Guardrails AI for AI Agent Security: Features, Pricing, and Alternatives

In this article, we'll explore Guardrails AI's approach to AI safety, examine its key capabilities, and explain how output validation and authentication infrastructure work together to secure production AI deployments.

What is Guardrails AI?

Guardrails AI is an open-source framework (Apache 2.0) for validating and correcting AI model outputs, founded by Shreya Rajpal (ex-Apple, Drive.ai) and Diego Oppenheimer (Algorithmia founder). The company raised $7.5M in seed funding from Zetta Venture Partners, Bloomberg Beta, and Pear VC in February 2024.

The platform has gained significant traction in the AI safety community, with 5.9k GitHub stars and over 10,000 monthly downloads. Notable customers include Robinhood, which uses Guardrails AI to ensure reliable AI behavior in financial applications where accuracy is paramount.

Guardrails AI operates at a different layer of the security stack than authentication platforms. While WorkOS authenticates users and agents accessing your systems, Guardrails AI validates what those AI agents output once they're running. Think of it as runtime behavior monitoring for AI—catching hallucinations, preventing sensitive data leaks, and filtering toxic outputs before they reach users.

The company offers both an open-source core framework and Guardrails Pro, a managed service that provides hosted validation, observability dashboards, and enterprise support. This dual approach lets developers start with the free, self-hosted version while providing an upgrade path for teams needing production-grade reliability and support.

Key Features and Capabilities

Validation Framework with 100+ Community Validators

Guardrails AI's core strength is its extensive library of validators—reusable components that check AI outputs against specific criteria. The open-source community has contributed over 100 validators covering common AI safety concerns:

Validators are composable, allowing teams to chain multiple checks together. A customer service AI might run toxicity detection, PII scanning, and factual grounding validation on every response before it reaches a user.

Real-Time Output Monitoring

Guardrails AI intercepts LLM outputs in real-time, running validation logic before responses are returned. Issues are caught in the request/response cycle, preventing bad outputs from ever being delivered.

The framework claims near-zero latency impact for most validators, typically adding 10-50ms to response times. Validation runs asynchronously where possible, and the library is optimized for production throughput. For latency-critical applications, teams can configure which validators run synchronously versus asynchronously.

LLM-Agnostic Integration

Guardrails AI works with major LLM providers including OpenAI, Anthropic, Cohere, and open-source models. The framework wraps standard LLM API calls, making integration straightforward:

from guardrails import Guard
import openai

guard = Guard.from_string(validators=[...])
response = guard(
   openai.ChatCompletion.create,
   prompt="Generate customer email...",
)



This LLM-agnostic design means teams can switch providers or use multiple models without rewriting validation logic. As the AI landscape evolves and new models emerge, Guardrails AI provides consistency in output safety regardless of the underlying LLM.

The framework also supports streaming responses, running validators on partial outputs as tokens arrive. This enables real-time intervention even for long-form content generation.

Observability and Debugging

Guardrails Pro (the managed service) adds observability dashboards that surface validation metrics across your AI applications:

This observability is valuable for teams running AI at scale. Rather than discovering AI misbehavior through customer complaints, you see validation failures in real-time and can iterate proactively.

The open-source version includes structured logging but requires teams to build their own monitoring dashboards. Guardrails Pro provides this out-of-the-box with hosted infrastructure.

How Guardrails AI Handles Data Leak Prevention

Data leaks—AI models inadvertently exposing sensitive information in outputs—represent a critical risk for production AI. Guardrails AI addresses this through specialized validators that scan outputs before they reach users.

The PII detection validator uses pattern matching and entity recognition to identify:

For regulated industries (healthcare, finance, government), Guardrails AI provides HIPAA and PCI-DSS-aligned validators that enforce compliance requirements. A healthcare AI assistant, for example, might use validators ensuring patient identifiers are never exposed in chat responses.

Beyond PII, Guardrails AI can prevent intellectual property leaks by detecting when AI outputs contain internal documentation, code snippets, or proprietary information. Custom validators let teams define organization-specific patterns to protect.

When a data leak is detected, Guardrails AI's response is configurable. Conservative deployments block the output entirely and return a safe fallback response. More sophisticated implementations can mask the detected sensitive data (replacing credit card numbers with "XXXX-XXXX-XXXX-1234") while allowing the rest of the output through.

