Different Teams, Different Needs
Data scientists often prefer hands-on GPU access — SSH, Jupyter notebooks, and the flexibility to experiment freely with credit-based billing.
Developers, especially those familiar with services like OpenAI or Pinecone, often prefer managed endpoints they can integrate directly into their applications.
Supporting both shouldn't require running two separate platforms or becoming a GPU infrastructure specialist.
One Platform. Two Experiences.
For Data Scientists
VM-like access, credit-based billing
7 Instances
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Jupyter Notebook
Teaching & POC ready
Remote Access
Full control via SSH
Remote Access
Full control via SSH
Familiar cloud-like experience
For Developers
OpenAI-compatible API endpoints
7 Instances
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LLM Endpoint
OpenAI-compatible API
LLM Endpoint
OpenAI-compatible API
LLM Endpoint
Ready-to-use, no config
Familiar API-first experience
Built for Infrastructure Teams
Enterprise-grade infrastructure management without the complexity. Everything you need to manage GPU resources across your organization.
Multi-Tenant Isolation
Complete resource isolation between teams. Each workspace is fully separated with dedicated compute and storage.
Role-Based Access Control
Fine-grained permissions for teams and projects. Control who can access, deploy, and manage GPU resources.
Flexible Quota System
Credit-based quotas instead of fixed time slots. Teams use GPU when needed, no wasted allocations.
Team Workspace Management
Self-serve workspace provisioning. Teams get up and running without waiting for IT tickets.
Role-Based Access Control
Complete Visibility & Control
Monitor, track, and manage all GPU resources from a single dashboard.
Unified Dashboard
Single pane of glass for all GPU resources across teams.
Real-Time Monitoring
Live GPU utilization, memory, and performance metrics.
Usage Analytics
Track consumption by team, project, and user.
Audit Logging
Complete audit trail for compliance and governance.