Paperspace
UnclaimedBuild, train, and deploy AI/ML models on accelerated cloud GPUs with simplicity and scalability.
Visit WebsiteReviews onG2Capterra
36 reviews trackedThe Bottom Line
Entry price
Free plan available, paid tiers above
Biggest pro
Significantly reduces compute costs compared to major public clouds or self-hosting.
Biggest con
Specific instance types and their availability may vary.
TL;DR - Paperspace
- Accelerated cloud GPU platform for AI/ML development.
- Supports full ML lifecycle: develop, train, and deploy models.
- Offers scalable, on-demand GPU access with per-second billing.
Pricing: Free plan available
Best for: Growing teams
4.0/5 across review platforms
What is Paperspace?
Paperspace, now part of DigitalOcean, provides an accelerated cloud computing platform specifically designed for AI and Machine Learning workloads. It offers access to powerful GPUs, including NVIDIA H100, enabling users to develop, train, and deploy AI applications efficiently. The platform is built to simplify complex infrastructure management, allowing individuals and teams to focus on model development rather than server maintenance. It supports the entire ML lifecycle from launching notebooks for proof-of-concept to training and fine-tuning models, and finally converting them into scalable API endpoints.
The platform caters to a wide range of users, from individual ML engineers and data scientists to large teams and startups. It emphasizes speed, affordability, and scalability, offering low-cost GPUs with per-second billing and no long-term commitments. Paperspace aims to remove infrastructure bottlenecks, providing features like instant provisioning, job scheduling, resource provisioning, and automatic versioning. It also includes collaboration tools and insights for team management, making it a comprehensive solution for building and scaling next-generation AI applications.
Available on: Web
Pros & Cons
Pros
- Significantly reduces compute costs compared to major public clouds or self-hosting.
- Simplifies AI/ML infrastructure management, allowing focus on model development.
- Offers flexible, on-demand scaling with no long-term commitments.
- Provides a comprehensive platform for the entire ML lifecycle, from development to deployment.
- Includes collaboration features for team-based projects.
Cons
- Specific instance types and their availability may vary.
- Free tier has limitations on storage and auto-shutdown duration.
Ratings Across the Web
4(36 reviews)
Ratings aggregated from independent review platforms. Learn more
Key Features
NVIDIA H100 GPU access for AI/ML workloadsML platform for building, training, and deploying models (Gradient)Fully-managed cloud GPU platform (CORE)1-click hosted Jupyter NotebooksEnd-to-end MLOps capabilitiesLow-cost GPUs with per-second billingOn-demand pricing with no commitmentsInstant provisioning of instances
Pricing Plans
Pricing checked May 29, 2026
Free
$0
- FREE GPU
- Public projects
- Auto-shutdown (12 hour limit)
- 5GB storage
- Basic instances
Pro
$8 / month
- Private projects
- 15GB storage
- Auto-shutdown (configurable)
- Mid-range instances
- Faster free GPUs (based on availability)
Growth
$39 / month
- Private projects
- 50GB storage
- Auto-shutdown (configurable)
- High-end instances
- Expert Support
T0 Small Teams
$0 + utilization costs on paid instance types
- Low Instance Types
- Public Notebooks
- 10GB Persistent Storage included
- Auto-shutdown (12 hour limit)
- Total Notebooks: 10
- Running Notebook limit: 1
T1 Mid-Size Teams
$12 user/month
- Low - Mid Instance Types
- Private Notebooks
- 500GB Persistent Storage included
- Auto-shutdown (configurable)
- Total Notebooks: 100
- Running Notebook limit: 10
T2 Large Teams
Contact Sales
- Low - High Instance Types
- Private Notebooks
- Scalable Storage
- Auto-shutdown (configurable)
- Unlimited Notebooks
- Unlimited Running Notebooks
Enterprise
Contact Sales
- Host Gradient on your own infrastructure
- Run without limits
- Custom plans
Reviews
4.0/5
Across 36 verified user reviews on Capterra, G2
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Paperspace FAQ
How does Paperspace's per-second billing for GPUs compare to other cloud providers?
Paperspace offers per-second billing for GPU compute, which can result in up to 70% savings compared to major public clouds. This model ensures you only pay for the exact duration of your usage, eliminating costs for idle time.
What is the difference between the CORE and Gradient products within Paperspace?
CORE is a fully-managed cloud GPU platform providing virtual servers, storage, and networking options for general accelerated computing. Gradient is specifically an ML platform built on CORE, designed for building, training, and deploying Machine Learning models of any size and complexity, offering features like 1-click hosted Notebooks and MLOps tools.
Can I integrate Paperspace with my existing private cloud or on-premise infrastructure?
Yes, Paperspace offers solutions for private cloud, on-premise deployments, and hybrid environments. The Managed Service and Private Cluster options for Gradient allow for deployment on private Azure/AWS/GCP/Paperspace clouds or on-premise installations like DGX.
What kind of reproducibility features does Paperspace offer for ML experiments?
Paperspace provides full reproducibility for ML experiments through automatic versioning, tagging, and life-cycle management. This ensures that your models and their development history are tracked and can be recreated consistently.
How does the auto-shutdown feature work for Notebooks, and can it be configured?
Notebooks on Paperspace include an auto-shutdown feature to manage costs. For the Free plan, there's a 12-hour limit. For Pro and Growth plans, the auto-shutdown is configurable, allowing users to set their preferred duration before instances are automatically shut down.
What types of GPUs are available on the Paperspace platform for AI/ML workloads?
Paperspace provides access to a range of powerful GPUs, including NVIDIA H100, which are optimized for AI and ML workloads. The platform offers various instance types, from basic to high-end, to suit different computational needs.
Source: paperspace.com