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Cloud GPUs

High-Performance GPUs On Demand.

Run AI, ML, and HPC workloads on powerful cloud
GPUs—without limits or wasted spend.

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.
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 sets RunPod’s serverless apart from other platforms?
RunPod’s serverless GPUs eliminate cold starts with always-on, pre-warmed instances, ensuring low-latency execution. Unlike traditional serverless solutions, RunPod offers full control over runtimes, persistent storage options, and direct access to powerful GPUs, making it ideal for AI/ML workloads.
What programming languages and runtimes are supported?
RunPod supports Python, Node.js, Go, Rust, and C++, along with popular AI/ML frameworks like PyTorch, TensorFlow, JAX, and ONNX. You can also bring your own custom runtime via Docker containers, giving you full flexibility over your environment.
How does RunPod reduce cold-start delays?
RunPod uses active worker pools and pre-warmed GPUs to minimize initialization time. Serverless instances remain ready to handle requests immediately, preventing the typical delays seen in traditional cloud function environments.
How are deployments and rollbacks managed?
RunPod allows deployments directly from GitHub, with one-click launches for pre-configured templates. For rollback management, you can revert to previous container versions instantly, ensuring a seamless and controlled deployment process.
How does RunPod handle event-driven workflows?
RunPod integrates with webhooks, APIs, and custom event triggers, enabling seamless execution of AI/ML workloads in response to external events. You can set up GPU-powered functions that automatically run on demand, scaling dynamically without persistent instance management.
What tools are available for monitoring and debugging?
RunPod offers a comprehensive monitoring dashboard with real-time logging and distributed tracing for your serverless functions. Additionally, you can integrate with popular APM tools for deeper performance insights and efficient debugging.
Clients

Trusted by today's leaders, built for tomorrow's pioneers.

Engineered for teams building the future.

Build what’s next.

The most cost-effective platform for building, training, and scaling machine learning
models—ready when you are.