Guardrails detect. Something still has to enforce.
If you are comparing Llama Guard, NeMo Guardrails, Guardrails AI, Lakera Guard or Bedrock Guardrails, this page is written to be useful even if you do not buy PRIOR. Those tools are detectors: they read a prompt or a response and return a verdict on prompt injection, jailbreaks or a policy breach. That is a real job and the field does it well. But a verdict is not an outcome. Something still has to make the decision stick inside the model, and that is the part PRIOR is built for: a self-hosted enforcement layer that works from inside the open-weight model you already run.
Every guardrail stack is really two jobs.
Keeping these apart is the single most useful thing you can do when evaluating this category, because most tools are strong at one and quiet about the other.
1. Detection
Decide whether a request or a response breaks one of your rules. This is a classification problem, it is well understood, and it is where nearly every product in this category competes. Llama Guard, Lakera Guard and the jailbreak rails in NeMo all live here.
2. Enforcement
Make the outcome stick once the decision is made. In most stacks this means blocking the response and returning an error, or asking the model to try again. The model itself was never changed, so it will happily produce the same output on the next attempt.
Where the gap is
Detection has many good options. Enforcement usually has exactly one: drop the response. That works until you need the model to answer correctly rather than not at all, and it does nothing about a model that was steered off course mid-generation.
PRIOR is the enforcement half, and it steers instead of blocking.
PRIOR also has a detector, and we are candid below about its limits. But the reason to look at PRIOR is the enforcer. When a request is flagged, PRIOR does not wait for the model to finish and then judge the text. It steers the model from inside as it generates, so the output that comes back is already on policy.
Filters read; PRIOR acts
A text filter sees the answer after the model has already decided what to say. By then the only remaining options are to allow it or destroy it. PRIOR changes what the model is doing while it is doing it, so a successful prompt injection does not become a successful response.
It catches a relent mid-answer
A refusal opens with a steered onset, and the model continues it in its own voice rather than being replaced by a fixed message. If the model starts to relent partway through, that is caught during generation and reverted. A filter only ever sees the finished response, so by the time it can object the model has already written the thing.
Bring a document, not a DSL
The usual path is writing policy as code: Colang, YAML, Python validators, regex. PRIOR builds its pack from your actual policy document, and the result is a plain .arbiter.json you can open and edit by hand.
What the other tools genuinely do better.
A comparison page that finds no reason to choose anything else is not a comparison page. Here is where we would point you elsewhere.
You are on a closed model
PRIOR works from inside open-weight models you run yourself. If your workload is on a hosted API you do not control, PRIOR cannot reach inside it, and a request-and-response filter is the correct architecture. Use one.
You want breadth of detection today
Mature detection suites ship large, actively maintained taxonomies covering categories well beyond ours, plus PII redaction and content moderation. Detector recall is our published open work, not our strong point.
You want free and open source
NeMo Guardrails, Guardrails AI, LLM Guard and Llama Guard are open source and self-hostable at no licence cost. PRIOR is commercial software. If budget is the binding constraint, start there honestly.
The numbers, with their conditions attached.
Every figure below names the model it was measured on and who judged it. Numbers without those two things are not worth comparing, ours included.
Enforcement holds
When the detector flags a request, the refusal holds essentially every time, scored by an independent HarmBench classifier from CAIS rather than by us. Primary judged model: Llama-3.1-8B-Instruct.
Steered attack success: 2 to 3%
HarmBench, 159 standard behaviors, judged by the independent cais/HarmBench classifier, on the 1.1.0 harm_veto path. Measured across a benchmark fleet of 12 models from 1B to 14B across six families.
Where it falls short
Detector recall is the limitation we publish rather than hide: the enforcer is strong, and helping the detector recognise more phrasings of the same bad request is the open work. The capabilities page states the rest, including what is out of scope by design.
Evaluate it against your own policy.
The trial ships the full capability set for 30 days, runs entirely on your hardware, and verifies its licence offline with no activation call. Point it at a certified open-weight build, hand it your policy document, and see whether the enforcement half holds up on your traffic rather than on our benchmark.