Patronus AI
Automated evaluation and monitoring platform for testing and optimizing language models and AI agents
- Also known as
- patronus-ai
Available worldwide · Popular in: US
What is Patronus AI?
A managed platform that evaluates language models and AI agents using specialized scoring models, provides adversarial test datasets, monitors performance in production, and enables side-by-side comparison and debugging of AI systems at scale.
Patronus AI pricing
Plans, per-tier features and add-ons, dated and linked to live pricing. Pricing changes often; always verify at source before you rely on it.
Free tiers available ($0/mo), paid plan from $25/mo, API pricing from $10/1k calls
Developer
FreeFree tier for API evaluation
- $10 in free credits
- Patronus Experiments (last 2 weeks)
- Patronus Comparisons
- Patronus Datasets
- Optional Patronus API
Individual
FreeFree platform tier
- pages
- 20
- 20 pages
- Customizable options
- Secure data storage
- Email support
Base
Professional plan
- pages
- 600
- 600 pages
- Page add-ons available
- 24/7 customer support
- Analytics and reporting
- Account Management
Enterprise
Contact salesCustom enterprise solution with security and AI services
- pages
- Unlimited
- Unlimited pages and runs
- On-premise or dedicated VPC deployment
- Custom data retention
- SSO
- Premium Platform Features (Evaluation Runs, webhooks)
- Premium API Features (higher rate limits, volume discounts)
- Custom eval model fine-tuning
- Eval dataset generation
Add-ons
- API - Small Evaluator$10 per 1000 calls
- API - Large Evaluator$20 per 1000 calls
- API - Eval Explanations$20 per 1000 calls
What Patronus AI does
The capabilities that matter for ai evals testing, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- Architecture model
- Managed enterprise saas
- LLM as a judge prompt grading framework
- ✓
- Specialized rag metrics faithfulness context relevance
- ✓
- Deterministic regex and json schema assertions
- -
- Synthetic test dataset generation from documents
- -
- Ci cd github actions pipeline blocking gates
- -
- Red teaming and adversarial vulnerability scanning
- ✓
- Multi model side by side ab regression testing
- ✓
- Human in the loop hitl annotation UI
- -
- Dashboard analytics for metric drift over time
- ✓
- SOC2 type ii
- -
- Mit or apache permissive oss license
- ✗
- Pricing model
- Per test execution cloud
Platform & deployment
Independently observed- Web
- Cloud / SaaS
- Hybrid
- On-premise
Integrations (5)
Independently observed- Smolagents
- OpenAI Agents
- Pydantic
- CrewAI
- Langchain
Patronus AI alternatives
Other ai evals testing we track, ranked by the same independent score.
Compare Patronus AI
Side by side against other ai evals testing, attribute by attribute, with a source on every value.
The Vioscale score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for Patronus AI, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Pricing transparency | 100 | 0.08 | 8.4 | ✓ |
| Capabilities | 75 | 0.05 | 3.6 | ✓ |
| Price level | 50 | 0.05 | 2.6 | ✓ |
| Integrations | 22 | 0.04 | 0.9 | ✓ |
| Reliability | 0 | 0.07 | 0.0 | - |
| Security posture | 5 | 0.07 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | Pricing model: per_test_execution_cloud · Architecture model: managed_enterprise_saas · Mit or apache permissive oss license: No · Llm as a judge prompt grading framework: Yes · Dashboard analytics for metric drift over time: Yes · Multi model side by side ab regression testing: Yes | mediumsource · 2026-08-21 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 5 | mediumsource · 2026-08-21 · 60% |
Market
| Attribute | Value | Evidence |
|---|---|---|
| Availability | PrimaryMarkets: … · AvailabilityScope: global · AvailableCountries: … · NotAvailableCountries: … | mediumsource · 2026-08-21 · 50% |
Pricing
Security
| Attribute | Value | Evidence |
|---|---|---|
| Gdpr | Yes | highsource · 2026-08-21 · 75% |