RunPod

RunPod is the go-to GPU cloud for developers and researchers who need raw compute at the lowest possible price. Unlike managed inference platforms like Together AI or Replicate that abstract away the infrastructure, RunPod gives you direct access to GPUs — from RTX 4090s at $0.34 per hour to H100s at $1.99 per hour — with the flexibility to configure them however you want. If you know how to set up a model serving environment, RunPod will be significantly cheaper than any managed alternative at scale.

The platform offers two distinct cloud tiers. Community Cloud aggregates GPUs from vetted providers into a shared marketplace, offering the lowest prices but with dynamic pricing that fluctuates based on supply and demand. Secure Cloud runs on enterprise-grade data centers with SOC 2 Type II certification, providing dedicated single-tenant hardware at a premium. The Serverless option bridges the gap, offering per-second billing with auto-scaling — you pay only when your endpoint is actively processing requests, similar to AWS Lambda but for GPUs.

The 7-day reserved pricing option cuts costs by up to 30%, making RunPod particularly attractive for sustained workloads like model training or high-traffic inference. Storage costs at $0.07-0.14 per GB per month are reasonable for keeping model weights and data persistent. The main trade-off is that RunPod is a bare-metal cloud provider, not a managed AI platform. There is no model marketplace, no one-click deployment, and no built-in fine-tuning pipeline. You bring your own Docker containers, configure your own serving stack (vLLM, TGI, etc.), and manage your own deployments. For teams with the technical expertise, this flexibility and cost advantage is compelling.