your users are asking the same question 40 different ways, and you're paying your LLM to answer every single one from scratch. "How do I reset my password?" "Forgot my login, help" "Can't get into my account" Same answer. Three full model calls. Normal caching can't catch this. It only works when the text matches exactly, character for character. Semantic caching matches on MEANING. A question comes in. It gets turned into an embedding. The cache checks if you've already answered something close enough. Hit? The saved answer goes back instantly. Zero LLM call. Miss? The model answers, and that answer gets saved for next time. Redis now runs this as a managed service called LangCache. No extra database to deploy, TTL and eviction handled for you. They're claiming cache hits up to 15x faster and big cuts to your API bill. But here's the part nobody's posting about. The similarity threshold is where this breaks. Set it too loose and "how do I rotate my API key" returns the answer for "how do I revoke my API key." Close in meaning. Very different outcome. So start strict. Log every hit for a week. Loosen it only where you've checked the answers are actually interchangeable. Your savings depend on how repetitive your traffic really is. Support bots and internal docs assistants win huge. Creative or one off queries barely move. Check your logs before you check the pricing page. follow @cyrilXBT https://t.co/tQmQCsxVlo