9 tactics to slash LLM API costs, from caching to model routing

Developers can significantly reduce their Large Language Model (LLM) API expenses by implementing several cost-saving strategies. These tactics focus on optimizing prompt handling, model selection, and request batching. Key methods include caching identical or semantically similar prompts, routing requests to cheaper models for simpler tasks, and compressing prompts by shortening system messages or pruning retrieval-augmented generation contexts. Additionally, controlling output token limits, utilizing batch processing for non-urgent tasks, and leveraging provider-side prompt caching can further decrease costs. AI

IMPACT Developers can cut LLM API costs by 50-90% through techniques like caching, model routing, and prompt compression.

RANK_REASON Article provides practical advice and techniques for optimizing LLM API usage, rather than announcing a new product or research.

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• OpenAI
• Redis
• retrieval-augmented generation

• infra
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