Overview
Last updated
Last updated
This section of the documentation covers our previous generation of Gradient. For the current version go to .
Gradient clusters are private clusters that run machine learning workloads. Gradient clusters can be on Paperspace Cloud, on any other cloud provider (AWS, GCP, Azure), or on your own servers via the .
Find out more about Gradient's multi-cloud capabilities .
You can using the Web UI in a couple of clicks.
Managed Service (multi-tenant)
Managed Private Clusters
Self-Hosted Clusters
Infrastructure:
Shared, managed by Paperspace
Private, managed by Paperspace
Private, self-hosted
Setup time:
None
Setup time: 10 minutes
Setup time: 20-30 minutes
Features:
Notebooks, experiments, deployments, model repo, data management, GradientCI
Notebooks, experiments, deployments, model repo, data management, GradientCI
Target audience:
Hobbyists & students
Startups & SMBs running production workloads
Mid-market & enterprise businesses conducting ML at scale
This section of our documentation covers the private cluster options. If you are looking to use our managed service, just to get started right away.
Compute, Storage, & Networking The Kubernetes master node, storage, and networking cost to run the cluster is $0.12/hr. Private Clusters require a minimum of one CPU node for cluster orchestration and clusters include 500GB of storage by default.
In addition, instances used to run workloads are charged at the regular rate (see ) plus a small .
Notebooks (including !), basic experiments
Compute, Storage, & Networking Customers are responsible for their infrastructure costs. Gradient does not bill for any compute, storage, and networking costs other than the .
Subscription Gradient Private Clusters require a Essentials or great .