Paperspace
Cloud GPU platform for ML developers, with managed notebooks and Gradient ML pipelines
About Paperspace
Paperspace (now part of DigitalOcean) provides on-demand cloud GPUs and the Gradient platform for end-to-end machine learning. Researchers can spin up persistent Jupyter notebooks on A100, H100, A6000, and consumer-grade GPUs in minutes, with built-in dataset and model versioning. Its managed Core VMs target individual practitioners while Gradient targets teams running training and deployment pipelines.
Pros & Cons
Pros
- Best-in-class managed Jupyter notebooks
- Strong free tier for experimentation
- Integrated dataset and model versioning
Cons
- GPU availability for top SKUs can be tight
- Pricing higher than spot/marketplace alternatives
- Smaller global footprint than hyperscalers
Key Features
Gradient Notebooks
Persistent Jupyter environments with pre-configured PyTorch and TensorFlow images on cloud GPUs
Cloud GPU Machines
On-demand GPU VMs from RTX 4000 to H100, billed per hour with auto-shutdown
Gradient Workflows
Git-backed ML pipelines for training, evaluation, and deployment
Public Datasets
Pre-mounted ML datasets that eliminate slow downloads to the instance
Pricing
- Limited free GPU notebook hours
- Public projects only
- Community support
- Private notebooks
- Longer auto-shutdown
- Priority queue access
- Team workspaces
- Higher concurrency
- Advanced ML pipelines
Best For
Solo ML researcher prototyping
Spin up a persistent notebook with PyTorch pre-installed for fast experimentation.
Reproducible team training
Use Gradient Workflows to version datasets, code, and model artifacts across runs.
ML education
Educators provision identical GPU notebooks for every student in a class.
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