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Fuzzy cache

As explained in Machine Learning System Design

A fuzzy cache is a cache that matches requests using relaxed similarity conditions rather than exact key equality.

A fuzzy cache reuses expensive ML results without requiring exact request-key equality. This matters when inputs have a long tail and many requests are unique: even a small reduction in computation can save meaningful money. section 15.2.2

Relaxed matching can be implemented at increasing levels of aggressiveness:

  • Match cache keys against regular expressions so one result serves multiple similar requests. section 15.2.2
  • Match keys by semantic similarity, as in GPTCache-style caching for LLM queries. section 15.2.2
  • Treat fuzziness as a reliability risk: relaxed matching increases the chance of false cache hits. section 15.2.2

When exact repetition is rare

A search or language-model service may receive mostly unique requests within a cache lifetime. A regex-based or semantic cache can still share selected results, but only when mistaken reuse is acceptable. section 15.2.2

How Machine Learning System Design covers it

How deep it goes
Explains regex and semantic matching with false-hit caution, without detailing implementation or matching-threshold choices.
Examples
Uses regular-expression cache keys and GPTCache-style semantic caching for LLM queries.
What you can do after
Evaluate relaxed caching as a compute-saving option while weighing the risk of false cache hits.
How it connects
Connects caching with key-value storage, cache lifetime, semantic similarity, ML inference, and reliability.
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