Many of your users ask the same question worded differently, and you're paying your LLM to answer every single one from scratch. Give your application a semantic cache to reuse answers for questions that mean the same thing for lower inference costs and faster responses. If your #AI project is stuck in prototype because the production cost doesn't work or your application latency gets worse with production traffic, this one's for you. Traditional caches need exact string matches, which almost never happen with natural language. Semantic caching matches on meaning instead and the impact is staggering. Build a semantic cache with Amazon ElastiCache (#Valkey) that intercepts redundant LLM calls before they hit your model See the real cost math: up to 86% reduction in LLM API costs & up to 88% faster response times Learn how to tune similarity thresholds so your cache saves money without sacrificing #generativeAI answer quality Next steps: Get started by referencing the example code in this blog: https://lnkd.in/eGguS6DG
Cut Your LLM Costs and Latency up to 86% with Semantic Caching
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From a narriel.com perspective, however, semantic caching is not just an optimization layer — it is a semantic intervention layer. The moment a system decides that two prompts “mean the same thing,” it introduces a classification step. That step depends on similarity thresholds, embedding models, and contextual assumptions. If tuned aggressively, the cache may suppress nuance. If tuned conservatively, the economic benefit shrinks. The architectural question therefore is not only: “How much cost can we save?” It is: “How do we ensure that semantic equivalence does not override contextual variance?” (Answer => narriel.com) In enterprise settings, especially under governance constraints, semantic reuse must remain observable, auditable, and adjustable. Otherwise, optimization can quietly reshape output behavior. Reducing inference cost is valuable. Preserving decision integrity while doing so is the harder engineering problem.
hi
Semantic caching is one of those “boring” optimizations that becomes a superpower at scale. Rule of thumb: cache intent + context, not just strings. But be strict on when not to cache (fresh data, personalized outputs, regulated flows). The real win is pairing similarity thresholds with observability so you can see savings without silent quality drift.
I found supabase is amazing vectordb with sql kind of queries.
I am guessing this supports dynamic auto scaling
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mdb has a nice new way of shortening quick scripts especially for systems involved with aws in terms of Postgresql database and mysql, any syncing?
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