RunPod vs Modal

RunPod and Modal both promise zero-Ops GPU inference. They're built for different workloads. Here's how to pick. RunPod hands you a long-lived pod, charges per-second, and trusts you to manage the runtime. Modal hands you a Python function and manages the runtime for you. The right answer depends less on price-per-A100-hour and more on whether your team thinks in pods or functions.

What we tested

We ran the same fine-tune-then-inference workload against both platforms across two GPU classes (A100 80GB and L4). On RunPod we built a Docker image, attached persistent volumes, and ran the workload as a long-running pod. On Modal we deployed the same workload as a @modal.function with a 30-second warm pool. We tracked spin-up latency, sustained throughput across a 30-minute fine-tune, and the developer experience of iterating on the runtime image. Modal's spin-up landed at 8.5 / 10 with the warm pool engaged.

When to pick which

Pick RunPod for: long-lived training jobs, teams that already think in Docker images, workloads that want SSH access for debugging, and anything that benefits from a persistent volume across iterations.

Pick Modal for: bursty inference where the function pattern fits, Python-native teams, and workloads where the warm pool amortises across short calls.

Try the platforms