The AI guardrails market has exploded. A year ago, you had to build everything yourself. Now there are dozens of tools ranging from simple libraries to full-featured platforms. The challenge isn't finding options—it's choosing the right one for your needs.
I've evaluated most of these tools for various projects. Here's an honest breakdown of what's available, what each does well, and where each falls short.
Open Source Libraries
NVIDIA NeMo Guardrails
NVIDIA's toolkit for adding guardrails to LLM applications. Uses a domain-specific language (Colang) to define conversational flows and safety boundaries.
✓ Strengths
- Powerful conversation flow control
- Good documentation
- Active development
- Integrates with LangChain
✗ Limitations
- Learning curve for Colang
- Primarily conversational focus
- DIY deployment and scaling
- Limited out-of-box detectors
Best for: Teams with engineering capacity who want fine-grained conversation control.
Guardrails AI
Python framework for validating LLM outputs. Focuses on structural validation—ensuring outputs match expected schemas, types, and formats.
✓ Strengths
- Strong output validation
- Pydantic integration
- Good for structured outputs
- Easy to get started
✗ Limitations
- Less focus on security guardrails
- Limited input protection
- Not a complete solution
- Self-hosted only
Best for: Developers who need to ensure LLM outputs conform to specific formats.
LLM Guard
Security-focused toolkit with pre-built scanners for detecting various threats in LLM inputs and outputs.
✓ Strengths
- Security-first approach
- Multiple threat scanners
- PII detection included
- Active community
✗ Limitations
- Can add latency
- Needs tuning for accuracy
- No managed option
- Limited enterprise features
Best for: Teams prioritizing security who can manage their own infrastructure.
Microsoft Presidio
Data protection and PII detection toolkit. Not AI-guardrails-specific but commonly used for PII protection in LLM pipelines.
✓ Strengths
- Excellent PII detection
- Multiple languages
- Production-tested at scale
- Extensible recognizers
✗ Limitations
- Only PII—not a complete guardrails solution
- Needs integration work
- No prompt injection detection
- Self-managed
Best for: Organizations with strong PII requirements who need a specialized solution.
Enterprise Platforms
Prime AI Guardrails
Full-featured AI governance platform providing runtime guardrails, policy management, and compliance controls as a managed service.
✓ Strengths
- Complete solution (input, output, action)
- Sub-50ms latency
- Enterprise-ready (SOC 2, HIPAA)
- No ML expertise needed
- Centralized policy management
✗ Limitations
- Not free (enterprise pricing)
- Cloud-hosted (on-prem available)
- May be overkill for simple use cases
Best for: Enterprises needing production-ready guardrails with compliance requirements.
Arthur AI Shield
Real-time firewall for LLMs focused on detecting and blocking harmful inputs and outputs.
✓ Strengths
- Strong on hallucination detection
- Good observability features
- Model performance monitoring
✗ Limitations
- Less focus on governance
- Pricing can be high
- More ML-ops than security focus
Best for: Data science teams focused on model quality and reliability.
Lakera Guard
API-based service focused on prompt injection and security threats to LLMs.
✓ Strengths
- Excellent prompt injection detection
- Simple API integration
- Low latency
- Good security research team
✗ Limitations
- Narrower scope (security-focused)
- Less governance features
- Limited policy customization
Best for: Teams prioritizing security against adversarial attacks.
Cloud Provider Options
Amazon Bedrock Guardrails
Native guardrails feature within AWS Bedrock for models hosted on that platform.
✓ Strengths
- Native AWS integration
- Easy setup for Bedrock users
- Topic blocking and PII filters
✗ Limitations
- Only works with Bedrock
- Limited customization
- Basic feature set
- AWS lock-in
Best for: Teams already committed to AWS Bedrock who need basic guardrails.
Azure AI Content Safety
Microsoft's content moderation service that can be applied to AI outputs.
✓ Strengths
- Strong content moderation
- Multi-modal (text + image)
- Azure native
✗ Limitations
- Content safety focus only
- No prompt injection detection
- Azure ecosystem required
Best for: Azure shops needing content moderation.
Need Help Deciding?
The right choice depends on your specific requirements—compliance needs, technical capacity, deployment model, and budget. Talk to the Prime AI team for an honest assessment of what solution fits your situation.
How to Choose
Consider Open Source If:
- You have engineering capacity to build and maintain
- Your requirements are narrow and well-defined
- You need maximum customization
- Budget is a primary constraint
- You're comfortable managing your own infrastructure
Consider Enterprise Platforms If:
- You have compliance requirements (SOC 2, HIPAA, etc.)
- You need comprehensive coverage (not just one threat type)
- Time-to-value is important
- You want managed service with SLAs
- You're deploying at scale with multiple AI systems
Consider Cloud Provider Tools If:
- You're already committed to that cloud ecosystem
- Your needs are basic
- Simplicity trumps features
- You only use models from that provider
The Build vs. Buy Calculation
I've seen teams spend 6+ months building guardrails infrastructure that they could have deployed in a week with an enterprise platform. I've also seen teams pay for platforms they didn't need when a simple library would have sufficed.
Here's my rule of thumb: if you're protecting production AI that affects customers, revenue, or compliance—use a platform. If you're protecting internal experiments—open source is probably fine.
The cost of a guardrails platform is trivial compared to the cost of a single serious AI incident. Choose accordingly.