# Guardrails AI vs Guard — 40° South

> Guardrails AI validates LLM output in your code. Guard is managed compliance infrastructure for Australian regulation. Different layers.

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#  Guardrails AI vs Guard 

 Guardrails AI validates LLM output in your code. Guard is managed compliance infrastructure for Australian regulation. Different layers. 

Why this comes up

Guardrails AI is a popular open-source Python framework. You wrap LLM calls with validators, structured-output schemas, type checks, regex, and custom "guards" so the model returns data your application can trust. For output quality and reliability it’s genuinely useful.

It is not, and doesn’t claim to be, a compliance product. There’s no Australian PII detection with TFN, Medicare, or ABN checksums; no cryptographic per-call attestation; no regulatory mapping; and no managed audit trail. It’s a developer library for output validation, running inside your code, maintained by you.

Guard operates at a different layer. It’s managed infrastructure that sits in the request path, applies Australian PII and injection detection on both input and output, signs each call as tamper-evident evidence mapped to CPS 234 and the Privacy Act, and retains it for seven years. Output validation and compliance evidence are complementary, not the same thing.

The difference

Guardrails AI validates outputs in your code. Guard is managed compliance infrastructure with Australian evidence.

## Side by side

| Capability                                              | Guardrails AI | 40° South Guard |
| ------------------------------------------------------- | ------------- | --------------- |
| Open-source output validation framework                 | ✓             | ✗               |
| Structured output / schema enforcement                  | ✓             | ✗               |
| Managed infrastructure (no code to maintain)            | ✗             | ✓               |
| Australian personal data detection (TFN, Medicare, ABN) | ✗             | ✓               |
| Per-call signed proof                                   | ✗             | ✓               |
| CPS 234 / Privacy Act regulatory mapping                | ✗             | ✓               |
| 7-year tamper-evident evidence vault                    | ✗             | ✓               |

✓ = supported · \~ = partial · ✗ = not supported 

Reflects publicly available information as at July 2026\. Tools change; check current vendor documentation. 

[ Download the full comparison (PDF) ](/downloads/guard-vs-guardrails-ai.pdf) 

Could you run them together?

Yes. Use Guardrails AI in your application for output validation and structure, and route the underlying model calls through Guard for the compliance evidence layer.

One makes your outputs reliable; the other makes your AI use defensible to a regulator.

## See Guard on your own AI calls 

Book a demo and we’ll show you a signed attestation for a real call, mapped to your obligations under CPS 234, the Privacy Act, and AI transparency. 

[Book a demo → ](/#get-started) [See all comparisons ](/compare)

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Markdown version of https://40south.au/compare/guardrails-ai for AI readers. Site index for LLMs: https://40south.au/llms.txt