--- title: Parsemoney: A Request-Translation Layer for LLM, and... description: Parsemoney is a Claude-specific API gateway written in Go that translates OpenAI-shaped chat-completion requests into native Anthropic Messages API format,... canonical: https://daily.dev/posts/parsemoney-a-request-translation-layer-for-llm-and-what-it-actually-costs-to-cache-a-prompt-2vjjf3pzn twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Parsemoney: A Request-Translation Layer for LLM, and What It Actually Costs to Cache a Prompt | daily.dev og:description: Parsemoney is a Claude-specific API gateway written in Go that translates OpenAI-shaped chat-completion requests into native Anthropic Messages API format,... og:url: https://daily.dev/posts/parsemoney-a-request-translation-layer-for-llm-and-what-it-actually-costs-to-cache-a-prompt-2vjjf3pzn og:image: https://api.daily.dev/og/posts/2vjJf3pZN.png og:image:alt: Parsemoney: A Request-Translation Layer for LLM, and What It Actually Costs to Cache a Prompt 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. # Parsemoney: A Request-Translation Layer for LLM, and What It Actually Costs to Cache a Prompt **[Medium](https://daily.dev/sources/medium_js)** · 6 min read · 2 upvotes · 1 comments ## Summary Parsemoney is a Claude-specific API gateway written in Go that translates OpenAI-shaped chat-completion requests into native Anthropic Messages API format, with intelligent prompt-cache breakpoint placement and real-time cost accounting. The post explains the architectural decisions behind the translation layer and provides a precise breakdown of how Anthropic's prompt caching actually works: cache writes cost ~25% more than base input rates, the 90% discount applies only to cached-prefix tokens (not outputs), and cache entries require a minimum of ~1,024 tokens to be created. Real-world testing showed 54–60% total savings with ~1,150 cached tokens against 100+ output tokens, rising past 70% with larger cached blocks. The project deliberately excludes multi-provider support, Redis, and semantic caching, focusing instead on Claude-specific optimization for teams migrating existing OpenAI-based tooling. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Community discussion Top comments from developers on daily.dev. **@kartiknvj** · 0 upvotes > The breakpoint placement is the interesting part, not the OpenAI-to-Anthropic shape translation. Anthropic's cache is prefix-scoped so the moment a tool result shifts anywhere before the breakpoint, the write cost hits again and people mis-attribute the spend to the model. Does Parsemoney measure the actual cache hit rate per breakpoint, or just place them heuristically? ## Similar posts on daily.dev - [Prompt Caching for Anthropic and OpenAI Models: Building Cost-Efficient AI Systems](https://daily.dev/posts/prompt-caching-for-anthropic-and-openai-models-building-cost-efficient-ai-systems-mjzh92xkq) · DigitalOcean · 2 upvotes · 0 comments - [How Does Prompt Caching Work and When Does It Actually Cut LLM Costs?](https://daily.dev/posts/how-does-prompt-caching-work-and-when-does-it-actually-cut-llm-costs--vnardpi3a) · DigitalOcean Community · 1 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#golang](https://daily.dev/tags/golang), [#anthropic](https://daily.dev/tags/anthropic), [#api-gateway](https://daily.dev/tags/api-gateway) [View this post on daily.dev](https://daily.dev/posts/parsemoney-a-request-translation-layer-for-llm-and-what-it-actually-costs-to-cache-a-prompt-2vjjf3pzn) ```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":"Parsemoney: A Request-Translation Layer for LLM, and What It Actually Costs to Cache a Prompt","url":"https://daily.dev/posts/parsemoney-a-request-translation-layer-for-llm-and-what-it-actually-costs-to-cache-a-prompt-2vjjf3pzn","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/parsemoney-a-request-translation-layer-for-llm-and-what-it-actually-costs-to-cache-a-prompt-2vjjf3pzn"},"datePublished":"2026-07-21T21:32:28.544Z","dateModified":"2026-07-22T00:20:49.548Z","description":"Parsemoney is a Claude-specific API gateway written in Go that translates OpenAI-shaped chat-completion requests into native Anthropic Messages API format,...","image":"https://media.daily.dev/image/upload/s--CxzD6vbw--/f_auto/v1722860399/public/Placeholder%2005","thumbnailUrl":"https://media.daily.dev/image/upload/s--CxzD6vbw--/f_auto/v1722860399/public/Placeholder%2005","isAccessibleForFree":true,"articleSection":"Medium","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Medium","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/medium","url":"https://daily.dev/sources/medium_js"},"commentCount":1,"discussionUrl":"https://daily.dev/posts/parsemoney-a-request-translation-layer-for-llm-and-what-it-actually-costs-to-cache-a-prompt-2vjjf3pzn","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":2},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":1}],"keywords":"llm,golang,anthropic,api-gateway","timeRequired":"PT6M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Medium","item":"https://daily.dev/sources/medium_js"},{"@type":"ListItem","position":3,"name":"Parsemoney: A Request-Translation Layer for LLM, and What It Actually Costs to Cache a Prompt"}]} {"@context":"https://schema.org","@type":"WebPage","@id":"https://daily.dev/posts/parsemoney-a-request-translation-layer-for-llm-and-what-it-actually-costs-to-cache-a-prompt-2vjjf3pzn","comment":[{"@type":"Comment","text":"The breakpoint placement is the interesting part, not the OpenAI-to-Anthropic shape translation. 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