LakeraFreemium
An AI security platform that defends LLM applications against prompt injection, jailbreaks, data leakage, and other model-layer attacks.
AI Governance comparison
Lakera vs Prisma AIRS (formerly Protect AI)
Pricing, pros, cons, and ideal use cases — side by side.
Prisma AIRS (formerly Protect AI)Enterprise
An enterprise platform for AI and ML security — scanning models for threats, securing the ML supply chain, and giving security teams visibility into AI risk.
At a glance
| Lakera | Prisma AIRS (formerly Protect AI) | |
|---|---|---|
| Pricing | FreemiumFree tier for developers. Paid and enterprise plans for production protection. | EnterpriseEnterprise pricing, quoted per organization. |
| Category | AI Governance | AI Governance |
| Ideal for | Enterprises exposing LLM apps to untrusted inputSecurity teams responsible for AI riskOrganizations handling sensitive data through AI | Enterprise security and ML platform teamsOrganizations using open-weight or third-party modelsTeams needing AI supply-chain security |
Pros & cons
Lakera
Pros
- Real-time defense against prompt injection and jailbreaks
- Continuously updated threat intelligence
- Addresses risks that generic security tools miss
- Backed by the Check Point security portfolio
Cons
- Model-layer security is a fast-evolving arms race
- One layer of a broader security strategy
- Production protection requires a paid plan
Prisma AIRS (formerly Protect AI)
Pros
- Scans models for threats and vulnerabilities
- Secures the ML supply chain
- Org-wide visibility into AI security risk
- Now part of the Palo Alto Networks portfolio
Cons
- Enterprise-only — built for security teams
- Pricing requires a sales conversation
- Complements, not replaces, application-layer guardrails
Which should you choose?
Lakera is the lighter-weight option (Freemium), while Prisma AIRS (formerly Protect AI) sits higher on the pricing ladder (Enterprise). Lakera is built around enterprises exposing llm apps to untrusted input; Prisma AIRS (formerly Protect AI) leans more toward enterprise security and ml platform teams. Shortlist the one whose strengths line up with your biggest constraint.
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