Paperspace
Claim this toolBuild, 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 Jul 6, 2026
Free
Free
- 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
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4.0/5
Across 36 verified user reviews on Capterra, G2
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Paperspace FAQ
How does Paperspace support the entire machine learning lifecycle?
Paperspace provides a comprehensive platform that supports the entire ML lifecycle, from launching notebooks for proof-of-concept to training and fine-tuning models. It also enables converting models into scalable API endpoints for deployment. This allows users to manage their AI applications from development to production within a single environment.
Which teams would benefit most from using Paperspace?
Paperspace is designed for a wide range of users, including individual ML engineers, data scientists, and large teams or startups. It is particularly beneficial for those who need to build and scale next-generation AI applications efficiently. The platform's collaboration tools and simplified infrastructure management support team-based projects effectively.
How is Paperspace priced?
Paperspace offers a free tier for initial use, with paid plans available for increased usage and additional features. It utilizes per-second billing for its low-cost GPUs, providing flexibility without requiring long-term commitments. This model allows users to scale their compute resources on demand.
Can Paperspace reduce compute costs compared to other cloud providers?
Yes, Paperspace is designed to significantly reduce compute costs when compared to major public clouds or self-hosting solutions. It offers access to powerful GPUs with a cost-effective, per-second billing model. This allows users to optimize their spending on AI/ML workloads.
What kind of limitations might users encounter with Paperspace?
Users might encounter variations in the availability of specific instance types on Paperspace. Additionally, the free tier has certain limitations regarding storage capacity and the duration of auto-shutdown. These factors should be considered when planning projects.
How does Paperspace compare to a competitor like Lambda Labs?
Paperspace, similar to Lambda Labs, provides accelerated cloud computing specifically for AI and Machine Learning workloads, offering access to powerful GPUs like NVIDIA H100. However, Paperspace emphasizes simplifying the entire ML lifecycle within a single platform, from development to deployment, with integrated collaboration tools and automatic versioning. It also aims to reduce compute costs compared to major public clouds.
Does Paperspace offer tools for managing AI/ML infrastructure?
Yes, Paperspace simplifies AI/ML infrastructure management by providing features like instant provisioning, job scheduling, and resource provisioning. This allows users to focus on model development rather than server maintenance. The platform also includes automatic versioning and collaboration tools to streamline team workflows.
Source: paperspace.com