Cut LLM Costs and Latency with ScyllaDB Semantic Caching

Semantic caching is introduced as a technique to address high costs and latency issues when scaling AI workloads, particularly in applications relying on large language models (LLMs). By storing the meaning of user queries as vector embeddings, semantic caching allows for faster and cheaper responses by delivering cached results for semantically similar queries instead of repeatedly querying the LLM. This approach not only reduces the number of LLM calls, thereby decreasing costs, but also enhances response times by leveraging a low-latency database like ScyllaDB. ScyllaDB, with its built-in caching layer and vector search capabilities, is highlighted as an ideal platform for implementing semantic caching, offering high availability and strong performance metrics essential for real-time AI applications. The text outlines a basic workflow for implementing semantic caching, emphasizing the importance of maintaining cache accuracy through periodic invalidation to ensure up-to-date responses.