From Chaos to Control: How Kong AI Gateway Streamlined My GenAI Application

In this post, Kong Champion Sachin Ghumbre shares his journey of transforming a complex GenAI application from a state of operational challenges to streamlined control. Discover how Kong AI Gateway provided the enterprise-grade governance needed to secure, optimize, and scale his GenAI solution, tackling issues from escalating LLM costs to prompt injection risks.

🚧 The challenge: Scaling GenAI with governance

While building a GenAI-powered agent for one of our company websites, I integrated components like LLM APIs, embedding models, and a RAG (Retrieval-Augmented Generation) pipeline. The application was deployed using a Flask API backend and secured with API keys.

However, post-deployment, several operational challenges emerged:

• Escalating LLM usage costs
• Security risks from exposed API keys and prompt injection
• Limited observability into prompt flows, token usage, and latency
• Difficulty in maintaining and scaling the API infrastructure

It became clear that while the GenAI logic was sound, the API layer lacked enterprise-grade governance. That’s when I turned to Kong Gateway, specifically its AI Gateway capabilities.

🤖 Why Kong Gateway for GenAI?

Kong isn’t just a traditional API gateway; it now offers a dedicated AI Gateway designed to meet the unique demands of GenAI workloads. Here’s what makes it ideal:

• AI Manager: Centralized control plane for LLM APIs
• One-Click API Exposure: Secure and governed API publishing
• Secure Key Management: Store secrets in Kong Vault
• Prompt Guard Plugin: Prevent prompt injection attacks
• Semantic Routing: Route prompts based on intent/context
• RAG Pipeline Simplification: Offload orchestration to the gateway
• Caching & Optimization: Reduce token usage and latency
• Observability & Analytics: Monitor usage, latency, and cost
• Rate Limiting & Quotas: Control overuse and manage budgets
• Future-Ready: Support for multi-agent protocols like MCP and A2A

These features allowed me to shift complexity away from the backend and focus on GenAI logic.

🧱 Architecture overview

This architecture, built on AWS, leverages Kong Gateway to securely manage interactions between internal services and external LLM providers. The environment described reflects my development setup, including AWS services and supporting technologies. 

For production deployments, I recommend evaluating and adopting a more robust technology stack and configuration to ensure enhanced security, compliance, scalability, and high availability.

🔄 Challenge vs. solution matrix