Local offline embeddings (CEMBED): how it works and when to use it
Local offline embeddings (CEMBED), turns text into vectors with the bundled local model, no API key and no egress, with a micro-cache so repeats are free. Here's how Crowkis does it and why it matters for cost and safety.
Your users ask the same things all day, phrased a hundred different ways. Local offline embeddings (CEMBED) is how Crowkis turns text into vectors with the bundled local model, no API key and no egress, with a micro-cache so repeats are free.
How it works
Crowkis turns text into vectors with the bundled local model, no API key and no egress, with a micro-cache so repeats are free. It runs inside one Redis-compatible engine, so it composes with semantic caching, agent memory, and the other intelligence layers instead of being a separate service you wire together.
CEMBED "explain vector caches"
Why it matters
Repetitive LLM workloads are where the money is, and semantic caching can cut costs up to 60-70% on repetitive workloads. Local offline embeddings (CEMBED) is part of what makes that reuse safe rather than reckless, the difference between a cache you trust in production and one you audit after every incident. Community edition ships at full power, free to run.
Infrastructure earns the critical path one boring, verifiable feature at a time.