Why Every AI Team Ends Up Building the Same Gateway (And What to Do About It)

📰 Dev.to AI

Learn why AI teams often build the same gateway and how to avoid it by using existing APIs and infrastructure

intermediate Published 14 Apr 2026

Action Steps

Identify the need for a gateway in your AI workflow using tools like API Gateway or Kong
Evaluate existing APIs and infrastructure to determine if a custom gateway is necessary
Use cloud-based services like AWS Lambda or Google Cloud Functions to build and deploy your gateway
Implement a routing layer that can pick between different AI models like GPT or Claude
Monitor and optimize your gateway's performance using tools like Prometheus or Grafana

Who Needs to Know This

AI engineers and DevOps teams can benefit from understanding the common pitfalls of building AI gateways and how to use existing solutions to streamline their workflow

Key Insight

💡 Most AI teams end up building the same gateway due to a lack of standardization and reuse of existing solutions