--- title: The Complete Guide to Inference Caching in LLMs | daily.dev description: Inference caching reduces LLM API costs and latency by reusing stored computation results. Three complementary layers are covered: KV caching (automatic,... canonical: https://daily.dev/posts/the-complete-guide-to-inference-caching-in-llms-uh6o61eic twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: The Complete Guide to Inference Caching in LLMs | daily.dev og:description: Inference caching reduces LLM API costs and latency by reusing stored computation results. Three complementary layers are covered: KV caching (automatic,... og:url: https://daily.dev/posts/the-complete-guide-to-inference-caching-in-llms-uh6o61eic og:image: https://api.daily.dev/og/posts/uh6o61eIC.png og:image:alt: The Complete Guide to Inference Caching in LLMs og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # The Complete Guide to Inference Caching in LLMs **[Machine Learning Mastery](https://daily.dev/sources/mlm)** · 9 min read · 1 upvotes · 0 comments ## Summary Inference caching reduces LLM API costs and latency by reusing stored computation results. Three complementary layers are covered: KV caching (automatic, within a single request), prefix caching (reuses attention states across requests sharing the same prompt prefix), and semantic caching (skips model calls entirely for semantically similar queries using vector search). Practical guidance includes prompt structuring rules for prefix caching, provider implementations (Anthropic, OpenAI, Google Gemini, vLLM, SGLang), and a decision framework for choosing and combining strategies based on use case. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Rethinking KV Caching For Production Inference](https://daily.dev/posts/rethinking-kv-caching-for-production-inference-1px3uv98r) · Daily Dose of Data Science \| Avi Chawla \| Substack · 0 upvotes · 0 comments - [KV vs Prefix vs Prompt vs Semantic Caching](https://daily.dev/posts/kv-vs-prefix-vs-prompt-vs-semantic-caching-k9hzdns5l) · Daily Dose of Data Science \| Avi Chawla \| Substack · 0 upvotes · 0 comments - [The Inference Tax: How Prefix-Aware Routing Eliminates the Hidden Cost of LLMs at Scale](https://daily.dev/posts/the-inference-tax-how-prefix-aware-routing-eliminates-the-hidden-cost-of-llms-at-scale-eej7jbkjc) · DigitalOcean · 0 upvotes · 0 comments - [GKE Inference Gateway prefix caching accelerates AI inference](https://daily.dev/posts/gke-inference-gateway-prefix-caching-accelerates-ai-inference-qyjrx3igz) · Google Cloud · 1 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm) [View this post on daily.dev](https://daily.dev/posts/the-complete-guide-to-inference-caching-in-llms-uh6o61eic) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"The Complete Guide to Inference Caching in LLMs","url":"https://daily.dev/posts/the-complete-guide-to-inference-caching-in-llms-uh6o61eic","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/the-complete-guide-to-inference-caching-in-llms-uh6o61eic"},"datePublished":"2026-04-17T12:20:51.201Z","dateModified":"2026-04-17T12:21:20.841Z","description":"Inference caching reduces LLM API costs and latency by reusing stored computation results. 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