🚀 Just published the story behind building Corrath — an AI Security Gateway for production LLM applications. I share the problems I faced, what I built, and the lessons learned. Feedback is welcome!

Haricharan — Sat, 25 Jul 2026 01:08:21 +0000

Haricharan

Jul 25

How I Built Corrath: An AI Security Gateway for Modern LLM Applications

#ai #webdev #api #security

2 min read

How I Built Corrath: An AI Security Gateway for Modern LLM Applications

Haricharan — Sat, 25 Jul 2026 01:07:04 +0000

How I Built Corrath: An AI Security Gateway for Production LLM Applications

Over the past few months, I've been building Corrath, an AI Security Gateway designed for developers and teams deploying LLM-powered applications in production.

As AI applications become more capable, they also become harder to secure. Prompt injection, runaway API costs, provider outages, leaked API keys, and limited visibility into AI traffic are becoming common operational problems.

I wanted a single gateway that sits between an application and AI providers to solve these issues without requiring major infrastructure changes.
The goal was to give developers one place to secure, monitor, and control every AI request without changing their existing application architecture.

The Problem

Most AI applications connect directly to providers like OpenAI, Anthropic, Google Gemini, or Mistral.

That works well initially, but as traffic grows several challenges appear:

Prompt injection attacks
Excessive API spending
No centralized logging
Provider outages
API key management
Lack of usage visibility
Difficult multi-provider routing

Most teams end up combining multiple services to solve these problems.

I wanted everything in one place.

Building Corrath

Corrath acts as a centralized gateway between applications and LLM providers.

Instead of calling providers directly, applications send requests through Corrath.

Corrath then performs security checks, routing, monitoring, budgeting, and failover before forwarding requests.

Current capabilities include:

Prompt injection detection
Secure API key vault
Per-key budgets
Usage analytics
Threat detection
Audit logs
Team management
Webhooks
Multi-provider routing
Cost forecasting The biggest goals while building Corrath were:

Keep latency extremely low
Make integration simple
Support multiple AI providers
Provide enterprise-grade security
Give developers complete visibility into AI usage
Lessons Learned

Building an AI infrastructure product is very different from building a typical SaaS application.

The biggest challenges weren't the frontend—they were:

Authentication
Security
Provider compatibility
Request routing
Production monitoring
Cost optimization

The project also taught me how important developer experience is. Good documentation and simple APIs matter just as much as features.

What's Next

I'm continuing to improve Corrath with features like:

Additional AI providers
Better analytics
Enhanced security policies
More automation
Enterprise integrations
I'd Love Feedback

If you've built or deployed AI applications in production, I'd love to hear your thoughts.

What security or operational problems have you run into?

Website:

https://corrath.io

- Prompt injection detection

If you're building with OpenAI, Claude, Gemini, Mistral, or other LLM providers, I'd love your feedback.

🌐 Website: https://corrath.io

⭐ GitHub: https://github.com/Haricharan071/corrath

𝕏 Follow: https://x.com/corrathhq

DEV Community: Haricharan

🚀 Just published the story behind building Corrath — an AI Security Gateway for production LLM applications. I share the problems I faced, what I built, and the lessons learned. Feedback is welcome!

How I Built Corrath: An AI Security Gateway for Modern LLM Applications

How I Built Corrath: An AI Security Gateway for Modern LLM Applications

- Prompt injection detection