01
You're billed for reruns
The same questions arrive all day in different words, and every variation is a fresh, full-price model call.
how crowkis solves it · Semantic + structural matching turns paraphrases into sub-millisecond cache hits.
The problem
Your LLM bill is mostly
déjà vu.
Support bots, copilots, RAG apps, and agents answer the same questions all day, just phrased differently. This page is the anatomy of that waste, and what Crowkis does about each piece of it.
Same question. Different words. Full price, every time.
A production assistant doesn't get a thousand unique questions, it gets a few hundred questions a thousand ways. Without a cache that understands meaning, each variation is a separate, billable, seconds-long model round-trip.
0-70%
of spend is repeats*
0s+
per model round-trip
<1ms
per Crowkis hit
*typical range for support, docs, and copilot workloads, measure yours with Crowkis Replay.
Without semantic caching, the second and third ask are pure waste.
The two obvious fixes both fail.
One misses everything; the other serves things it shouldn't. The whole reason Crowkis exists is the gap in the middle.
Byte-for-byte matching means paraphrases never hit. Hit rate ≈ 0 on LLM traffic.
Everything near in embedding space gets served, including the things that must not be.
exact-matchredis-style
never wrong · never hits
similarity-onlyvector-style
always hits · sometimes lies
crowkisthe gap in the middle
reuses aggressively · refuses the rest
Reuse aggressively where meaning and structure agree. Refuse where they don't.
Five problems. One binary.
02
Exact caches miss everything
Redis-style caches compare bytes. Change one word and it's a 'new' question, so LLM hit rates round to zero.
how crowkis solves it · Crowkis compares meaning and structure, so rephrasing doesn't reset the bill.
03
Similarity caches can't be trusted
Vector-only caches serve 'cancel my subscription' for 'pause my subscription'. Close in math, catastrophic in production.
how crowkis solves it · Intent classes, template checks, and a confidence floor veto unsafe matches.
04
One bad entry poisons the neighbourhood
A hallucination or injected answer in a semantic cache gets served to every nearby query, poison radiates.
how crowkis solves it · A five-stage trust pipeline scores every write before it can ever be served.
05
Model upgrades torch your cache
Switch models and a normal cache cold-starts: all the value you accumulated is gone overnight.
how crowkis solves it · Canary + migration workflows carry warm cache value across model versions.
Stop reading about the waste. Measure yours.
Pull the free image and watch the dashboard count what you save, or book a call and we'll replay your own traffic through the cache live.