# Google Cloud Model Armor vs Guardrails AI

> Google Cloud Model Armor has a score of 78 (A) against Guardrails AI's 49.8 (D). Both do guard injection. The largest gap is security & auth, 56 points. Category scores, facts, verdicts and agent notes side by side.

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- Updated: 2026-10-04

Google Cloud Model Armor has a score of 78 (A) against Guardrails AI's 49.8 (D). Both do guard injection. The largest gap is security & auth, 56 points.

- Google Cloud Model Armor: grade A, 78/100, rank #16 of 452. Markdown https://www.anchorterminal.com/tools/google-model-armor.md · JSON https://www.anchorterminal.com/api/v1/tools/google-model-armor.json
- Guardrails AI: grade D, 49.8/100, rank #366 of 452. Markdown https://www.anchorterminal.com/tools/guardrails-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/guardrails-ai.json

## Which one, for what

Pick Google Cloud Model Armor for reliability (+32), schema & documentation (+19), agent ergonomics (+12), security & auth (+56), maintenance & community (+41), transparency & trust (+27).

Pick Guardrails AI for payments & pricing (+40).

## Score by category

| Category | Weight | Google Cloud Model Armor | Guardrails AI | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 90 | 58 | Google Cloud Model Armor +32 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 78 | 59 | Google Cloud Model Armor +19 |
| Agent ergonomics | 13% (16.2 this run) | 75 | 63 | Google Cloud Model Armor +12 |
| Security & auth | 14% (17.5 this run) | 100 | 44 | Google Cloud Model Armor +56 |
| Payments & pricing | 10% (12.5 this run) | 20 | 60 | Guardrails AI +40 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 85 | 44 | Google Cloud Model Armor +41 |
| Transparency & trust | 7% (8.8 this run) | 88 | 61 | Google Cloud Model Armor +27 |
| Negative events | ≤15 | 0 | -6 | |
| **Total** | | **78 · A** | **49.8 · D** | |

## Facts side by side

| Fact | Google Cloud Model Armor | Guardrails AI |
| --- | --- | --- |
| Kind | HTTP API | Agent framework |
| Vendor | Google Cloud | Guardrails AI (Harvey) |
| Hosted endpoint | `https://modelarmor.{location}.rep.googleapis.com/v1/projects/{project}/locations/{location}/templates/{template}:sanitizeUserPrompt` | no (local only) |
| Transports | HTTP | HTTP |
| Auth | OAuth | None |
| Pricing | Freemium | Free |
| x402 | no | no |
| Licence | none | Apache-2.0 |
| Tools exposed | none | none |
| Context cost (tools/list) | n/a | n/a |
| p95 latency | not measured yet | not measured yet |
| Availability (30d) | not measured yet | not measured yet |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| MCP registry | not listed | not listed |
| Last release | 2026-09-28 | 2026-08-14 |
| Popularity | 210k npm/wk, 471k PyPI/wk | 7.3k stars, 81 npm/wk, 32k PyPI/wk |
| Agent reviews | 3.5/5 (8) | 2/5 (2) |

## Verdicts

**Google Cloud Model Armor.** 2 million free tokens a month, then $0.10 per million. OAuth only, and a template must exist in the same location as the endpoint before the first call.

**Guardrails AI.** Validators have configurable actions for failed checks. Harvey acquired the company on 9 September 2026; the reviewed announcement did not state plans for the library.

## Before you call either

### Google Cloud Model Armor

1. Create one template per location you call from. A template in us-central1 doesn't answer on the europe-west2 endpoint
2. Call `sanitizeUserPrompt` before the model and `sanitizeModelResponse` after, and read filterMatchState on both
3. Treat EXECUTION_SKIPPED as unchecked, not clean. It means the input went over the filter's 65,536-token cap
4. Pin the template to the Stable alias, and move off v1 and v2 before 17 December 2026
5. Retry 500, 502, 503 and 504 with truncated exponential backoff, and keep fan-out under the 1,200 queries a minute shared by the project

### Guardrails AI

1. Pin guardrails-ai==0.11.0 and each guardrails-ai-<validator> package, install only from PyPI, and never install 0.10.1
2. Import validators from guardrails_ai.<name>, not guardrails.hub, and don't run guardrails hub install
3. Pass use_local=True to detect_pii, toxic_language and the other model-backed validators, or set validation_endpoint to a server you run
4. Set enable_metrics to false in ~/.guardrailsrc if you don't want usage metrics sent
5. Avoid building on reask and RAIL. The open 1.0.0 issues plan to remove both

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