RunPod Tutorial: Getting Started the Right Way
This RunPod tutorial walks you through your first GPU pod, from account setup to running code and cleanup, plus pricing and mistakes that catch beginners.
By Galactic News Editorial · Published 2026-11-11T10:20:37+00:00 · Updated 2026-11-11T10:20:37+00:00
RunPod Tutorial: Getting Started the Right Way
RunPod rents GPU computers by the second. You pick a graphics card, start a Pod, run your code, and stop the Pod when you finish. This RunPod tutorial shows you the whole flow. You will create an account, deploy your first Pod, run code on it, and clean up so you stop paying. Follow the steps in order. Your first session can be live in under fifteen minutes.
What RunPod Is
RunPod is a cloud GPU platform. It gives developers on-demand access to graphics cards for training models, running inference, and rendering work. You do not buy hardware. You rent it for as long as you need it.
RunPod offers three products. Pods are dedicated GPU instances. They work like a remote computer with a graphics card. Serverless runs inference workers that scale to zero when idle. Clusters link many GPUs together for large multi-node jobs.
Pods come in two cloud tiers. Community Cloud runs on hardware from independent hosts. It costs less. Secure Cloud runs in RunPod-managed data centers. It costs more and suits production work. The official pricing page lists both rates side by side.
RunPod Tutorial: Deploy Your First Pod in 7 Steps
These steps follow the official RunPod documentation. Keep the console open as you work through them.
- Create your account. Sign up at runpod.io. Verify your email address. Turn on two-factor authentication for security.
- Add credits. RunPod bills against prepaid credit. Add funds before you deploy. A Pod will not start on an empty balance.
- Open the deploy page. Click + New in the top-right corner of the console. Select Pod.
- Pick a GPU and a template. Choose a card that fits your workload. Pick a template such as PyTorch or Jupyter. Templates come preinstalled with common tools.
- Choose your cloud tier. Community Cloud costs less. Secure Cloud offers managed data centers. Beginners should start with Community Cloud for practice.
- Review the price and deploy. Check the hourly rate on the review screen. Click deploy. Wait for the Pod to finish starting.
- Run your code. Open the Pod detail pane. Under HTTP Services, click Jupyter Lab. Select the Python 3 notebook. Type
print("Hello, world!")in the first cell and run it.
That is the core loop. Everything else in this guide builds on it.
How Much Does RunPod Cost?
RunPod bills per second while a Pod runs. There is no monthly subscription for Pods. You pay for the GPU you rent and the storage you use. Prices change with availability, so check the pricing page before you deploy.
These rates come from the official RunPod pricing page, checked on November 11, 2026. They are Secure Cloud rates unless noted.
| GPU | Secure Cloud | Community Cloud from |
|---|---|---|
| RTX 4090 (24 GB) | $0.89 per hour | Check the live page |
| A100 SXM (80 GB) | $1.79 per hour | $1.39 per hour |
| H100 PCIe (80 GB) | $2.89 per hour | $1.99 per hour |
| H100 SXM (80 GB) | $3.99 per hour | $2.69 per hour |
| H200 (141 GB) | $5.29 per hour | $3.59 per hour |
Storage adds a small charge. Container disk costs $0.10 per GB per month. Volume disk costs $0.10 per GB per month while running and $0.20 per GB per month while idle. A stopped Pod still charges for storage. That detail matters for your bill.
Serverless uses a different model. It bills per second of active worker time. The official page lists an RTX 4090 worker at $1.34 per hour of active compute. An A100 worker costs $2.72 per hour. You pay nothing while workers sit idle.
Pods vs Serverless: Which Should You Pick?
Pods and Serverless solve different problems. Pick the wrong one and you pay more or move slower.
| Factor | Pods | Serverless |
|---|---|---|
| Best for | Development, training, experiments | Production inference APIs |
| Billing | Per second while running | Per second of active work only |
| Idle cost | You pay while it runs | Scales to zero |
| Control | Full access to the machine | You ship a container image |
| Setup effort | Low, pick a template | Higher, build a worker image |
Start with Pods. Build and test your code there. Move to Serverless when your model is ready for users. RunPod documents this Pod-first workflow in its own guides. You develop interactively on a Pod, then deploy the same Docker image to Serverless.
5 Costly Beginner Mistakes
Most first bills surprise people for the same reasons. Avoid these five mistakes.
- Leaving a Pod running. The meter runs until you stop it. Stop the Pod the moment your session ends.
- Stopping instead of terminating. A stopped Pod still charges $0.20 per GB per month for storage. Terminate Pods you no longer need.
- Renting a bigger GPU than you need. Match the card to your model size. A small model on an H100 wastes money.
- Skipping persistent storage. Pod disk disappears when you terminate. Attach a network volume for files you want to keep.
- Deploying before testing locally. Build and test your Docker image on your own machine first. A broken image burns GPU time while you debug it.
How to Clean Up and Stop Paying
Cleanup takes less than a minute. Do it every time you finish a session.
- Open the Pods page in the console.
- Click your running Pod to open its detail pane.
- Click the Stop button to pause it. Confirm the stop.
- If you do not need the data, click Terminate instead. Confirm the termination.
Terminating deletes the Pod and its local disk. Stopping keeps the disk and keeps charging storage. Pick the option that matches your plan.
Bottom Line
RunPod is a fast way to rent GPU power without buying hardware. Create an account, add credit, pick a template, and deploy a Pod. Start small on Community Cloud. Watch the hourly rate before you deploy. Stop or terminate your Pod when you finish. Follow those habits and your first RunPod session will be cheap, fast, and free of billing surprises.