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Patronus AI Pricing & Plans 2026

Complete pricing guide for Patronus AI. Compare all plans, analyze costs, and find the perfect tier for your needs.

Try Patronus AI Free →Compare Plans ↓

Not sure if free is enough? See our Free vs Paid comparison →
Still deciding? Read our full verdict on whether Patronus AI is worth it →

🆓Free Tier Available
💎3 Paid Plans
⚡No Setup Fees

Choose Your Plan

Developer

$0

mo

Up to 2 projects5 experiments per project2-week data retention for logs and tracesUnlimited comparisons and dataset access$10 in free Patronus API credits

  • ✓Core evaluation workflows
  • ✓Datasets and comparisons
  • ✓Developer access to Patronus API credits
Start Free Trial →
Most Popular

API Usage

$10-$20 per 1,000 calls

mo

$10 per 1,000 small evaluator API calls$20 per 1,000 large evaluator API calls$10 per 1,000 evaluation explanations

  • ✓Small evaluator API calls
  • ✓Large evaluator API calls
  • ✓Evaluation explanations
Start Free Trial →

Enterprise

Custom

mo

Custom usage limitsCustom data retentionHigher API rate limitsVolume discounts availableContact sales for contract terms

  • ✓Unlimited access to platform features
  • ✓Enterprise deployment and data-control options subject to contract
  • ✓SSO
  • ✓Webhooks
  • ✓Custom evaluator model fine-tuning
  • ✓Dataset generation services
Contact Sales →

Pricing sourced from Patronus AI · Last verified March 2026

Feature Comparison

FeaturesDeveloperAPI UsageEnterprise
Core evaluation workflows✓✓✓
Datasets and comparisons✓✓✓
Developer access to Patronus API credits✓✓✓
Small evaluator API calls—✓✓
Large evaluator API calls—✓✓
Evaluation explanations—✓✓
Unlimited access to platform features——✓
Enterprise deployment and data-control options subject to contract——✓
SSO——✓
Webhooks——✓
Custom evaluator model fine-tuning——✓
Dataset generation services——✓

Is Patronus AI Worth It?

✅ Why Choose Patronus AI

  • • Purpose-built evaluator models such as Lynx and Glider make Patronus more specialized than using a generic LLM judge for every quality check
  • • Lynx is described as open weights, giving teams an option to inspect the hallucination-detection model rather than relying only on a closed hosted evaluator
  • • Glider returns both scores and natural-language critiques, which helps reviewers understand why a response passed or failed instead of only seeing a numeric grade
  • • Percival is positioned for agent failure localization, which is valuable when debugging multi-step workflows where the final answer alone does not reveal the root cause
  • • The platform spans 3 important production needs in one workflow: evaluation and quality controls, security and governance, and observability
  • • Compared to the 3 listed alternatives in this record, Patronus is especially strong for teams that need explainable evaluation outputs

⚠️ Consider This

  • • Self-serve subscription pricing is limited; teams still need to contact sales for enterprise contract pricing and deployment terms
  • • The platform is likely heavier than lightweight CI-only evaluation tools for small teams that only need prompt regression tests
  • • Advanced capabilities such as Percival and custom evaluator training may require higher-tier or enterprise access
  • • Model-based evaluation still requires representative datasets; poor test coverage can produce misleading confidence even with strong evaluator models
  • • Teams in specialized domains may need calibration and human review because hallucination detection can miss subtle or context-dependent factual errors

What Users Say About Patronus AI

👍 What Users Love

  • ✓Purpose-built evaluator models such as Lynx and Glider make Patronus more specialized than using a generic LLM judge for every quality check
  • ✓Lynx is described as open weights, giving teams an option to inspect the hallucination-detection model rather than relying only on a closed hosted evaluator
  • ✓Glider returns both scores and natural-language critiques, which helps reviewers understand why a response passed or failed instead of only seeing a numeric grade
  • ✓Percival is positioned for agent failure localization, which is valuable when debugging multi-step workflows where the final answer alone does not reveal the root cause
  • ✓The platform spans 3 important production needs in one workflow: evaluation and quality controls, security and governance, and observability
  • ✓Compared to the 3 listed alternatives in this record, Patronus is especially strong for teams that need explainable evaluation outputs

👎 Common Concerns

  • ⚠Self-serve subscription pricing is limited; teams still need to contact sales for enterprise contract pricing and deployment terms
  • ⚠The platform is likely heavier than lightweight CI-only evaluation tools for small teams that only need prompt regression tests
  • ⚠Advanced capabilities such as Percival and custom evaluator training may require higher-tier or enterprise access
  • ⚠Model-based evaluation still requires representative datasets; poor test coverage can produce misleading confidence even with strong evaluator models
  • ⚠Teams in specialized domains may need calibration and human review because hallucination detection can miss subtle or context-dependent factual errors

Pricing FAQ

What is Patronus AI best used for?

Patronus AI is best used for evaluating and governing production LLM, RAG, and agent systems. It is especially relevant when teams need hallucination detection, explainable LLM judges, red-teaming, guardrails, and observability in a single workflow. Based on our analysis of 870+ AI tools, Patronus is a stronger fit for enterprise AI safety and quality programs than for simple one-off prompt experiments.

How does Patronus AI detect hallucinations?

The current tool data identifies Lynx as Patronus AI's hallucination-detection model. Lynx is designed to evaluate whether model outputs are supported by the provided context, which is particularly important for RAG systems. Accuracy will still depend on the quality of the source context, the evaluation dataset, and the thresholds a team configures for its use case.

Can Patronus AI evaluate custom quality criteria?

Yes. Patronus supports custom evaluators for domain-specific checks, including natural-language criteria and code-based scoring functions according to the existing product data. This is useful for teams that need to evaluate legal compliance, medical safety language, brand voice, internal policy adherence, or other rules that generic evaluators will not understand reliably.

Does Patronus AI support CI/CD quality gates?

Yes. The current data states that Patronus provides CLI tools and API endpoints for running evaluations in CI/CD pipelines. Teams can configure pass/fail gates, such as blocking a deployment when hallucination rates exceed a defined threshold like 5% on a test set. This makes it useful for catching prompt, model, or retrieval regressions before they reach production users.

How transparent is Patronus AI pricing?

Patronus AI has a free Developer tier with up to 2 projects, 5 experiments per project, 2-week retention, unlimited comparisons and dataset access, and $10 in API credits. Paid API usage is listed at $10 per 1,000 small evaluator calls, $20 per 1,000 large evaluator calls, and $10 per 1,000 evaluation explanations. Enterprise pricing remains custom and requires contacting sales.

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More about Patronus AI

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