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Pricing sourced from Patronus AI · Last verified March 2026
Patronus's hallucination detection models are trained specifically for this task and consistently outperform general-purpose LLMs on hallucination benchmarks. Accuracy varies by domain and context length, but the system provides confidence scores to help calibrate trust in detections.
Yes, you can define custom evaluators using natural language descriptions or code-based scoring functions. This allows evaluation of domain-specific criteria like legal compliance, medical accuracy, or brand voice consistency.
Patronus guardrails are optimized for low latency, typically adding 50-200ms depending on the checks enabled. For most interactive applications this is acceptable, and guardrails can be configured to run asynchronously for non-blocking use cases.
Yes, Patronus provides CLI tools and API endpoints for running evaluations in CI/CD pipelines. You can set quality gates that fail deployments when evaluation scores fall below configured thresholds.
AI builders and operators use Patronus AI to streamline their workflow.
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