Overview

Gradient brings browser-based notebooks, project sharing, and model deployment into a single environment. Deployments expose models through API endpoints and allow choices of runtime, instance type, and autoscaling. That makes it relevant to teams moving from experiments toward serving; users who only need a framework or local development environment may prefer a narrower tool.

Key features

Notebooks, containers, and compute

The browser IDE launches GPU-enabled Jupyter notebooks, and lightweight Docker containers make notebook environments portable. GitHub integration supports managing work and compute resources with git, while support for major machine-learning frameworks and libraries broadens its fit across projects. On-demand GPU and IPU instances are priced per second, which supports workload-based use but means subscription fees alone are not a reliable compute budget.

Collaboration and model serving

Users can share projects and invite collaborators; paid Pro and Growth plans include private projects. API endpoint deployment offers runtime, instance-type, and autoscaling choices, useful when a team wants its development workspace connected to serving. The trade-off is that teams need to weigh endpoint compute use as well as plan charges.

Security and support

Paperspace describes centralized permissions, activity logs, and round-the-clock threat monitoring. Its datacenters meet SOC 1, SOC 2, PCI-DSS, and ISO 27001 standards, a useful foundation for organizations with compliance requirements. Free ticket-based support runs seven days a week; enterprise support is contract-based and includes infrastructure assistance and customer success managers.

Pricing

Gradient has a free entry point and paid plans beginning at $8/mo. The subscription price is only part of the cost when using paid instances: utilization is billed separately, with per-second pricing. Storage and instance access vary by tier, so the cheapest plan is best treated as a constrained starting point rather than a full compute allowance.

Plan	Price	What it includes
Free	0.00 USD per free	Public projects, 5GB storage, and basic instances.
Pro	8.00 USD per month	Private projects, 15GB storage, and mid-range instances; paid-instance utilization costs extra.
Growth	39.00 USD per month	Private projects, 50GB storage, high-end instances, and Expert Support; paid-instance utilization costs extra.

Free works for public-project experimentation within 5GB and basic instances, but it gives up private projects and the stronger instance tiers. Pro is the practical step for an individual or small project that needs privacy and mid-range instances, while Growth is aimed at workloads needing more storage, high-end instances, and Expert Support. Neither paid tier removes the separate utilization charges.

Other plan structures also describe individual and team subscriptions: Free (Individual) is 0.00 USD per free with 5GB storage; Pro (Individual) is 8.00 USD per month with 15GB; Growth (Individual) is 39.00 USD per month with 50GB. Free (Team) is 0.00 USD per free with 5GB; Pro (Team) is 12.00 USD per month with 15GB; Growth (Team) is 39.00 USD per month with 50GB. Paid instances add utilization costs on individual plans, and Pro (Team) is billed monthly. These storage-based tiers make sense when selecting by account type, but teams should account for the higher Pro price and compute charges.

The notebook-tier structure is more explicit about capacity: T0 is 0.00 USD per free for 10 notebooks, one running notebook, 10GB persistent storage, and low instance types; paid instance utilization costs extra. T1 is 12.00 USD per month, billed user/month, for 100 notebooks, 10 running notebooks, 500GB persistent storage, and low–mid instance types, also with utilization charges. T2 has custom pricing and includes low-to-high instance types, private notebooks, scalable storage, and unlimited notebooks and running notebooks. T0 is suitable for small trials, while T1 raises concurrency and storage for active users; T2 targets teams that have outgrown fixed notebook limits.

Platforms

Gradient is available through API, Linux, macOS, web, and Windows. The native Paperspace app supports Windows 10+, OS X 10.13+, and Linux beta, with hotkeys, drag-and-drop uploads, and multi-monitor support. The desktop app is useful for users who want a native interface, while Linux support remains beta.

Who it's for

Gradient suits individual ML/AI engineers, data scientists, researchers, teams, research groups, and startups that need notebook collaboration and a path to API serving. It is less compelling for framework users who do not need hosted notebooks, GPU/IPU capacity, or deployment, or for teams that need predictable total costs without tracking instance utilization.

Pros and cons

Pros: GPU-enabled Jupyter notebooks, Docker containers, GitHub integration, and model API deployment connect development and serving in one platform.
Pros: Paid tiers add private projects, more storage, and stronger instance choices; T1 and T2 notebook options also scale concurrency and notebook limits.
Pros: Centralized permissions, activity logs, stated datacenter compliance standards, and seven-day ticket support address operational concerns.
Cons: Paid-instance utilization is extra, so the $8.00 per month Pro subscription does not cap compute spending.
Cons: Free access is limited to public projects and 5GB storage on the core plan, restricting private or storage-heavy work.
Cons: Linux support in the native app is beta, and enterprise support requires a contract.

Alternatives

Amazon SageMaker Autopilot is a paid option with a free trial and pay-as-you-go pricing, making it the alternative to consider when usage-based billing is preferred over Gradient's subscription tiers. Apache TVM is free and open source under Apache License 2.0, so it is a better fit for readers seeking an open-source software option. TensorFlow is free and open source, and is the framework alternative for readers looking for that rather than a hosted notebook-and-serving platform. Trackio offers a free library and Hugging Face hosting with no stated plan limits, which may suit those seeking a free tracking-focused option. PaddlePaddle is a free framework alternative. PyTorch is a free framework alternative. Valohai has paid subscriptions, a free trial, and a fixed fee per user per month with custom quotes, making it an option for readers evaluating a separately priced experiment-management platform. ONNX Runtime is free and open source, an alternative for readers looking for a runtime rather than Gradient's broader workspace.

For category browsing, see Deep Learning Software, Data Science Platforms, and Coding Playgrounds.

Verdict

Choose Gradient if your team wants GPU-backed notebook development, collaboration, and API deployment tied together, with tier choices for individual through higher-capacity notebook work. Look elsewhere if you need only an open-source framework or runtime, or if a subscription plus separately billed instance utilization makes costs too difficult to control.

	Hexagon-ML	Paperspace Gradient
Free plan	No	Yes
Free trial	No	No
Paid from	—	$8/mo
Open source	No	No
Platforms	Web	api, Linux, macOS, Web, Windows
Model deployment	Yes	—
Supported languages	Python, R, Julia	—
Free plan	—	Yes

Hexagon-ML vs Paperspace Gradient

Hexagon-ML

Paperspace Gradient