Plans & Pricing

Execute training runs utilizing git-integrated cloud compute in your local CLI

Manage datasets, model artifacts, and other resources, and integrate your own cloud accounts within our Spell filesystem

Define and schedule end-to-end processes from training to deployment for automation and reproducibility

Create hosted Jupyer environments in the cloud, accessible from anywhere

Leverage grid, random, and bayesian hyperparameter searches and visualize your results

Distribute DL training loads with our native Horovod integration

Compare and visualize training, hardware, and other custom metrics and metadata within our web console

Utilize our CLI tools and Python API to customize logging and tracking

Invite team members and organize team projects within your private organization account

Create models from best performing training runs and record their artifacts to prepare for deployment

Serve single or multiple trained models in real-time or batch, backed by a managed Kubernetes service for maximum scalability and robustness

Track end-to-end lineage and metadata from dataset to deployed server, and reproduce any model backed by Spell runs as needed

Make use of existing Google accounts for authentication and other necessary security features for your organization (e.g. multi-factor authentication)

Control data access and user privileges for your organization from our web dashboard

Configure Spell around an existing cloud provider account, backed entirely by your own VPC and compute instances

Integrate your existing enterprise authentication system with Spell's services (e.g. LDAP, Active Directory)

Integrate your existing private storage volumes (e.g. NFS, HDFS) along with customized data access controls

Use Spell's public cloud GPU clusters for training models

Deploy Spell within your own public-cloud backed account, managed within a VPC

Host Spell fully on-prem within your own bare-metal, private cloud, or other infrastructural setup

Join our public Slack channel for questions, comments, and feedback, directly to our Engineering teams

Use a private Slack channel for your organization for dedicated issue support

Obtain white glove service backed by our enterprise level SLA

Hire our experienced ML solutions engineers for various deep learning advisory services