PromptWall vs custom guardrails
Should you build AI security guardrails in-house or deploy a platform? A pragmatic build vs buy analysis considering time-to-value, total cost, and capability breadth.
The build temptation
Engineering teams often prefer building in-house — it offers full control, customization, and avoids vendor dependency. For AI guardrails, the initial prototype seems achievable: call an NER model, add some regex patterns, log the results. But production-grade AI security requires far more than a prototype.
Cost comparison
| Component | Build Time | Build Cost/yr | Buy Cost |
|---|---|---|---|
ML Engineering PII NER models, injection classifiers, similarity engines | 3-6 months | $200-400K/yr | Included |
Platform Development Policy engine, audit trail, multi-tenant, UI | 6-12 months | $300-500K/yr | Included |
Browser Extension Chrome extension for ChatGPT, Claude, Gemini | 2-4 months | $100-200K/yr | Included |
Editor Integration VS Code / Cursor for Copilot | 2-3 months | $100-150K/yr | Included |
Ongoing Maintenance Model updates, new attack patterns, provider API changes | Continuous | $150-250K/yr | Included |
Hidden complexity
- Model maintenance — Attack techniques evolve. Your ML models need continuous retraining against new injection techniques.
- Multi-surface coverage — Browser extension, editor plugin, CLI proxy, and ICAP gateway are each separate engineering efforts.
- Policy engine — Configurable policy enforcement with multi-tenant isolation adds significant complexity.
- Compliance — Audit trail design, compliance reporting, and evidence generation require security expertise.
- Provider API changes — OpenAI, Anthropic, and Google change APIs regularly. Custom solutions break; platforms absorb the change.
Time to value
PromptWall deploys in days. Custom development takes months to years. During that gap, your organization's AI interactions are unprotected — data is leaking, injection attempts are undetected, and there's no audit evidence for compliance.
Deploy in days, not months
Get production-grade AI security without the engineering burden.
Frequently asked questions
How long does it take to build custom AI guardrails?+
Building a minimum viable prompt inspection system (PII detection + injection classification) typically takes 3-6 months with a dedicated team. Adding browser extension, editor integration, policy engine, audit trail, and multi-tenant capabilities extends this to 12-18 months or more.
What does custom AI security cost to build?+
Typical build costs include: 2-3 ML engineers (model training, NER), 1-2 full-stack engineers (platform, UI), 1 security engineer (policy, audit), infrastructure (GPU for inference, vector DB for document similarity), plus ongoing maintenance. Estimated: $500K-$1M+ per year.
When should I build instead of buy?+
Consider building when you have unique requirements that no platform addresses, your organization has deep AI/ML expertise available, you need to integrate with proprietary internal systems, or your scale justifies the investment. For most enterprises, buying provides faster ROI.