We put our tiny embedding model up against OpenAI and NVIDIA. It didn't blink.
crowsight is a small, offline embedding model that ships inside Crowkis. We didn't trust it on faith, so we made it compete with the biggest embedding APIs on the one job a semantic cache actually needs. Here's what happened.
There's a quiet insecurity that comes with shipping your own model. Everyone else is calling OpenAI's text-embedding-3 or a hosted NVIDIA endpoint, and here we are with a 22-megabyte file called crowsight baked into the binary. So we did the uncomfortable thing: we set up a head-to-head and let the giants swing first.
In plain words. An embedding model turns a sentence into a list of numbers that captures its meaning. It's the part that lets a cache know 'reset my password' and 'how do I change my password' are the same question. crowsight is the one Crowkis ships with, no API call, no per-query fee.
The test was deliberately real. We took genuine rephrasings, the same question worded differently, the way a real user or a real agent would, and measured how reliably each model recognizes them while safely ignoring genuinely different questions. In semantic caching that trade-off is everything: catch the paraphrase (you save money) without matching a different question (you'd serve a wrong answer).
The headline isn't 'we beat OpenAI', the top models all nail this task. The headline is that a small model you run yourself, with zero per-query cost and nothing leaving your box, sits in the same tier as embedding APIs that bill you forever. crowsight also had the cleanest separation of the whole field: rephrasings scored high, different questions scored near zero. Clean separation is what keeps a cache from confidently serving the wrong answer.
The bottom line
If your semantic cache phones an embedding API for every lookup, you're paying a tax to understand questions you've already answered, and shipping every prompt off-box to do it. crowsight makes that unnecessary. It's bundled, it's offline, it's fast, and on the job that matters it keeps pace with models a hundred times its size.