Caching cuts GenAI inference spend by removing work you are paying for twice: reprocessing the same prompt prefix, regenerating the same answer, or recomputing attention over tokens the model has already seen. Three mechanisms do most of the work. Prompt caching stores a repeated input prefix and bills it at roughly 10 to 50 percent of the standard input rate, which matters most when a long fixed system prompt or document dwarfs a short variable question. Semantic caching lives in your application and returns a stored answer when a new query is close enough in meaning to a prior one, removing the inference call entirely. Key value cache reuse keeps the attention state from earlier turns so a conversation does not reprocess its whole history every turn. The discounts are real, but each method has a failure mode a buyer has to govern, and the only honest measure of savings is the cache hit rate against live traffic.
Here is how each mechanism works, what it discounts, and the tradeoff that decides whether it pays.
How does prompt caching lower input cost?
Most enterprise inference calls carry a large fixed prefix, a system prompt, a tool schema, a retrieved document, followed by a short variable instruction. Without caching you pay full input rate to process that whole prefix on every call. Prompt caching stores the processed prefix for a short window and charges cached tokens at a fraction of the standard input rate. AWS Bedrock, Azure OpenAI, and GCP Vertex all expose a form of this, with cached input priced well below fresh input. The economics are simple: the larger the fixed share of the prompt and the higher the call volume against the same prefix, the more caching pays. A workload where the prefix is ten times the variable suffix can see input cost fall by a large fraction once the cache is warm. The buyer action is to structure prompts so the stable content sits at the front where it can be cached and the variable content sits at the end.
How much does each method actually discount?
| Method | What it avoids | Indicative effect | Main risk |
|---|---|---|---|
| Prompt caching | Reprocessing a repeated input prefix | Cached input billed at roughly 10 to 50 percent of standard input rate | Short cache lifetime, low reuse on varied prompts |
| Semantic caching | The inference call itself on near duplicate queries | Avoided calls cost only a cheap embedding lookup | A loose threshold returns a wrong near match |
| KV cache reuse | Recomputing attention over prior conversation turns | Lower latency and compute on multi turn sessions | Memory pressure on long contexts, mostly self hosted |
All figures are indicative and depend on provider, model, and traffic shape; verify against current provider pricing before you model savings.
When should you reach for semantic caching?
Semantic caching pays when a meaningful share of your traffic repeats in meaning even when the wording differs: support questions, product lookups, repeated analytical queries. You embed each incoming query, compare it to stored queries, and if the nearest match is above a similarity threshold you return the stored answer instead of calling the model. The avoided call costs only a cheap embedding and a vector lookup, so on high repeat traffic the savings dwarf prompt caching. The danger is the threshold. Set it too low and the cache returns an answer to a different question that merely sounds similar, which is worse than paying for a fresh call. Set a conservative threshold, scope a time to live to how quickly the underlying truth changes, and never cache answers that depend on per user state unless the cache key includes that state.
A worked example
A European SaaS company ran a customer facing assistant on a hosted model with a 4,000 token fixed system and policy prefix and a roughly 300 token variable question, at high volume. Two changes moved the number. First, restructuring prompts so the fixed prefix was cacheable cut input cost on warm traffic by a large fraction, because the expensive part of every call was now billed at the cached rate. Second, a semantic cache in front of the assistant caught a meaningful share of repeated questions and answered them with an embedding lookup rather than an inference call, with a conservative threshold and a short time to live tuned to how often the knowledge base changed. Together the two layers cut blended inference spend by close to a third without any drop in answer quality, measured by hit rate and human review. Figures are verified against billing data and anonymised.
Frequently asked questions
What is prompt caching and how much does it save?
What is the difference between prompt caching and semantic caching?
When does caching hurt rather than help?
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