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.
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.