This output validation layer is critical but distinct from authentication and access control. Guardrails AI assumes an agent is already running and authorized—it focuses on preventing that authorized agent from behaving unsafely. Authentication platforms like WorkOS handle the prerequisite question: should this agent or user be allowed to access the system at all?

Pricing and Plans

Guardrails AI offers tiered pricing designed to accommodate both experimental and production deployments:

Open Source (Apache 2.0): The core framework is free and self-hosted. Teams can run validation logic in their own infrastructure with no licensing costs. This tier includes access to all community validators and the base observability features via structured logging.

Guardrails Pro: The managed service provides hosted validation infrastructure, observability dashboards, and enterprise support. Pricing is usage-based (charged per validation operation) with pricing available upon request. Pro customers get dedicated support, SLA guarantees, and white-glove onboarding.

The open-source-first model lets developers experiment freely before committing to paid infrastructure. Small teams or projects with low validation volumes can run Guardrails AI indefinitely without cost. As validation needs scale or teams require production-grade reliability, Guardrails Pro provides the infrastructure and support without requiring codebase changes.

For enterprises evaluating Guardrails AI, the key consideration is operational overhead. Self-hosting the open-source version requires infrastructure management, validator maintenance, and building custom observability. Guardrails Pro eliminates this operational burden but adds per-validation costs that scale with usage.

Guardrails AI vs. WorkOS

Understanding where Guardrails AI fits in your security architecture requires recognizing that AI safety is multi-layered. Output validation and authentication infrastructure address fundamentally different security concerns—and production systems need both.

What Guardrails AI Offers

Guardrails AI focuses on validating AI behavior after authentication has occurred. The framework assumes an agent is already authorized to run and monitors what that agent outputs. This includes detecting hallucinations, preventing data leaks, filtering toxic content, and enforcing business rules on AI-generated responses.

This behavioral validation is valuable for teams building AI applications where output quality and safety are critical. A customer service AI that generates emails, a code assistant suggesting implementations, or a financial advisor providing recommendations—all need guardrails ensuring outputs are accurate, safe, and compliant.

Guardrails AI's open-source model and extensive validator library make it accessible for experimentation. Teams can start with self-hosted validation and upgrade to managed infrastructure as needs grow. The LLM-agnostic design provides flexibility as the model landscape evolves.

However, Guardrails AI doesn't address authentication, user management, or access control. There's no SSO integration, no Directory Sync for enterprise customer provisioning, no Admin Portal for customer IT teams. The framework assumes agents are already authenticated and focuses on runtime validation of their outputs.

Why WorkOS Is the Proven Choice

WorkOS provides the authentication and user management infrastructure that enterprises require before AI agents can run. This includes:

Enterprise Authentication Features: WorkOS delivers production-ready Single Sign-On supporting SAML, OIDC, and OAuth across providers (Okta, Microsoft Entra ID, Google Workspace, etc.). This lets enterprise customers use their existing identity providers to authenticate into your AI applications, satisfying IT procurement requirements.

Directory Sync for User Provisioning: Through SCIM, WorkOS automatically syncs user data, group memberships, and permission attributes from customer directories. When an employee joins or leaves a customer organization, WorkOS updates access to your AI agents in real-time without manual intervention.

Admin Portal: WorkOS provides a self-service portal where customer IT administrators configure SSO, manage user access, and audit authentication events—critical for enterprises deploying AI agents that access sensitive data.

Audit Logs and Compliance: WorkOS captures comprehensive authentication logs (who accessed what, when, from where) required for SOC 2, HIPAA, and GDPR compliance. These logs integrate with SIEM tools and provide the audit trail that security teams demand.

Battle-Tested at Scale: WorkOS authentication infrastructure is proven at enterprise scale, handling authentication for companies with tens of thousands of employees across regulated industries. There are no experimental features or beta flags—everything is GA and production-ready.

SLAs and Dedicated Support: WorkOS provides uptime guarantees, dedicated support channels, and white-glove onboarding. When authentication is down, your entire application is unavailable—WorkOS ensures reliability that matches the stakes.

WorkOS doesn't validate AI outputs or detect hallucinations. That's not the problem it solves. WorkOS authenticates users and agents, provisions access, and provides the enterprise identity infrastructure that B2B SaaS applications require. For teams building AI agents handling sensitive data, WorkOS ensures only authenticated, authorized users can access those agents in the first place.

