous background tasks, batch indexing, and embedding updates during these windows.
Tier 4
Token Trimming: Strip unnecessary whitespace, eliminate markdown formatting instructions when raw text suffices, and enforce strict max_token caps on model responses.
RunAnAi Takeaway
Spend an afternoon building a two-tier routing script. The moment your app scales, this setup will save you hundreds of dollars in compute costs every month.
Related Questions & User Queries
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