Perfai Security vs Lakera
Detailed side-by-side comparison to help you choose the right tool
Perfai Security
🟡Low CodeAI Security
Autonomous, agentic application security platform for AI-built and vibe-coded apps: its agents learn your app, exploit real business-logic, access-control, and prompt-injection flaws, then ship the fix as a pull request.
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CustomLakera
🔴DeveloperAI Security
Real-time AI security platform that protects LLM applications from prompt injection, data exfiltration, jailbreaks, and unsafe outputs.
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Perfai Security - Pros & Cons
Pros
- ✓Purpose-built for vibe-coded apps — targets the AI-native threat categories (prompt injection, RAG abuse, IDOR, business logic) that generic scanners miss.
- ✓Real-exploit validation dramatically cuts the false-positive noise that makes most scanner reports unusable for solo founders.
- ✓Auto-fix pull requests and push-to-tool fixes (Cursor, Claude Code, Copilot, Replit, Windsurf) mean remediation lands in the workflow you already use.
- ✓Free tier gives first findings in 20 minutes with 900 credits/month — enough to actually evaluate on a real app.
- ✓Continuous testing model (not scheduled scans) matches how fast AI-generated codebases actually change.
Cons
- ✗Agentic AppSec is a young category — treat auto-fix PRs as suggestions to review, not accept-blindly commits.
- ✗Credit-based pricing means very active apps can exhaust Pro (1,800/mo) and be pushed to Growth ($499/mo).
- ✗No MCP server or client advertised, so it doesn't slot into MCP-native agent stacks today.
- ✗Live-production coverage of your own domains is gated to Enterprise/MSSP — smaller plans focus on pre-launch and staging.
Lakera - Pros & Cons
Pros
- ✓Detection models trained on the largest public adversarial dataset (Gandalf, 60M+)
- ✓Latency genuinely fast enough for synchronous inline use, not just async logging
- ✓Both prompt and response screening in one API — covers the full I/O surface
- ✓SOC 2 Type II + EU origin makes compliance conversations easier
- ✓Open PINT benchmark gives credibility versus closed-evaluation competitors
Cons
- ✗Pricing opacity makes early-stage budgeting hard — must talk to sales
- ✗Free tier is small (1K req/month) — production prototypes hit limits fast
- ✗Adds a network hop and per-call cost on top of the LLM bill
- ✗Custom policies still require some prompt-engineering expertise to author
- ✗No general-purpose content moderation beyond the LLM-security focus
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