The Cold Start Tax on Serverless AI Agents
A standard Lambda function with a thin Python handler cold-starts in about 250ms. Your AI agent, calling the same runtime with a few SDK imports added, cold-starts in 8–12 seconds. Add local model inference and you're at 40–120 seconds. The first user to hit a scaled-down deployment waits the length of a TV commercial before the agent responds. That gap — not latency per inference token, not throughput, but the initial startup cost — is where most serverless AI deployments quietly fail their users.
The problem isn't unique to serverless, but serverless makes it visible. When you run agents on always-on infrastructure, you pay for idle capacity and cold starts never happen. When you embrace scale-to-zero to cut costs, every period of low traffic becomes a trap waiting for the next request.
