One name that consistently appears in conversations around GPU-based virtual machines and cloud GPU providers is Paperspace. Known for its accessible pricing and focus on data science workflows, Paperspace offers cloud GPU virtual machines, containers and tools aimed at making GPU computing easier for developers, researchers and businesses alike.
In this Paperspace review, we explore the platform’s features, performance, pricing and more so you can understand if Paperspace is the right fit for your workloads.
What is Paperspace?
Founded in 2014 and acquired by DigitalOcean for $111 million, Paperspace is a cloud computing provider focused primarily on offering GPU-backed infrastructure. It allows users to spin up virtual machines, run Jupyter notebooks, build ML pipelines and train or deploy machine learning models, all in the cloud.
Key Features of Paperspace
The key features of Paperspace include :
1. Virtual Machines
Paperspace’s bread and butter is its virtual machine offering. Users can select from a variety of CPU and GPU options, install their own software, and access the machine via web browser, desktop app or SSH. You get :
- Multiple GPU options: including NVIDIA A100, V100, RTX4000, and more
- Pre-installed templates for ML, gaming, and creative workflows
- Snapshots and persistent storage
- Access via Linux, Windows, or Ubuntu
2. Gradient
Gradient is Paperspace’s MLOps platform that includes a range of tools for building, training, and deploying machine learning models. It simplifies the end-to-end ML lifecycle and is particularly valuable for teams looking to scale their workflows.
Gradient includes :
- Notebooks: Jupyter-based, GPU-backed notebooks for data exploration and prototyping.
- Workflows: Pipelines for training, hyperparameter tuning, and model validation.
- Deployments: Simple APIs and endpoints to deploy models in production.
- Datasets: Versioned dataset management built directly into the platform.
3. Container Support
Paperspace supports custom containers, allowing users to run their preferred environments. This flexibility is particularly useful for teams that have specific software stacks or dependencies.
You can :
- Automate tasks with CLI or API access
- Bring your own Docker image
- Use a prebuilt ML container
4. API and Automation
For more advanced users, Paperspace provides a comprehensive RESTful API. This is especially useful for automating tasks like creating VMs, starting/stopping instances, or integrating with external CI/CD pipelines. Paperspace also integrates with popular tools like GitHub and provides CLI access for scripting and deployment.
Paperspace User Experience
One of the best parts about Paperspace is its user-friendly interface. From the dashboard, users can manage machines, monitor usage, launch notebooks and configure storage with just a few clicks.
Even first-time users will find it easy to get started :
- The VM creation wizard walks you through OS selection, machine type, and storage.
- Notebook UI is clean, fast and responsive.
- Live logs and cost estimators help keep track of usage.
Paperspace Performance and Reliability
Paperspace has built its name around offering powerful GPUs at a fraction of the cost of traditional cloud providers. Performance generally lives up to the promise, especially for short to mid-term training runs and development.
Performance includes :
- Low-latency service with a RESTful API
- Solid performance with TensorFlow, PyTorch, and CUDA-based applications
That said, compared to hyperscalers like AWS or Azure, Paperspace might lack in global availability and ultra-high SLAs, making it better suited for dev and experimentation than enterprise-critical workloads.
Paperspace Pricing
Paperspace offers transparent and affordable pricing, especially when compared to bigger players.
Pay-as-you-go pricing
- For example, cost-effective GPU VMs like RTX A4000 for $0.40/hour
- Reserving high-end GPUs like the H100 can cost upwards of $2.24/hour. (Check out the full pricing here.)
- Notebooks and workflows in Gradient come with free tiers and usage-based billing
- Storage, bandwidth and snapshots are charged separately
You can also opt for monthly reserved instances or dedicated usage, which lowers the hourly rate if you commit long-term. For those training large models or deploying ML pipelines regularly, pricing can add up. However, compared to AWS or GCP GPU instances, the cost savings can be significant, sometimes up to 50–70%.
Paperspace Use Cases
Paperspace is tailored to a particular audience: those who want GPU compute without the complexity and overhead of traditional cloud providers.
Here’s what it’s ideal for :
- AI/ML developers : Fast access to Jupyter Notebooks and GPU compute
- Startups & researchers : Cost-effective experimentation and prototyping
- Educators & students : Simple UI and affordable pricing for learning
- Game developers : Rendering and 3D workloads with GPU acceleration
- Freelancers & small teams : Project-based GPU use without big commitments
Community and Support
Paperspace has an active online community, along with documentation and Discord forums to help users find answers and troubleshoot. Also, Paperspace provides two levels of support :
- Standard : Free support available seven days a week, including access to ticket-based assistance.
- Enterprise : Support provided through contractual agreement, including help with technical infrastructure, best-effort support for third-party software, and a dedicated team of customer success managers.
Is Paperspace Worth It?
Paperspace is a powerful, flexible and accessible cloud GPU provider. The platform offers low-cost GPU access, a user-friendly interface and full-featured ML development tools, an ideal choice for developers, researchers and scalable businesses alike.
That said, it’s not a one-size-fits-all solution. Larger companies may find it lacking in features like multi-region failover, enterprise compliance or autoscaling infrastructure. But for most solo developers or teams looking to accelerate AI and ML workflows without breaking the bank, Paperspace is worth exploring.
FAQs
1. What is Paperspace used for?
Paperspace is used to run GPU-powered virtual machines, train machine learning models, and build AI workflows in the cloud.
2. How much does Paperspace cost?
Paperspace offers affordable GPU pricing starting at $0.40/hour with options for reserved instances and usage-based billing.
3. Is Paperspace good for machine learning?
Paperspace is a great choice for machine learning, offering Jupyter notebooks, powerful GPUs, and an end-to-end MLOps platform.
4. Does Paperspace support custom containers?
Yes, Paperspace supports custom Docker containers, letting users run tailored environments and automate tasks via API or CLI.
5. How does Paperspace compare to AWS or GCP?
Paperspace provides a simpler setup and significantly lower GPU pricing compared to AWS or GCP, ideal for developers and startups.
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