Serverless to Containers: A Decision Framework for Scaling AI Agents Across Teams
Serverless is a great place to start an AI agent, but it often stops fitting once dozens of teams depend on it. This covers the cost, cold-start latency, state management and observability limits that push agent workloads from AWS Lambda to Fargate or Kubernetes, and a practical framework for decidi
Almost every internal AI agent platform starts the same way: one team, one agent, one Lambda function. That's a sensible place to begin. The trouble shows up later, when the support team's agent becomes the template for ten other teams, runs get longer, and the monthly bill starts to look strange. Four pressure points push agents off serverless: cost, cold starts and time limits, state, and observability. We'll go through each, look at AWS Fargate as a middle step and Kubernetes as the destination, and finish with a decision framework, a worked example and a packaging trick that keeps every mo
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