Skip to main content

What is PaletteAI?

Deploying AI/ML applications on Kubernetes is complex. Data science teams need GPU-accelerated clusters, specialized storage, and properly configured networking; however, they would rather focus on experiments and models than infrastructure. Meanwhile, platform teams want to provide self-service access while maintaining control over resources, costs, and security.

PaletteAI solves this by providing a Kubernetes-native platform where platform teams manage infrastructure and data science teams deploy applications through a self-service interface - no Kubernetes expertise required.

Key Concepts​

To achieve this self-service model, PaletteAI has four key concepts:

  • Profile Bundles - Reusable templates (bundles) that package infrastructure configurations and applications, providing consistent, repeatable deployments.
  • Compute Pools - Shared or dedicated Kubernetes clusters where AI/ML applications run. Clusters are provisioned using Palette as the infrastructure platform.
  • App Deployments - Deploy AI/ML applications, such as Run:ai and ClearML.
  • Tenants and Projects - Organizational hierarchy that provides isolation, GPU quotas, and role-based access control (RBAC). Tenants represent organizations or teams; Projects are workspaces within Tenants.

Integration with Palette​

PaletteAI integrates with Palette to automate Kubernetes cluster provisioning with:

  • Infrastructure templates (Cluster Profiles) for OS, Kubernetes, networking, and storage
  • GPU hardware discovery and management
  • Edge host and bare metal orchestration

Ready-to-Use Application Catalog​

The PaletteAI Studio provides a catalog of pre-built Profile Bundles for common use cases, such as Run:ai and ClearML.

Each Profile Bundle includes all dependencies and configurations needed to quickly deploy your application or model. These profiles can be customized to fit your needs, and teams can create their own profiles to build custom, in-house stacks. With Profile Bundles, teams can quickly and repeatably deploy many instances of applications, increasing velocity and productivity while reducing the time spent on configuring and deploying AI and ML stacks.

Who Benefits?​

Data Science Teams​

With PaletteAI, data scientists can deploy AI/ML environments in minutes and get to work without worrying about Kubernetes. Bringing repeatability and consistency to application and model deployments, data scientists can focus on what matters to them instead of being bogged down with complex infrastructure configuration and deployment complexities. Whether initial deployment or ongoing lifecycle management, PaletteAI makes sure applications are kept up-to-date.

  • No Infrastructure Expertise Required - Click to deploy ClearML, Run:ai, or model servers
  • Consistent Environments - Same configuration across development, staging, and production
  • Self-Service Access - Deploy when needed without waiting for platform teams
  • GPU Management - Automatic allocation and quota enforcement

Platform Engineering Teams​

Platform engineers configure PaletteAI to provide self-service access for data scientists. They tailor compute resources with the right hardware (GPUs, storage, networking) and customize Profile Bundles to meet the needs of AI/ML applications while meeting security standards and maintaining control. This declarative approach makes life easier for platform engineering teams with consistency, repeatability, and all the enterprise-grade controls and governance they need for both training and inferencing clusters.

  • Reusable Templates - Define infrastructure once in Profile Bundles and deploy everywhere
  • GPU Quotas - Enforce limits at Tenant and Project levels to control costs
  • Multi-Tenancy - Isolated workspaces with RBAC for different teams
  • Policy Enforcement - Ensure clusters meet security and compliance requirements
  • GitOps Compatible - All configurations stored as Kubernetes custom resources

Example Workflow​

  1. Platform engineer creates Tenants and Projects for data scientists to separate permissions, GPU quotas, compute resources, and more based on team needs.
  2. Platform engineer creates Cluster Profiles in Palette to define the base infrastructure and registers machines that PaletteAI discovers via the Compute resource.
  3. Platform engineer uses Compute Config to set default networking and node configurations for clusters deployed from the Project.
  4. Platform engineer creates Profile Bundles with GPU cluster configuration and ClearML Server dependencies.
  5. Data scientist selects that Profile Bundle and deploys ClearML to track experiments.
  6. PaletteAI provisions the cluster (or uses an existing one) and deploys the application. Configurations such as SSH keys, VIPs, and more are set automatically using the defaults from Compute Config.
  7. Data scientist connects to ClearML and starts logging experiments.

Next Steps​

Choose your path to get started:

  • Install PaletteAI - Follow our Appliance Installation Guide to create a cluster using the PaletteAI ISO and deploy self-hosted Palette and PaletteAI.
  • Look Under the Hood - Review the Architecture and Concept pages to understand how PaletteAI delivers a seamless AI/ML deployment experience.
  • Discover Resources - Browse our Resources for custom resource definitions and more.