I wanted to deploy an LLM inference API without spending $1,200/month on AWS GPU instances. OCI turned out to be significantly cheaper, and the Docker workflow was identical. Here's what I set up.

Why I Looked at OCI for GPU Workloads

I've been building GPU infrastructure tools for a while now (keda-gpu-scaler, otel-gpu-receiver, GPU NUMA scheduling for Volcano), and most of my testing was on AWS. The g5.xlarge instances with A10 GPUs run about $1.01/hr, plus $73/month for the EKS control plane. It adds up fast when you're iterating.

Someone on the Volcano Slack mentioned OCI's GPU pricing and I was skeptical. But when I looked it up, the numbers were real — same A10 GPU, roughly 40% cheaper, and OKE doesn't charge for the Kubernetes control plane at all. So I tried moving a vLLM inference workload over.

OCI GPU Pricing

Here's what OCI actually charges for GPU instances.…