Bring your own embedder / reranker: how it works and when to use it
Bring your own embedder / reranker, swaps in any sentence-transformers MiniLM or GTE export via ONNX, so you're never locked to the default model. Here's how Crowkis does it and why it matters for cost and safety.
Every reworded repeat of a question you've already answered is a full-price model call. Bring your own embedder / reranker is how Crowkis swaps in any sentence-transformers MiniLM or GTE export via ONNX, so you're never locked to the default model.
How it works
Crowkis swaps in any sentence-transformers MiniLM or GTE export via ONNX, so you're never locked to the default model. 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.
Why it matters
Repetitive LLM workloads are where the money is, and semantic caching can cut costs up to 60-70% on repetitive workloads. Bring your own embedder / reranker 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. It's one self-hosted binary, Redis-compatible, free to run.
Infrastructure earns the critical path one boring, verifiable feature at a time.