What is Llama Guard?
A suite of input/output moderation models and security evaluation benchmarks designed to help developers build safer generative AI applications. Includes tools to detect prompt injection, jailbreaking attempts, and other violating content.
Llama Guard pricing
We don't have Llama Guard's full plan breakdown yet (its pricing page resisted automated reading). Here's what we could confirm. Always check live pricing for exact numbers.
Free and open source under MIT license
What Llama Guard does
The capabilities that matter for ai guardrails, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- Architecture model
- Open source python library
- Prompt injection and jailbreak blocking
- ✓
- Inbound pii and credit card masking redaction
- ✗
- Outbound toxicity and competitor mention blocking
- ✓
- Hallucination and topical off brand drift detection
- ✗
- Sub 50ms latency guarantees for proxy routing
- ✗
- Custom regex and deterministic denylist rulesets
- ✗
- Streaming token interception and chunked eval
- ✗
- Rag retrieval chunk poisoning defense
- ✗
- Zero data retention and prompt privacy guarantees
- ✓
- SOC2 type ii
- ✗
- ISO 27001
- ✗
- Pricing model
- Free open source
Platform & deployment
Independently observed- CLI
- Self-hosted
Security & compliance
Known vulnerabilities: 0 (0 in the last 12 months) sourcea count reflects scale & disclosure, not quality
Llama Guard alternatives
Other ai guardrails we track, ranked by the same independent score.
- Enkrypt AISecurity and compliance platform that automatically detects and blocks threats in AI agents while generating audit-ready evidencemedium · 55%
- Arthur ShieldPlatform for discovering, monitoring, and controlling AI applications at organizational scalemedium · 56%
- NVIDIA NeMo Guardrailslow · 34%
- HiddenLayerComprehensive platform that discovers, tests, and protects machine learning models throughout their lifecycle from supply chain risks and runtime attacks.low · 21%
- Lakera GuardAI security platform that detects and blocks threats to large language model applications in real-timemedium · 55%
- Rebufflow · 31%
Compare Llama Guard
Side by side against other ai guardrails, attribute by attribute, with a source on every value.
The Vioscale score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for Llama Guard, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Pricing transparency | 80 | 0.08 | 6.7 | ✓ |
| Price level | 100 | 0.05 | 5.2 | ✓ |
| Capabilities | 58 | 0.08 | 4.9 | ✓ |
| Development activity | 28 | 0.09 | 2.7 | ✓ |
| Security score | 59 | 0.04 | 2.5 | ✓ |
| Stars | 69 | 0.03 | 1.8 | ✓ |
| Reliability | 0 | 0.07 | 0.0 | - |
| Integrations | 0 | 0.09 | 0.0 | - |
| Dependent projects | 0 | 0.06 | 0.0 | ✓ |
| Release cadence | 0 | 0.05 | 0.0 | - |
| Security posture | 0 | 0.07 | 0.0 | - |
| Package downloads | 0 | 0.14 | 0.0 | - |
| Developer Q&A activity | 0 | 0.06 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Activity
| Attribute | Value | Evidence |
|---|---|---|
| Commits last 30d | 7 | mediumsource · 2026-08-26 · 65% |
Adoption
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | Iso 27001: No · Soc2 type ii: No · Pricing model: free_open_source · Architecture model: open_source_python_library · Rag retrieval chunk poisoning defense: No · Prompt injection and jailbreak blocking: Yes | mediumsource · 2026-08-21 · 60% |
Language
| Attribute | Value | Evidence |
|---|---|---|
| Primary | Python | highsource · 2026-08-26 · 90% |