Eclusia
Freemium
Legal professionals handling confidential cases
Patronus Scanner
FreemiumAPISelf-Hosted
Shipping an LLM feature and discovering hallucinations through customer complaints is the kind of thing that ends quarters — Patronus AI exists to catch that failure before it reaches production.
$10 / 1k small evaluator API calls; $20 / 1k large; Enterprise custom · Free tier: Last 2 weeks access; 2 projects; 5 experiments/project; $10 free credits
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Summary
Patronus AI positions itself as evaluation and monitoring infrastructure for production LLM applications, with its Lynx model — the vendor states it beats GPT-4 on hallucination detection tasks — as the core detection engine. The platform covers automated evaluation via API, multimodal image-text alignment checks, and experiment tracking for prompt and model optimization. Self-hosted deployment is available for teams with data residency requirements, which means regulated industries can run evaluations without shipping sensitive outputs to a third-party endpoint. The free tier gets you started, but the on-premises and enterprise-grade monitoring features are paid-only. Teams operating at high evaluation volume will hit throughput questions that the docs do not answer publicly.
Bottom line: Patronus is a defensible choice for an AI team that needs automated hallucination scoring baked into a deployment pipeline — but teams requiring full audit trails, SLA-backed monitoring, and deep integration with existing observability stacks will find the enterprise tier opaque and may move toward platforms with more public documentation on production limits.
Pricing Plans
Usage-Based- Price
- $10 / 1k small evaluator API calls; $20 / 1k large; Enterprise custom
- Free Tier
- Last 2 weeks access; 2 projects; 5 experiments/project; $10 free credits
Developer
Free
Access to last 2 weeks data; 2 projects; 5 experiments per project; $10 free credits
- Patronus Experiments
- Patronus Logs
- Patronus Traces
- Unlimited Datasets and Comparisons
Enterprise
Custom
Everything unlimited; on-prem / dedicated VPC; SSO; custom data retention
- Premium Platform Features
- Higher rate limits
- AI Services: custom eval fine tuning
View full pricing on patronus.ai →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
- Lynx hallucination detection model is purpose-trained for detection rather than generation, so you avoid the circularity of using GPT-4 to evaluate GPT-4 — the vendor states Lynx outperforms GPT-4 on hallucination tasks, which means false negatives that would slip past a general-purpose judge get caught here.
- API-first evaluation surface means hallucination scoring and safety checks can be wired directly into a deployment pipeline or CI step, so regressions surface before a bad output reaches a user rather than after.
- Self-hosted deployment option means teams in regulated industries — finance, healthcare — can run evaluations without routing sensitive model outputs through a third-party cloud endpoint, which unblocks data residency requirements that would otherwise kill adoption.
- Published benchmarks (FinanceBench, BLUR, GLIDER) ground the evaluation methodology in peer-reviewable research, so compliance and legal teams have citable evidence for why the scoring criteria are trustworthy — not just a vendor's assertion.
- Multimodal evaluation support covers image-text alignment, so teams shipping vision-language features do not need a separate tool to catch outputs where the generated text contradicts or ignores the image context.
Cons
- The company's public research focus has shifted substantially toward Digital World Models and agent simulation infrastructure; teams evaluating a long-term vendor relationship on evaluation tooling face roadmap risk when the vendor's flagship research agenda is somewhere else entirely.
- On-premises configuration is enterprise-gated and not publicly documented, which means a regulated-industry team cannot estimate integration effort before entering a sales process — procurement cycles lengthen, and teams with a sprint deadline will reach for a competitor with public self-hosting docs instead.
- Evaluation throughput limits, latency at scale, and SLA terms for the production monitoring surface are not stated in public-facing documentation; teams that discover a ceiling mid-deployment have no public benchmark to plan against, and the workaround is contacting sales before the spike arrives rather than after.
- Teams that need a unified observability platform — traces, logs, evals, and alerting in one surface — will find Patronus covers the evaluation layer but not the broader stack, forcing a second tool for tracing and a third for alerting; at that integration cost, some teams consolidate onto an observability-first platform that includes evaluation as a feature rather than building Patronus into an existing stack.
About
- Platforms
- Web, API, Python SDK, TypeScript SDK
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- September 20, 2026
Best For
Who it's for
- AI teams shipping production LLM applications
- Enterprises requiring safety and compliance checks
- Developers needing automated evaluation APIs
What it does well
- Detecting hallucinations in LLM outputs
- Monitoring AI agents in production
- Running experiments to optimize prompts and models
- Evaluating multimodal systems for image-text alignment
Integrations
Frequently Asked Questions
- Is Patronus Scanner free?
- Patronus Scanner offers a free option alongside paid upgrades. Pricing: $10 / 1k small evaluator API calls; $20 / 1k large; Enterprise custom. Check the current allowances and terms before choosing a plan.
- Does Patronus Scanner have an API?
- Yes. Patronus Scanner exposes a developer API. See the official documentation at https://patronus.ai for details.
- Can I self-host Patronus Scanner?
- Yes. Patronus Scanner supports self-hosting on your own infrastructure.
- What platforms does Patronus Scanner support?
- Patronus Scanner is available on: Web, API, Python SDK, TypeScript SDK.
Curated lists that include this category
Shipping an LLM feature and learning about hallucinations only through customer complaints can derail a quarter. Patronus Scanner supplies evaluation and monitoring infrastructure to surface those failures earlier.
What it provides
The Lynx model acts as the core detection engine. The vendor states it outperforms GPT-4 on hallucination tasks. The platform supplies automated evaluation via API, multimodal image-text alignment checks, and experiment tracking for prompts and models. Self-hosted deployment supports data-residency needs so regulated teams avoid sending outputs to third-party endpoints. Platforms include web, API, Python SDK, and TypeScript SDK, with integrations for AWS, Databricks, and MongoDB.
Pricing
Usage-based pricing lists $10 per 1k small evaluator API calls and $20 per 1k large calls, with enterprise custom options. The free tier covers the last 2 weeks of access, 2 projects, 5 experiments per project, and $10 in credits.
Trade-offs
API-first design lets teams wire hallucination scoring into deployment or CI steps so regressions appear before users see them. Purpose-trained Lynx reduces reliance on general models for evaluation. Self-hosting fits compliance requirements. However, the company’s research emphasis has shifted toward Digital World Models, creating roadmap uncertainty for long-term evaluation tooling. On-premises configuration stays enterprise-gated without public documentation, lengthening procurement for teams on tight timelines.
Who it is for / who should skip it
It suits AI teams shipping production LLM applications and enterprises that need safety checks plus self-hosting. Teams wanting a vendor whose primary research stays fixed on evaluation tooling or needing public self-host docs before sales engagement should look elsewhere.
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