How to cut LLM API costs with semantic caching
The cheapest token is the one you never spend twice. Here's the simple math behind semantic caching, and where the savings actually come from.
LLM bills grow with repetition, and production traffic is deeply repetitive: support bots, internal copilots, and doc assistants answer the same handful of questions all day, rephrased endlessly. Every rephrasing is a full-price call for an answer you already produced.
The savings math
Savings roughly equal the share of your traffic that is repeat or rephrased, times the rate at which the cache catches it. On repetitive workloads that adds up fast: Crowkis can cut LLM costs up to 60–70% there. The more repetitive the workload, the higher the ceiling.
answer = cache.get_or_compute(
"explain vector caches",
lambda q: call_your_model(q), # only runs on a genuine miss
ttl=3600,
)Because Crowkis returns a confidence score with each hit and tracks savings on its dashboard, you can see exactly what the cache is worth, hit rate, tokens saved, and dollars avoided, rather than guessing. And because it refuses unsafe reuse, the savings don't come at the cost of wrong answers.
A cache that saves money by occasionally serving the wrong answer isn't saving money. It's deferring a support ticket.