An LLM response cache that's aware of dynamic data
Raymond here from Butter.dev, an LLM response cache built as a chat-completions proxy. Today we're launching a key feature for the platform: the ability to generalize on dynamic, templated inputs. Caching at the HTTP request level has the obvious problem of generalizability. Nearly no request is identical, due to templated variables (like names) and metadata (like timestamps), so exact-match cache lookups rarely hit. We solve this at Butter by using LLMs to detect dynamic content in requests and derive their inter-relationships, allowing the cache entry to be stored as a template + variables…
In plain words
Butter.dev is an LLM response cache that works as a chat-completions proxy. It identifies dynamic content like names and timestamps within requests and stores responses as reusable templates with variables, enabling cache hits on similar but not identical requests. This approach is designed for teams running repetitive tasks like back-office automation, data processing, or agent-based workflows where request patterns are similar but contain varying data.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Raymond here from Butter.dev, an LLM response cache built as a chat-completions proxy. Today we're launching a key feature for the platform: the ability to generalize on dynamic, templated inputs. Caching at the HTTP request level has the obvious problem of generalizability. Nearly no request is identical, due to templated variables (like names) and metadata (like timestamps), so exact-match cache lookups rarely hit. We solve this at Butter by using LLMs to detect dynamic content in requests and derive their inter-relationships, allowing the cache entry to be stored as a template + variables + deterministic code. This allows future requests to contain different variable data, yet still serve from cache. We've found this approach greatly improves cache hit rate, and believe it could be useful for agents performing repetitive back-office tasks, computer use, or data transformations where input data is frequently of the same shape. - You can see a demo of learning patterns here: https://www.youtube.com/watch?v=ORDfPnk9rCA - We wrote more about the technical approach here: https://blog.butter.dev/on-automatic-template-induction-for-... - It's free to try out here: https://butter.dev/auth
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