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Compare guardrail approaches: NeMo Guardrails, Guardrails AI, safety classifier models, and custom code.
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| Approach | Strengths | Weaknesses | Best for |
|---|---|---|---|
| NeMo Guardrails | Dialogue flows, topic rails, integrates many checks | Learning curve (Colang), latency from extra LLM calls | Conversational bots needing controlled flows |
| Guardrails AI | Output validation, re-asking, validator hub | Less about dialogue control | Structured output quality, PII/toxicity validators |
| Safety classifier models | Fast, purpose-built harm detection, customisable taxonomies (some) | Generic categories; multilingual gaps; need thresholds | Input/output harm screening |
| Provider moderation APIs | Easy, maintained | External dependency; generic policies | Quick baseline moderation |
| Custom code / policy engines | Deterministic, auditable, precise business rules | You build and maintain it | Permissions, limits, schemas, domain rules |
Selection advice
- Start with code-level validation and permissions (non-negotiable).
- Add classifiers for harm categories relevant to your product.
- Use frameworks when you need dialogue control or many validators, and measure their latency and false positive rates.
- Evaluate every guardrail like a model: precision, recall, latency, cost.
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