Slashing LLM Cloud Costs by 70% with Semantic Embedding Caching in Redis

In customer-facing AI applications, over 60% of user queries are variations of common recurring questions (e.g., 'What is your refund policy?' or 'How do I reset my password?').

Re-evaluating full language model completions on every query wastes massive API token budgets and introduces unnecessary 2-second response delays.

Semantic caching generates vector embeddings of incoming prompts, querying Redis for existing completions with cosine similarity above 0.95 and returning answers in under 5ms at zero cost.

AVELIO implements semantic caching layers that dramatically reduce monthly LLM infrastructure bills while accelerating user experience.