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.
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:
It became clear that while the GenAI logic was sound, the API layer lacked enterprise-grade governance. Thatās when I turned to Kong Gateway, specifically its AI Gateway capabilities.
Kong isnāt just a traditional API gateway; it now offers a dedicated AI Gateway designed to meet the unique demands of GenAI workloads. Hereās what makes it ideal:
These features allowed me to shift complexity away from the backend and focus on GenAI logic.
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.Ā
For production deployments, I recommend evaluating and adopting a more robust technology stack and configuration to ensure enhanced security, compliance, scalability, and high availability.
1. AWS Infrastructure
Ā 2. Kong Gateway Components
3. External LLM Integration
š Data Flow Overview
1. User interacts with GenAI agent (S3-hosted React app)
2. Request sent to Kong Gateway
3. Kong routes request, queries DB if needed, forwards to Gemini
4. Response returned via Kong to GenAI agentĀ
Building a GenAI application is only half the battle; the real complexity begins when scaling, securing, and monitoring it in production.
By integrating Kong Gateway and its AI-specific capabilities, I was able to:
Kongās AI Gateway isnāt just an API wrapper; itās a purpose-built control layer for modern AI workloads. If you're building GenAI applications in production, I highly recommend exploring Kongās capabilities to future-proof your architecture.
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