Patronus AI
Patronus AI provides an LLM evaluation API that scores model outputs for correctness, relevance, hallucination, toxicity, and custom criteria, enabling agents and CI pipelines to automatically assess and gate LLM response quality.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Evaluation requests include LLM output content that is sent to Patronus servers — ensure data handling policies are acceptable before sending PII or proprietary content. SOC2 certified. TLS enforced on all endpoints.
⚡ Reliability
Best When
An agent or pipeline needs automated, programmatic quality gates on LLM outputs with explainable scores and customizable evaluation criteria across large volumes of responses.
Avoid When
You only need basic output filtering (profanity, PII) without nuanced relevance or correctness scoring — lighter-weight guardrail libraries like Guardrails AI may suffice.
Use Cases
- • Automatically evaluate RAG pipeline outputs for hallucination and faithfulness to retrieved context before returning answers to users
- • Run batch evaluations across a test dataset to measure regression in LLM quality when upgrading models or changing prompts
- • Integrate Patronus into a CI/CD pipeline to block deployments when evaluated LLM quality drops below a defined threshold
- • Score customer-facing LLM responses for toxicity and policy violations in real-time to trigger human review workflows
- • Evaluate retrieval quality (context relevance, answer completeness) separately from generation quality to isolate failure modes in RAG systems
Not For
- • Real-time user-facing inference — evaluation adds latency and is better suited for async quality assurance pipelines
- • Replacing human expert review for high-stakes domains (medical, legal) where nuanced judgment is required beyond automated scoring
- • Evaluating non-text modalities like images or audio — Patronus is focused on text-based LLM outputs
Interface
Authentication
API key authentication via Authorization Bearer header. Keys are scoped to a project/organization. No OAuth flow required for programmatic access. Key rotation is supported through the dashboard.
Pricing
Pricing scales with number of evaluations and evaluator type; some evaluators (LLM-based) cost more than heuristic ones. Free tier suitable for development and small-scale testing.
Agent Metadata
Known Gotchas
- ⚠ LLM-based evaluators (hallucination, relevance) add 500-2000ms per call — avoid calling synchronously in user-facing response paths
- ⚠ Evaluation criteria must be defined in the Patronus dashboard before being referenced by name in the API; referencing undefined criteria returns a vague 400 error
- ⚠ Batch evaluation jobs are asynchronous — agents must poll job status endpoints rather than blocking on the initial request
- ⚠ Scores are returned as floats between 0 and 1 but threshold semantics differ per evaluator type; read evaluator-specific docs before writing threshold logic
- ⚠ The Python SDK does not support async/await natively as of early 2025 — use a thread executor for non-blocking calls in async agent frameworks
Alternatives
Full Evaluation Report
Detailed scoring breakdown, competitive positioning, security analysis, and improvement recommendations for Patronus AI.
Scores are editorial opinions as of 2026-03-06.