Model-free online evals (CEVAL): how it works and when to use it
Model-free online evals (CEVAL), grades output with deterministic evaluators (toxicity, PII, relevance, JSON validity and more) and tracks the results over time, no LLM-judge. Here's how Crowkis does it and why it matters for cost and safety.
Production LLM traffic is deeply repetitive, and repetition is exactly what a bill is made of. Model-free online evals (CEVAL) is how Crowkis grades output with deterministic evaluators (toxicity, PII, relevance, JSON validity and more) and tracks the results over time, no LLM-judge.
In plain words. In plain words: model-free online evals (ceval) grades output with deterministic evaluators (toxicity, PII, relevance, JSON validity and more) and tracks the results over time, no LLM-judge.
How it works
Crowkis grades output with deterministic evaluators (toxicity, PII, relevance, JSON validity and more) and tracks the results over time, no LLM-judge. 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.
CEVAL relevance "q?" "answer" THRESHOLD 0.7Why it matters
Repetitive LLM workloads are where the money is, and semantic caching can cut costs up to 60-70% on repetitive workloads. Model-free online evals (CEVAL) 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.