Guardrails AI vs Prompt Security

Both compete in Runtime Enforcement & Guardrails. Guardrails AI positions itself as “Open-source framework for validating and correcting LLM inputs and outputs”, while Prompt Securityleads with “Runtime security for enterprise GenAI usage, applications, and AI agents”. The table below compares what each publishes.

Where Guardrails AI pulls ahead

ML and application engineers who want an open, extensible, code-level validation layer for LLM apps

Where Prompt Security pulls ahead

Publishes support for NIST AI RMF, EU AI Act, GDPR, which Guardrails AI does not. Security teams governing sanctioned and shadow GenAI use while protecting homegrown AI apps and agents

	Guardrails AI	Prompt Security
Positioning	Open-source framework for validating and correcting LLM inputs and outputs	Runtime security for enterprise GenAI usage, applications, and AI agents
Category	Runtime Enforcement & Guardrails	Runtime Enforcement & Guardrails
Frameworks	None published	NIST AI RMF, EU AI Act, GDPR
Deployment	Open-source, API, SaaS	SaaS, On-prem, API
Built for	Data Science / ML, Security	Security, Risk, Compliance
Founded	2023	2023
Headquarters	Menlo Park / Seattle, USA	Tel Aviv, Israel
Ownership	Independent, venture-backed (open-source core framework)	Acquired by SentinelOne (announced August 2025, reported ~$250M)
Funding	$7.5M seed (2024), led by Zetta Venture Partners; Bloomberg Beta, Pear VC, GitHub Fund, Factory	~$23M total; investors include Hetz Ventures, Jump Capital, Ridge Ventures, Okta, F5
Pricing	Open-source (free); managed/enterprise offerings available	Enterprise licensing / subscription
Key capabilities	Guardrails Hub validator library Input and output validation guards Re-ask and auto-fix corrective actions Structured/JSON output enforcement PII, jailbreak and hallucination validators OpenAI-compatible Guardrails Server	Real-time GenAI usage visibility Policy-based prompt and data controls Sensitive-data leakage prevention Prompt-injection and jailbreak prevention MCP gateway security Model-agnostic coverage across major LLMs
Integrations	OpenAI, Anthropic, Hugging Face, LangChain, Any LLM via API	OpenAI, Anthropic, Google, Browser extensions / proxy, MCP servers, Enterprise SSO/SIEM
Notable customers	None published	None published
Best for	ML and application engineers who want an open, extensible, code-level validation layer for LLM apps	Security teams governing sanctioned and shadow GenAI use while protecting homegrown AI apps and agents
Limitations	Requires developer integration and configuration; validator quality varies across the community hub; smaller company with less turnkey enterprise governance tooling than large-vendor platforms	Being integrated into SentinelOne, so standalone roadmap may converge with that platform; primarily security-focused rather than a full GRC/compliance suite

Which should you shortlist?

Choose Guardrails AI if mL and application engineers who want an open, extensible, code-level validation layer for LLM apps

Choose Prompt Security if security teams governing sanctioned and shadow GenAI use while protecting homegrown AI apps and agents

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

Full Guardrails AI profile →Full Prompt Security profile →

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