TypeSafe's Jev, a decision model designed for fast binary/graded judgments, outperforms GPT-5.4 nano as an LLM guardrail by 15-18x in speed and 12-14x in cost while catching the same policy violations. The article benchmarks both approaches on a dealership chatbot that screens customer inputs, agent replies, and tool calls against business rules.

• Jev returns typed probability distributions in ~100ms per call versus GPT-5.4 nano's ~1,900ms, and costs $0.042/M tokens versus $0.20-1.25/M, because it's built for structured decisions rather than token-by-token generation.
• Multiple guardrail boundaries (inbound message, draft reply, tool arguments) catch attacks that single-point checks miss—the second attack in the demo never jailbreaks the conversation but fails at the tool-call boundary where an input-only guardrail wouldn't see it.
• Policy rules should be split between model judgment (fuzzy calls like "does this sound like a binding commitment?") and deterministic code (hard rules like "price below floor"), not stuffed into prompts.