AI BriefingHow We Cut LLM Costs by 64% and Achieved a 98% Cache Hit Rate
Key point
Musinsa introduced Prompt Caching and cut LLM API costs by 64% while achieving a 98% cache hit rate.
Details
Musinsa built a token metrics dashboard to identify the cause of surging API costs after adopting LLMs. The analysis revealed that a specific API was repeatedly sending a massive system prompt of about 15K tokens each time, generating excessive costs.
To solve this, they applied Prompt Caching technology. Specifically, the following steps were taken.
- Separating prompt structure: Separated the unchanging system prompt from the message prompt that changes each time
- Setting cache points: Added cache points to the AWS Bedrock Converse API to reuse duplicate context
As a result, they achieved a high efficiency of 98% cache hit rate, cutting overall LLM costs by 64%. Through this, Musinsa maximized the operational efficiency of LLM services using large-scale context.
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