Modal vs RunPod

Modal uses functions: per-second cpu, memory and gpu resources; shared endpoints: per-token. storage and network egress have separate allowances and rates.; RunPod uses pods: per-second compute and local disk billing; network volumes: hourly storage billing. serverless flex/active workers: per-second lifecycle time.. Marketplace hourly spend tracks GPU uptime; serverless and managed APIs bill for what you invoke — different failure modes if you forget to shut things down.

Modal: Choose supported GPU types through the function GPU setting, including T4, L4, A10, L40S, A100, RTX PRO 6000, H100, H200, B200 and B300; allocation and multi-GPU limits vary.. RunPod: Broad consumer and datacenter SKUs. RunPod scores higher for GPU choice (9/10 vs 6/10). Pick a marketplace when you need a specific SKU; pick serverless or managed APIs when you want the platform to handle hardware.

Modal: Can be noticeable on cold containers; benchmark the real image and model.. RunPod: Depends on pod spin-up and image cache. Our editorial ratings favour Modal for warm API speed (7/10 vs 6/10), but real latency depends on model size, region, and whether endpoints are kept warm. Those 1–10 scores are editorial rankings, not measured milliseconds — record your own TTFT/p95 on a warm endpoint in the target region before you buy on latency.

Choose Modal when developers who want gpu code as functions with autoscaling. Choose RunPod when teams that want to pick a specific gpu sku and control the container stack. Watch out: You can choose a specific supported GPU type, but containers and scaling remain managed. Warm idle containers consume billable resources. Starter compute credits do not cover Shared Endpoints tokens. Pod compute stops when a Pod stops, but persistent volume disks ($0.20/GB-month stopped) and network volumes ($0.07/GB-month under 1TB; $0.05/GB-month above 1TB) continue billing until deleted. Temporary container disk is erased on stop. Serverless worker billing and always-on worker settings differ from Pod billing.

Not always. Stopping a pod or instance can leave disks/volumes attached and still billing. Terminate and confirm storage teardown when the experiment ends — especially on marketplace hosts like RunPod and Vast.ai.

Prefer a managed inference API when you want tokens quickly without CUDA ops and the model is already on the catalog. Prefer GPU rental when you need a specific SKU, custom serving stack, or sustained utilization that beats token pricing — size VRAM on /gpus first.