98% cheaper changes the economics
When a verdict costs about $0.15 per million tokens instead of $5.00, you stop rationing. Grade and guard every interaction, not a sample.
Purpose-built small language models that evaluate and guardrail every interaction, so you can watch 100% of traffic affordably.
A frontier judge can cost around $5.00 per million scanned tokens. At production volume that is too expensive to run on everything, so teams check 1 to 5% of interactions and hope the rest look the same. The ones you miss are the ones that hurt: the hallucination you never saw, the injection that slipped through.
A frontier judge can take about 3,200ms to return a verdict. By then the tool has already run, so the verdict can only describe what happened. It can audit, but it cannot guard. Luna returns a verdict in about 152ms, fast enough to block an action before the tool fires.
Luna returns a verdict as a single token, so scores are fast and repeatable. Run the same input twice and you get the same answer, which is what makes a model trustworthy as a judge. And because Luna runs and trains inside your VPC, your data never leaves your firewall.
Out-of-the-box metrics get you started, but your domain has its own definition of right. With Luna Studio, correct a handful of Luna's verdicts and it learns your standard, reaching around 95% accuracy on your own tasks. No labeling pipeline, no model training expertise. Review, correct, and ship a tuned metric, all from the studio.
Luna is a family of purpose-built small language models for evaluation. They grade and guard agent interactions quickly and cheaply, which is what makes it affordable to watch 100% of traffic instead of a sample.
A frontier judge is expensive and slow, so teams sample 1 to 5% of traffic and can only review actions after they run. Luna is built for one job, judging agent interactions, so it is cheap enough to grade everything and fast enough to guard in real time.
Luna runs evaluations and guardrails at roughly 98% lower cost than an LLM-as-judge approach, which changes the economics from rationing to running on every interaction.
Yes. Luna returns a verdict as a single token, so scores are deterministic: run the same input twice and you get the same answer, which is what makes a model trustworthy as a judge.
Yes. With Luna Studio you correct a handful of verdicts and it learns your standard, reaching around 95% accuracy on your own tasks, with no labeling pipeline or model-training expertise.
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