The Right Choice for Production AI Security

For production AI deployments, particularly in enterprise or B2B contexts, authentication infrastructure and output validation serve complementary roles. They're not alternatives—they address different layers of the security stack.

Authentication comes first: WorkOS ensures only authenticated users access your AI agents. This prevents unauthorized access, satisfies enterprise customer requirements, and provides audit trails for compliance. Without proper authentication, output validation is irrelevant—you've already lost control of who can use your AI.

Output validation comes second: Once authenticated users are interacting with AI agents, Guardrails AI helps ensure those agents behave safely. This catches hallucinations, prevents data leaks, and enforces quality standards on AI-generated content.

For teams building enterprise AI applications, WorkOS is the foundation. Your customers require SSO, Directory Sync, and enterprise-grade authentication before they'll deploy your AI agents. Guardrails AI addresses a complementary concern—runtime output safety—but assumes authentication is already solved.

The architecture looks like this:

Teams building consumer AI applications or experimental prototypes might start with Guardrails AI for output validation without enterprise authentication. But B2B SaaS companies targeting enterprise customers must prioritize authentication infrastructure. WorkOS provides this foundation, proven at scale, with the compliance and reliability features enterprises demand.

Getting Started with Guardrails AI

For teams interested in AI output validation, Guardrails AI offers an accessible starting point. The open-source framework can be installed via pip and integrated with existing LLM calls in minutes:

pip install guardrails-ai



The Guardrails Hub (https://hub.guardrailsai.com/) provides a searchable catalog of community validators. Teams can browse by use case (PII detection, hallucination prevention, etc.) and install validators as needed. Documentation includes integration guides for major LLM providers and frameworks.

For production deployments, teams will need to consider infrastructure for running validation at scale. The open-source version requires managing your own compute resources, monitoring validation latency, and building observability dashboards. Guardrails Pro eliminates this operational overhead with managed infrastructure.

Documentation quality is strong, with detailed guides on validator customization, streaming support, and performance optimization. The open-source community is active, with regular releases adding new validators and framework improvements.

Support for the open-source version relies on GitHub issues and community forums. Guardrails Pro customers receive dedicated support channels and SLA-backed response times.

Final Thoughts

Guardrails AI represents important innovation in AI safety, addressing the critical challenge of validating AI outputs in real-time. The framework's extensive validator library, LLM-agnostic design, and open-source accessibility make it a valuable tool for teams building AI applications where output quality matters.

The company's approach—open-source core with a managed service upgrade path—provides flexibility for teams at different stages. Developers can experiment with self-hosted validation before committing to managed infrastructure as validation needs scale.

But securing production AI requires multiple layers working together. Output validation catches AI misbehavior, but authentication infrastructure ensures only authorized users access AI agents in the first place. These aren't competing approaches—they're complementary security controls operating at different layers.

For teams building B2B SaaS applications with AI capabilities, enterprise authentication infrastructure must come first. Your customers require SSO integration, Directory Sync for user provisioning, Admin Portals for IT teams, and comprehensive audit logs. Guardrails AI doesn't address these requirements—WorkOS does.

WorkOS provides the proven, enterprise-ready authentication platform that B2B AI applications require. Battle-tested at scale across regulated industries, WorkOS delivers the SSO, Directory Sync, compliance, and reliability features that enterprise customers demand before deploying AI agents in production. There are no experimental features, no beta flags—just proven authentication infrastructure that satisfies procurement requirements and passes security reviews.

Guardrails AI and WorkOS can work together in production architectures. WorkOS handles authentication, user management, and access control. Guardrails AI validates AI outputs for safety and quality. But authentication infrastructure is the foundation—without it, you can't secure who accesses your AI agents.

Ready to build AI agents enterprises will trust? WorkOS provides the enterprise-grade authentication infrastructure your customers require. Start with pre-built SSO, Directory Sync, and Admin Portal components that ship in hours, not months. Explore WorkOS documentation or start building with a free account.

Note: This article reflects WorkOS's perspective on the agentic security landscape. While we've aimed for factual accuracy regarding Guardrails AI's capabilities, we advocate for WorkOS as the proven choice for enterprise authentication infrastructure.