HiddenLayer vs Patronus AI

Both compete in Red-Teaming & AI Security. HiddenLayer positions itself as “AI detection and response plus red teaming to protect machine learning models and AI products”, while Patronus AIleads with “Automated evaluation, guardrails, and judges for LLM and agent reliability”. The table below compares what each publishes.

Where HiddenLayer pulls ahead

Enterprises and regulated organizations needing runtime protection and detection for production ML models

Where Patronus AI pulls ahead

Publishes support for NIST AI RMF, which HiddenLayer does not. ML and product teams that need automated, research-grade evaluation plus guardrails to ship reliable LLM apps

	HiddenLayer	Patronus AI
Positioning	AI detection and response plus red teaming to protect machine learning models and AI products	Automated evaluation, guardrails, and judges for LLM and agent reliability
Category	Red-Teaming & AI Security	Red-Teaming & AI Security
Frameworks	None published	NIST AI RMF
Deployment	SaaS, On-prem, API	SaaS, API
Built for	Security, Data Science / ML, Risk	Data Science / ML, Risk, Compliance
Founded	2022	2023
Headquarters	Austin, Texas, USA	San Francisco, California, USA
Ownership	Private (VC-backed)	Independent, venture-backed
Funding	$56M total; $50M Series A in 2023 led by M12 and Moore Strategic Ventures, with Booz Allen, IBM and Capital One Ventures	$17M Series A (2024) led by Notable Capital, with Lightspeed and Datadog (~$20M total); subsequent Series B reported
Pricing	Not published	Commercial SaaS / usage-based; some open evaluators and models available
Key capabilities	Machine Learning Detection and Response (MLDR) Model scanning Automated red teaming Real-time attack detection AI risk and governance reporting Response actions (alert, isolate, deceive)	Automated LLM evaluation and benchmarking Adversarial test-case generation Lynx hallucination detection and judge models Percival agent debugging Runtime guardrails PII, safety and compliance checks
Integrations	Not published	OpenAI, Anthropic, Bifrost gateway, Common ML/LLM stacks via API
Notable customers	None published	None published
Best for	Enterprises and regulated organizations needing runtime protection and detection for production ML models	ML and product teams that need automated, research-grade evaluation plus guardrails to ship reliable LLM apps
Limitations	Historically ML-model centric; buyers seeking full agentic AI governance or compliance certification may need complementary tooling.	More an evaluation/observability platform than a hardened security firewall; deepest value requires building evaluation into workflows; younger company still expanding enterprise features

Which should you shortlist?

Choose HiddenLayer if enterprises and regulated organizations needing runtime protection and detection for production ML models

Choose Patronus AI if mL and product teams that need automated, research-grade evaluation plus guardrails to ship reliable LLM apps

Neither is a substitute for a governance program. Whichever you pick, you still need people who can define the policies the tool enforces.

Full HiddenLayer profile →Full Patronus AI profile →

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