Blink and it’s ready.
Deploy GPUs in under a minute—no need to wait for provisioning.
Scale globally.
Spin up one or hundreds of GPUs across 31 regions.
Pay by the second.
Ultra-flexible, on-demand billing—no commitments.
GPU Pricing
Thousands of GPUs across 30+ regions.
Simple pricing plans for teams of all sizes, designed to scale with you.
GPU
32GB VRAM
"The Runpod team has clearly prioritized the developer experience to create an elegant solution that enables individuals to rapidly develop custom AI apps or integrations while also paving the way for organizations to truly deliver on the promise of AI."
Amjad Masad
AI Apps
"Runpod is the only place I can deploy high-end GPU models instantly—no sales calls, no rate limits, no nonsense."
Daniel Chang
Research
“The main value proposition for us was the flexibility Runpod offered. We were able to scale up effortlessly to meet the demand at launch.”
Josh Payne
LLM Inference
“Runpod helped us scale the part of our platform that drives creation. That’s what fuels the rest—image generation, sharing, remixing. It starts with training.”
Matty Shimura
Model Training
Developer Tools
Built-in developer tools & integrations.
Powerful APIs, CLI, and integrations
that fit right into your workflow.
Full API access.
Automate everything with a simple, flexible API.
CLI & SDKs.
Deploy and manage directly from your terminal.
GitHub & CI/CD.
Push to main, trigger builds, and deploy in seconds.
Storage Pricing
Flexible, cost-effective storage for every workload.
No fees for ingress/egress. Persistent and temporary storage available.
Pod Pricing
Storage Type
Running Pods
Idle Pods
Volume
$0.10/GB/mo
$0.20/GB/mo
Container Disk
$0.10/GB/mo
$0.20/GB/mo
Persistent Network Storage
Storage Type
Under 1TB
Over 1TB
Network Volume
$0.07/GB/mo
$0.05/GB/mo
Container Disk
$0.10/GB/mo
$0.20/GB/mo
Gain additional savings with reservations.
Save more with long-term commitments. Speak with our team to reserve discounted active and flex workers.
FAQs
Questions? Answers.
Curious about unlocking GPU power in the cloud? Get clear answers to accelerate your projects with on-demand high-performance compute.
What are GPU Pods and how do they differ from other cloud GPU offerings?
GPU Pods are dedicated GPU instances you can spin up on Runpod. Unlike abstracted serverless GPUs, Pods give you full control over the underlying VM, drivers, and environment. You get a persistent instance (or ephemeral, if you prefer) with direct access to powerful GPUs, letting you run training, inference, or other workloads exactly how you want.
Which GPU models are available?
We offer 30+ GPU models, from entry-level inference cards to top-tier training accelerators. Examples include A100, H100, RTX 6000 Ada, L4/L40 series, and many more—over 30 options in total. You can pick any supported GPU when you launch a Pod, and new models roll out as soon as they’re live on the platform. For the latest availability, check the dashboard or query the API.
How is pricing structured?
Pricing is shown as an hourly rate but billed by the millisecond. You only pay for the exact time your Pod runs—if you start and stop a Pod in one minute, you’re charged just that minute. Storage volumes may incur minimal fees when attached, but compute costs are metered by the millisecond.
Can I bring my own Docker container or environment?
Yes. GPU Pods support custom Docker images. You can build an image with your preferred libraries and push it to a registry (Docker Hub, ECR, etc.), then reference it when you launch the Pod. That way you control the OS, drivers, and dependencies.
Which frameworks and runtimes are supported?
Any framework that runs on Linux and supports GPUs: PyTorch, TensorFlow, JAX, ONNX, CUDA toolkits, etc. Since you control the container, you can install whatever versions or additional tools you need (e.g., NCCL, Horovod). We provide base images with common ML stacks to speed up setup.
What about spot/preemptible GPUs?
We offer spot instances where GPU capacity is available at a discount, but with the risk of eviction when demand spikes. You can use them for fault-tolerant or batch workloads. The UI/API will indicate current spot availability and pricing.
Clients
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