As LLM-powered applications move into production — and as AI agents take on more consequential tasks like browsing the web, writing and executing code, and interacting with external services — safety moderation has quietly become one of the most operationally expensive parts of the stack.
Most developers who’ve deployed a production LLM system know the problem: you need to evaluate every user prompt before it reaches the model, and every model response before it reaches the user. That means your guardrail model runs on every single request, at every turn of a conversation. The guardrail latency compounds. The cost compounds. And the current generation of open-source guardrail models — LlamaGuard4 (12B), WildGuard (7B), ShieldGemma (27B), NemoGuard (8B) — are all decoder-only models with billions of parameters, built for flexibility but not for speed.
Fastino Labs released GLiGuard, a 300 million parameter open-source safety moderation model designed to address this specific problem. GLiGuard evaluates multiple safety dimensions in a single pass, and across nine safety benchmarks, its accuracy matches or exceeds models that are 23 to 90 times its size while running up to 16 times faster.
