Meta Llama vs Wiz Cloud Security AI
A direct comparison using the real catalog fields currently available for both products.
Last updated
Meta Llama
Meta AI
Wiz Cloud Security AI
Wiz
Free to download and use commercially (with usage limits at scale). Meta provides API access via partners (AWS, Azure, NVIDIA). Open source under the Llama Community License.
Custom enterprise; Wiz Free for small environments; Google acquiring for $32B (2025)
Cloud-native enterprises wanting agentless AI cloud security posture management across multi-cloud environments.
Open source model under Meta Llama Community License. Derivative models subject to same licence terms. Commercial use permitted with usage threshold restrictions.
Meta's data handling: no data sent to Meta when running locally. Cloud API providers subject to their own data handling policies.
When running Llama locally, no data is sent to Meta or any external party. Meta's cloud API providers have their own data handling policies. Review the Llama Community License for commercial use restrictions above 700M MAU.
FedRAMP authorized; HIPAA BAA; GDPR DPA; Google acquisition may affect data terms
- ✓Completely free to use without API fees — massive cost advantage for high-volume applications
- ✓Full data privacy when run locally — no data leaves company infrastructure
- ✓Largest ecosystem of fine-tuned derivatives, tools, and community support of any open source model
- ✓Llama 3.1 405B performance competitive with frontier proprietary models on many benchmarks
- ✓Fastest deployment of any cloud security (agentless)
- ✓45%+ Fortune 100 validation
- ✓Wiz AI NL queries useful
- ✓Best multi-cloud coverage
- ✓Fastest-growing security company
- ✕Running 405B model requires significant GPU hardware investment for on-premise deployment
- ✕Smaller Llama variants (8B) less capable than frontier proprietary models for complex tasks
- ✕Meta's commercial use licence has restrictions at very large scale (700M+ MAU) — review for large applications
- ✕Google acquisition creates uncertainty
- ✕Expensive at large cloud scale
- ✕Less depth for on-premise
- ✕Relatively new (2020)
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