RunPod vs Paperspace
Direct answer: RunPod and Paperspace both rent GPUs by the hour, but their architectures differ. RunPod is designed for fast container deployment and serverless GPU inference. Paperspace is built around Gradient, a managed ML platform with persistent notebooks, experiment tracking, and team collaboration. Your choice depends on whether you need a raw GPU or a managed ML environment.
At-a-glance comparison
| Feature | RunPod | Paperspace |
|---|---|---|
| GPU types | RTX 3090, 4090, A100, H100, A6000, RTX A4000 | RTX 4000, 5000, A100, A6000, V100 (limited regions) |
| Starting price | $0.20/hr (A4000) / $0.44/hr (RTX 3090) | $0.51/hr (RTX 4000) / $2.46/hr (A100) |
| Serverless inference | Native (RunPod Serverless) | Not available |
| Persistent storage | Network volumes (paid) | Free persistent SSD per machine |
| Notebook experience | Jupyter via template | Gradient Notebooks (first-class) |
| Container deployment | Docker-native, custom images | Docker support, but Gradient-centric |
| Free tier | $5 initial credit only | Free GPU hours (M4000, limited) |
| Preemptible/spot | Community cloud (variable pricing) | Not offered |
| API & CLI | GraphQL API, CLI available | Gradient CLI, REST API |
| Team features | Basic group sharing | Teams, shared projects, RBAC |
Pricing deep dive: what does a day of training cost?
Suppose you are fine-tuning a LoRA on SDXL for 8 hours. On RunPod, an RTX 3090 at $0.44/hr costs $3.52 for the session. On Paperspace, an RTX 5000 at $0.78/hr costs $6.24. The A100 comparison is starker: RunPod charges $1.19–$1.99/hr depending on VRAM, while Paperspace charges $2.46/hr.
However, Paperspace includes persistent storage in the machine price. RunPod charges separately for network volumes. If your workflow involves large datasets that stay on disk between sessions, Paperspace can be cheaper overall despite the higher GPU rate.
GPU availability and reliability
RunPod offers more GPU types and generally higher availability, especially for consumer cards like the RTX 3090 and 4090. Paperspace has narrower inventory and popular GPUs can be out of stock in certain regions. For always-available A100s, both platforms are reliable, but RunPod's pricing is more competitive.
Developer experience
RunPod workflow
- Create a pod from a template (PyTorch, ComfyUI, etc.).
- Connect via SSH or Jupyter.
- Upload code via SCP, Git, or cloud storage sync.
- Run training or inference.
- Stop the pod when done. Pay only for runtime.
Paperspace workflow
- Create a Gradient Notebook or machine.
- Choose a runtime (TensorFlow, PyTorch, custom container).
- Code inside the notebook; files persist automatically.
- Use Gradient's experiment tracking and model registry.
- Stop the machine when done. Storage remains.
Paperspace feels more like Google Colab Pro with persistence. RunPod feels more like AWS EC2 for GPUs with better UX. If you want a managed notebook, Paperspace is smoother. If you want a container running your exact stack, RunPod is faster.
Serverless inference: RunPod's killer feature
Paperspace has no equivalent to RunPod Serverless. If you need to deploy an inference API that scales to zero between requests, RunPod is the clear choice. Paperspace machines must stay running to serve traffic, or you must build your own auto-scaling layer on top.
When to choose RunPod
- You want the lowest GPU price per hour.
- You need serverless inference endpoints.
- You prefer Docker-native deployment and custom images.
- You need specific GPUs like the H100 or RTX 4090.
- You are comfortable managing your own storage and data pipeline.
When to choose Paperspace
- You want a persistent notebook with files that survive between sessions.
- You are learning and need a free GPU tier to start.
- You want built-in experiment tracking and model versioning.
- You are working in a team and need collaboration features.
- You do not want to manage Docker containers.
Performance benchmark: LLM inference throughput
A developer on Reddit r/LocalLLaMA reported Llama 3 70B inference at 45 tokens/second on an A100 80GB via vLLM on RunPod. On Paperspace's A100, the same configuration produced 42 tokens/second. The difference is negligible and likely due to network overhead rather than hardware. Both platforms use the same underlying NVIDIA GPUs.
Storage and data management
| Aspect | RunPod | Paperspace |
|---|---|---|
| Included storage | None (Network volumes extra) | Free SSD up to 500 GB |
| Pricing | $0.07/GB/month | Included in machine cost |
| Persistence | Detachable, survives pod stop | Tied to machine or project |
| Data upload | SCP, rclone, cloud sync | Native upload, artifact storage |
Migration path: switching from one to the other
Moving a project from Paperspace to RunPod means Dockerizing your environment and exporting datasets. Moving from RunPod to Paperspace means adapting to Gradient runtimes and potentially rethinking your deployment strategy if you rely on serverless. The friction is moderate in both directions; plan for a day of migration work per active project.
Related comparisons
FAQs
Is RunPod cheaper than Paperspace?
Yes for most GPU types on a per-hour basis. Paperspace includes storage, which can narrow the gap for workloads with large persistent datasets.
Does Paperspace have serverless inference?
No. Paperspace machines must stay running. You need an external auto-scaling solution or switch to RunPod Serverless.
Which is better for beginners?
Paperspace. The free tier and managed notebooks lower the barrier to entry. RunPod is better once you outgrow notebooks and need containers.
Can I use Docker on Paperspace?
Yes, but it is not the primary workflow. Gradient runtimes and notebooks are the native experience. Docker support is available on CORE machines.
Which platform has better GPU availability?
RunPod generally has more GPU types in stock and supports consumer cards. Paperspace inventory is more limited, especially outside the US.
Get the best GPU price per hour
RunPod offers lower hourly rates, more GPU types, and native serverless inference for production APIs.
Try RunPod