Paperspace Gradient
Paperspace Gradient: A low-cost GPU notebook platform with a strong machine-learning workflow. Ranked #64 of 137 in Coding Playgrounds by our editors (3.0/10); pricing: Free plan; best for low-cost GPU notebook experimentation.
At a glance
- Editor score3.0 / 10
- PricingFree plan
- Best forLow-cost GPU notebook experimentation
- Free planYes
- Paid from$8/mo
- Facts checked22 Sep 2026
Where it wins
- CPU and GPU-backed Jupyter notebooks in the browser
- Workflows, model repositories, and container-based deployments
- Free plan plus private projects on paid tiers
Where it doesn't
- Compute usage is billed separately by selected instance type
- Free plans use public projects or notebooks
- Machine-learning focus may not suit general coding experiments
Our verdict on Paperspace Gradient
Paperspace Gradient is a cloud-based machine-learning platform for individuals, researchers, data scientists, and teams. Its web-based Jupyter IDE runs on CPU- and GPU-backed notebook machines, with persistent storage, notebook sharing and forking, custom containers, and runtime dependencies. The platform also includes Machine-learning Workflows, a model repository, and container-based Deployments that expose models through REST API endpoints. It is aimed primarily at experimenting with, training, and serving machine-learning models rather than general-purpose coding.
Pricing spans free and paid plans for individuals and teams. Free (Individual) and Free (Team) include public projects, 5GB of storage, and basic instances, with paid instances billed by the hour. Pro (Individual) costs $8 per month and adds private projects, 15GB of storage, mid-range instances, and faster free GPUs based on availability. Growth (Individual) costs $39 per month and adds 50GB of storage, high-end instances, and Expert Support. Team plans include Pro at $12 per month, while Growth costs $39 per month; both add private projects, with storage and instance access varying by tier. The T0 and T1 plans provide another pricing structure, including persistent storage and notebook limits.
Gradient’s strongest advantage is the path from notebook experimentation to repeatable workflows and model deployment within one platform. Web and API access support browser-based work and programmatic control, while shared storage and notebook collaboration fit team projects. The trade-off is that subscription fees do not cover all compute: usage charges depend on the selected instance type. Public projects on free plans may also be unsuitable for private work. Pick Gradient for affordable GPU experimentation and machine-learning development with deployment options; choose a broader coding playground if general programming, rather than models and data science, is the priority.
Your product? Our editors can test Paperspace Gradient in depth and publish a review, labelled as paid · from $149/mo →
Paperspace Gradient pricing
All 9 Paperspace Gradient plans and prices →
Paperspace Gradient fact sheet
| Free plan | Yes |
|---|---|
| Paid from | $8/mo |
| Real-time collaboration | Not verified |
| Private projects | Yes |
| Full-stack runtime | Not verified |
| Deployment options | Full_stack |
| Project limit | Not verified |
| Runtime languages | Not verified |
| Sharing controls | Not verified |
| Deployment | Cloud, Self-hosted |
| Platforms | Web |
| Compliance & security | SSO/SAML |
| Support | Tickets, Docs |
| Built for | Solo, Small business, Mid-market, Enterprise (editorial estimate) |
| Pricing | Free plan (source) |
| Website | paperspace.com |
| Facts checked | 22 Sep 2026 |
Alternatives to Paperspace Gradient
- ReplitA mature browser-based workspace with persistence, publishing, AI assistance, and team features.9.3
- CodeSandboxA flexible coding playground for collaborative development, automation, and AI workflows.9.2
- Firebase StudioA free, AI-assisted cloud workspace for prototyping and deploying full-stack apps.9.1
See all Paperspace Gradient alternatives →
Also Ranked In
Paperspace Gradient is ranked in 5 of our directories.
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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