Many companies are seeing their bills for large language model (LLM) application programming interfaces (APIs) explode, driven by redundant queries, according to Sreenivasa Reddy Hulebeedu Reddy, an AI application developer. Reddy found that users often ask the same questions in different ways, causing the LLM to process each variation separately and incur full API costs for nearly identical responses.

Reddy's analysis of query logs revealed that users were rephrasing the same questions, such as asking about return policies using phrases like "What's your return policy?", "How do I return something?", and "Can I get a refund?". Traditional, exact-match caching, which uses the query text as the cache key, proved ineffective, capturing only 18% of these redundant calls. "The same semantic question, phrased differently, bypassed the cache entirely," Reddy explained.

To address this, Reddy implemented semantic caching, a technique that focuses on the meaning of queries rather than their exact wording. Semantic caching analyzes the intent behind a user's question and retrieves the appropriate response from the cache, regardless of how the question is phrased. After implementing semantic caching, Reddy reported a cache hit rate increase to 67%, resulting in a 73% reduction in LLM API costs.

Semantic caching represents a significant advancement over traditional caching methods in the context of LLMs. Traditional caching relies on exact matches, using the query text as a hash key. This approach fails when users rephrase their questions, even if the underlying intent remains the same. Semantic caching, on the other hand, employs techniques like semantic similarity analysis or embedding models to understand the meaning of a query and identify semantically equivalent queries already stored in the cache.

The development of effective semantic caching solutions requires addressing several challenges. Naive implementations can struggle with accurately capturing the nuances of language and identifying subtle differences in meaning. Furthermore, maintaining the cache's accuracy and relevance over time requires ongoing monitoring and updates to account for changes in the LLM's responses or the evolving needs of users.

The implications of semantic caching extend beyond cost savings. By reducing the computational load on LLMs, semantic caching can improve the performance and scalability of AI applications. It also contributes to more efficient use of resources, aligning with broader efforts to promote sustainable AI development. As LLMs become increasingly integrated into various aspects of society, techniques like semantic caching will play a crucial role in optimizing their performance and reducing their environmental impact.

Reddy published his findings on January 10, 2026, and open-sourced his semantic caching implementation, encouraging other developers to adopt and improve the technique. The development signals a growing focus on optimizing LLM performance and reducing costs as these models become more widely adopted.