Concepts
This section covers the core concepts you need to understand when working with PaletteAI. Whether you are a platform engineer setting up infrastructure or a data scientist deploying workloads, these concepts explain how PaletteAI organizes resources and manages AI/ML deployments.
Organization & Access
PaletteAI uses a hierarchical structure to organize teams and control access to resources.
| Concept | Description |
|---|---|
| Tenants and Projects | Organizational hierarchy for multi-tenancy, GPU quotas, and team access control |
| Settings | External integrations (Palette API credentials) and namespace-scoped configuration |
| Roles and Permissions | RBAC roles automatically created for Tenants and Projects |
Infrastructure
These resources define where and how your AI/ML applications run.
| Concept | Description |
|---|---|
| Compute | Discovers available machines for cluster provisioning |
| Compute Config | Default settings for cluster deployment (networking, SSH, node configurations) |
| Compute Pools | Kubernetes clusters where applications run (dedicated or shared modes) |
Applications
These resources define what gets deployed to your infrastructure.
| Concept | Description |
|---|---|
| Profile Bundles | Reusable packages combining Palette Cluster Profiles and PaletteAI Workload Profiles |
| App Deployments | Deploy AI/ML applications to Compute Pools |
Deployment Flow
A typical deployment flow involves these concepts working together:
- Platform setup - A platform engineer creates a Tenant with Settings for Palette integration
- Project creation - Teams get Projects with GPU quotas and role-based access
- Infrastructure provisioning - Compute discovers available machines; Compute Pools provision Kubernetes clusters
- Application packaging - Profile Bundles package infrastructure and application configurations
- Deployment - Data scientists create App Deployments using Profile Bundles on Compute Pools
For a deeper look at the system architecture, refer to our Architecture guide.