Originally published at https://blog.runc.ai/serverless-vs-dedicated-vms-for-gpt-endpoint-hosting/ . Key Takeaways The real question behind serverless vs dedicated vms for gpt endpoint hosting is not just cost. It is which deployment model best fits your endpoint's traffic shape, latency target, and serving complexity. Serverless GPU is usually the better fit when traffic is bursty, demand is still uncertain, or the team wants the fastest path to a working endpoint without managing warm dedicated capacity. GPU Pods are often the better default for production GPT endpoints when the serving stack is already containerized and the workload benefits from warm, persistent GPU capacity. VMs make the most sense when the endpoint needs stronger OS-level control, custom services, or a serving stack that goes beyond a standard container-first deployment. On RunC.ai, the practical decision is often not serverless vs VM alone.…