RunPod

Pick Runpod when your workload is spiky. An inference endpoint that sits idle overnight, a fine-tuning run that needs a specific GPU for six hours, a rendering batch — that's the shape of work this platform prices well. Per-second billing across Pods, Serverless, and Clusters means you stop paying the moment the job stops, and the 31-region footprint gives you somewhere to put workloads near your users. Pick it too if you want to stay close to the metal. You bring your own container, framework, and code; Runpod provides the GPU, the network, and the orchestration around Serverless queues. Teams that resent being locked into a vendor's training stack tend to like this. Pass if your ML practice depends on a tightly integrated platform. There's no native experiment tracker or model registry here, so you're pairing Runpod with MLflow, W&B, or similar. That's fine for many teams and friction for others, especially smaller ones without the tooling assembled already. Where it bites: Serverless cold starts are quoted sub-200ms, but that guarantee doesn't apply uniformly across every region and GPU type, so validate against your own workload before you commit. Clusters run to 64 GPUs on demand — past that, you're in Reserved Clusters territory, which means talking to sales and waiting for a quote. Enterprise buyers who need published contract rates before sign-off will find that annoying. The closest comparison is a hyperscaler. AWS and GCP give you a deeper service catalog, enterprise procurement channels, and mature networking and identity. What they don't give you is per-second GPU billing that stops at zero on an endpoint, or 30 GPU SKUs available on demand without a quota request. Runpod also undercuts them on flexibility rather than breadth, and happily accepts