LLM observability & evaluation · Patronus AI

Patronus AI

Evaluation-focused platform offering pre-built and custom evaluator models to score LLM outputs for accuracy, safety, and quality.

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Patronus AI is centered on evaluation rather than general tracing: it provides pre-built evaluator models (for hallucination detection, PII leakage, toxicity, and similar failure modes) alongside tools for building custom evaluators, running experiments against datasets, and comparing models or prompts. Logs and traces are supported but function primarily as the substrate evaluators run against, and the API is priced per evaluation call rather than per trace volume, distinguishing it from tracing-first tools. The free Developer tier includes a capped number of projects and experiments, with evaluator API usage billed per 1,000 calls on top (small vs. large evaluator models are priced differently) and starter credits included. Enterprise adds unlimited usage, on-prem/VPC deployment, custom evaluator fine-tuning, and synthetic eval-dataset generation.

At a glance

Vendor Patronus AI
Pricing model Usage-based
Free tier Yes
Deployment Cloud, Self-hosted
Open source No
Best for Teams that want ready-made, scored evaluators for safety and accuracy risks rather than building custom scoring from scratch.

Pricing

Free Developer tier includes limited projects/experiments and $10 in evaluator credits; evaluator API calls are billed per 1,000 calls (small vs. large models priced differently); Enterprise is custom with unlimited usage and on-prem/VPC deployment.

Plan Price Notes
Developer $0 2 projects, 5 experiments/project, $10 free evaluator credits, unlimited comparisons and datasets
Evaluator API add-on $10 per 1,000 small-model calls; $20 per 1,000 large-model calls Plus $10 per 1,000 eval explanations
Enterprise Custom Unlimited usage, on-prem/dedicated VPC, custom eval model fine-tuning, synthetic dataset generation

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Pre-built evaluator models for hallucination, PII leakage, and toxicity detection
  • Custom evaluator building
  • Dataset-based experiments and model/prompt comparison
  • Evaluation API priced per call
  • Synthetic eval-dataset generation on Enterprise
  • On-prem/VPC deployment on Enterprise

Integrations

Profile last reviewed September 21, 2026

Alternatives

DeepEval Open-source Python evaluation framework with 30+ research-backed metrics, with an optional paid platform (Confident AI) for CI/CD and hosted reporting. Ragas Open-source Python framework of reference-free metrics purpose-built for evaluating retrieval-augmented generation (RAG) pipelines. Galileo LLM observability and evaluation platform with real-time guardrails, aimed at monitoring agents and generative AI in production. Braintrust Evaluation-first platform for testing, scoring, and comparing LLM application changes, with production tracing built in.

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Terms to know

Data quality The degree to which data is accurate, complete, consistent and fit for the purpose it is being used for. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Hallucination When a language model generates confident, fluent output that is factually wrong or unsupported by its source material. LLM observability Monitoring a language model's live behavior in production, tracking its inputs, outputs, latency, cost, and failure patterns.

Related guides

How to choose an LLM observability tool Pick an LLM tool by whether you need tracing, scoring or a gateway first — most teams eventually need all three, rarely from one vendor.