If you're training or serving an AI model, at some point you need GPUs — and buying your own H100 cluster isn't realistic for most teams. Two of the most common answers to "where do I rent GPU compute" are CoreWeave and RunPod. They both sell NVIDIA GPU capacity, but they're built for different customers, and picking the wrong one means either overpaying for enterprise features you don't need, or hitting availability limits you didn't expect.
The short version
| CoreWeave | RunPod | |
|---|---|---|
| Built for | Enterprise AI labs, large dedicated capacity | Self-serve developers, pay-as-you-go |
| Pricing model | On-demand + reserved capacity (up to 60% off) + spot | Pay-by-the-second, no contracts |
| Typical cost | ~$6.16/GPU-hr on-demand (H100, sold in 8-GPU nodes) | $0.27–$7.39/hr per GPU (pods), $0.58–$9.98/hr (serverless) |
| Cold starts | Not built around fast serverless spin-up | Sub-200ms serverless cold starts (FlashBoot) |
| Notable customers | OpenAI, Mistral AI, Google, IBM | Self-serve developer and startup base |
CoreWeave: built for enterprise scale
CoreWeave is an enterprise cloud built specifically around NVIDIA GPUs, providing large-scale compute for training and running the biggest AI models. It's trusted by major AI labs — OpenAI, Mistral AI, Google, and IBM are named customers — at genuine production scale, which is a different tier of validation than most GPU clouds can claim.
Pricing: On-demand H100 runs around $6.16/GPU-hr, sold as an 8-GPU node (about $49.24/hr total) rather than individual cards. Reserved capacity can cut that by up to 60%, spot instances save roughly 50%, and storage has zero egress fees.
Strengths: Zero egress fees, a free Kubernetes control plane, and steep discounts if you can commit to reserved capacity.
Limits: On-demand pricing is notably higher than RunPod's cheapest tiers, GPUs are sold in fixed 8-GPU bundles rather than single cards (less flexible for small workloads), and the whole platform is positioned around enterprise contracts rather than instant self-serve simplicity.
RunPod: built for self-serve, pay-as-you-go
RunPod is pay-by-the-second GPU cloud for training and running AI models, with no contracts and fast serverless cold starts. It's aimed at AI developers, researchers, and companies who want to start running workloads immediately, without a procurement cycle.
Pricing: Pods run $0.27–$7.39/hr per GPU, serverless runs $0.58–$9.98/hr, and storage is $0.05–$0.14/GB/month — all pay-as-you-go with no contracts or minimum commitments.
Strengths: No contracts required, sub-200ms serverless cold starts via FlashBoot, and 30+ GPU types across 31 regions — generally cheaper than enterprise-focused competitors like CoreWeave.
Limits: Availability during high-demand periods can be less predictable than reserved or dedicated capacity, support is lighter-touch than an enterprise contract would get you, and the wide price range means your real cost depends heavily on which GPU tier you pick.
Pick CoreWeave if…
…you're operating at real enterprise scale, can commit to reserved capacity to bring costs down, and need the kind of dedicated infrastructure and support that comes with an enterprise contract. It's the choice large AI labs make when they need guaranteed capacity, not the cheapest hourly rate.
Pick RunPod if…
…you want to start running GPU workloads today without talking to a sales team, you're price-sensitive, or your workload is spiky rather than constant. Its serverless cold-start speed also makes it a strong fit for inference workloads that need to scale up and down quickly.
Both are legitimate options actually used in production by real teams — the honest dividing line is scale and commitment, not quality. If you're not sure yet how much capacity you'll need long-term, RunPod's no-contract model gives you room to figure that out before committing to anything bigger.