What is a semantic cache?

A semantic cache stores past prompts and their responses keyed by meaning rather than exact text. When a new query is similar enough to a cached one (by embedding distance), the stored answer is returned instantly without calling the LLM, saving both input and output tokens.

How do I estimate my cache hit rate?

It depends on how repetitive your traffic is. Support bots and FAQ assistants often see 40–60% similar queries; open-ended creative tools see far less. Start conservative and measure your real hit rate once a cache is in place.

Does the cache itself cost anything?

Yes, a little. Each query needs an embedding for similarity lookup, usually via a cheap embedding model, plus storage. This is typically a small fraction of the LLM call it replaces, so it is not subtracted in this estimate.

Will caching return stale or wrong answers?

It can if the similarity threshold is too loose or the underlying data changes. Tune the threshold carefully, cache only deterministic or slow-changing responses, and set a time-to-live so entries expire.

Which tools provide semantic caching?

Common options include GPTCache, Momento Cache, and vector-store-backed custom caches using your own embeddings. The savings math is the same regardless of which you choose.

What is the Semantic Cache Savings Estimator?

Free semantic cache savings estimator. Enter your daily query volume, cache hit rate and token sizes to model how much caching responses to semantically similar queries (GPTCache, Momento) saves on your LLM API costs — all in your browser. It runs free in your browser on Gera Tools, with nothing uploaded.

Semantic Cache Savings Estimator

Name: Semantic Cache Savings Estimator
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

See what semantic caching is worth before you build it

If your app answers similar questions over and over — support queries, FAQs, repeated lookups — many LLM calls are redundant. A semantic cache returns a stored answer when a new query is close enough in meaning to a previous one, skipping the model call entirely. This estimator models the saving from your own volume, hit rate and token sizes so you can decide whether caching is worth the engineering effort.

How it works

Every uncached request costs (input_tokens × input_price) + (output_tokens × output_price). A cache hit avoids that call completely, so it saves the full per-call cost. The estimator multiplies your monthly request volume by your cache hit rate to find how many calls are served from cache, then multiplies that by the per-call cost to get the monthly saving. The result shows your bill with and without the cache side by side, plus the percentage saved.

What is a realistic hit rate?

Hit rate varies enormously by application type:

Customer support bots — users ask variations of the same 50–100 questions. Hit rates of 40–60% are common once the cache warms up.
FAQ assistants — even higher repetition; a well-warmed cache can exceed 60–70%.
Code assistants or general chat — far lower; each conversation is unique. Expect single-digit hit rates.
Structured lookup tools (product search, entity extraction from known sets) — moderate repetition, 20–40%.

The warm-up period matters: a fresh cache has zero hits. Volume and hit rate both ramp as the cache fills. For this estimator, enter your expected steady-state hit rate, not day-one performance.

How semantic caches work under the hood

A semantic cache stores the embedding vector of each past query alongside the response. When a new query arrives, the cache embeds it and performs a nearest-neighbour search. If the closest past query is within a configurable similarity threshold (commonly a cosine distance under 0.1–0.2), the stored response is returned. If not, the query goes to the LLM and the result is stored for future use.

The key tuning variable is the similarity threshold. Setting it too loose means semantically distant queries get served the same cached answer, producing wrong results. Setting it too tight means the hit rate collapses and the cache provides little value. Good practice is to run the threshold calibration on a sample of real traffic: log the matched query pairs at a given threshold and have a human judge whether the cached answer was appropriate.

Tools and libraries

Common options include GPTCache (open-source, supports multiple embedding and storage backends), Momento Cache (managed, vector search built in), and roll-your-own approaches using pgvector or a dedicated vector store like Pinecone or Weaviate. The cost math in this estimator is independent of which backend you choose.

Tips for real-world caching

Match savings to repetition. Caching pays off most when traffic is repetitive. Measure your actual similar-query rate before committing.
Tune the similarity threshold. Too loose and you return wrong answers; too tight and your hit rate collapses. Validate against real queries.
Set a TTL. Expire cached entries so answers stay fresh when your data or prompts change.
Account for the cache’s own cost. Embeddings and storage add a small overhead — usually a fraction of the LLM call — so net savings are slightly below the headline figure here.