Many companies are facing unexpectedly high bills for their use of Large Language Model (LLM) APIs, prompting a search for cost-effective solutions. Sreenivasa Reddy Hulebeedu Reddy, in a recent analysis of query logs, discovered that a significant portion of LLM costs stemmed from users asking the same questions in different ways.
Reddy found that while traffic to his company's LLM API was increasing, the cost was growing at an unsustainable rate of 30% month-over-month. He explained that users were submitting semantically identical queries, such as "What's your return policy?", "How do I return something?", and "Can I get a refund?", which were all being processed as unique requests by the LLM, each incurring the full API cost.
Traditional, exact-match caching, which uses the query text as the cache key, proved ineffective in addressing this redundancy. "Exact-match caching captured only 18 of these redundant calls," Reddy noted. "The same semantic question, phrased differently, bypassed the cache entirely."
To combat this, Reddy implemented semantic caching, a technique that focuses on the meaning of the queries rather than their exact wording. This approach led to a significant improvement in cache hit rate, reaching 67%, and ultimately reducing LLM API costs by 73%.
Semantic caching addresses the limitations of exact-match caching by understanding the intent behind a user's query. Instead of simply comparing the text of the query, semantic caching uses techniques like embeddings or semantic similarity algorithms to determine if a similar question has already been answered. If a semantically similar query exists in the cache, the system can retrieve the cached response, avoiding the need to call the LLM again.
The rise in LLM API costs is a growing concern for businesses integrating AI into their workflows. As LLMs become more prevalent, optimizing their usage and reducing costs will be crucial. Semantic caching represents one promising approach to address this challenge, but, as Reddy points out, successful implementation requires careful consideration of the nuances of language and user behavior.
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