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Cloud & DevOps

Caching Strategies for AI Responses

The cheapest AI call is the one you do not make. What is safe to cache, how to key it, and where caching quietly breaks correctness.

Updated 2 min readBy SpiderHunts Technologies

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Quick answer — TL;DR

Cache aggressively where the input fully determines the answer and the answer does not age. Key on the normalised input plus the model and prompt version, so a change to either invalidates the cache rather than serving an old answer.

The short answer

Repeated inputs are far more common than teams expect, particularly for classification and extraction. Caching those removes both cost and latency entirely for the repeats.

The risk is serving an answer produced by a prompt or model you have since changed, which is solved by putting both in the cache key.

What is safe to cache

CaseCacheable?Note
Classifying the same text againYesInput determines the answer
Extracting fields from the same documentYesDeterministic enough
Summarising a fixed articleYesUntil the article changes
Answering about live dataNoThe data moves
Anything personalisedPer user onlyNever share across users
Creative generationUsually notVariety is the point

The personalisation row is a correctness and privacy issue rather than a performance one. A cache keyed without the user can serve one person's answer to another.

Key it properly

  1. Normalise the input: trim, collapse whitespace, consistent case where appropriate.
  2. Include the model identifier.
  3. Include a prompt version that you increment when the prompt changes.
  4. Include anything else that changes the answer, such as language or user context.
  5. Hash the result for a compact key.

Point three is the one that prevents the worst failure. Without it, improving a prompt has no effect for every input already cached, and you will spend a day wondering why.

Expire sensibly

Answers about static content can live a long time. Anything touching data that changes needs a lifetime matched to how fast it changes, and anything about live state should not be cached at all.

Where you are unsure, a short lifetime still removes the burst of identical requests that arrive together, which is frequently where the cost is.

Measure the hit rate

If the hit rate is low, the cache is adding complexity without benefit and the key is probably too specific. If it is high, that is money saved and worth reporting.

Track it per use case rather than overall. One high-volume endpoint with repeated inputs frequently carries the whole benefit.

FAQ

Frequently asked questions

The questions readers ask us after this guide.

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How much can caching save?

It depends entirely on how often inputs repeat, which is higher than most teams expect for classification and extraction. Measure the hit rate rather than assuming.

What breaks if we cache wrongly?

Serving answers from an old prompt or model, and in the worst case serving one user's personalised answer to another.

Should the cache key include the prompt?

A prompt version, yes. Without it, prompt improvements have no effect on anything already cached.

Can we cache creative output?

Usually not, because variety is the point. Caching there makes the feature feel broken.

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