How to Reduce LLM Costs by 40% in 24 Hours (2025)
This article covers five strategies to optimize costs for using large language models (LLMs), including prompt caching, model routing, semantic caching, batch processing, and using an AI gateway.
Why it matters
As LLM usage becomes more widespread, controlling costs will be critical for teams building AI-powered applications and services.
Key Points
- 1LLM costs scale linearly with usage, but can be reduced by 40-70% using optimization strategies
- 2Prompt caching stores frequently-used context to avoid paying full price for the same tokens
- 3Model routing sends requests to the most cost-effective model based on task complexity
- 4Semantic caching stores responses for similar queries to avoid redundant processing
- 5Batch processing reduces costs for async workloads by up to 50%
- 6An AI gateway provides a centralized way to implement all optimization strategies
Details
The article explains that as LLM usage grows, costs can spiral out of control if teams don't optimize their workflows. It provides a cost breakdown for different LLM models, showing that cheaper 'efficient' and 'ultra-low' models can provide significant savings compared to more powerful 'frontier' models. The five optimization strategies covered can reduce costs by 40-70% in total. Prompt caching stores frequently-used context to avoid paying full price, model routing sends requests to the most cost-effective model, semantic caching stores responses for similar queries, batch processing reduces costs for async workloads, and an AI gateway provides a centralized way to implement all these strategies.
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