You reach an AI Virtual Machine with virtctl, the KubeVirt command line tool, against the Compute Pool's cluster. virtctl proxies through the cluster's KubeVirt API, so there is no separate SSH endpoint to expose and no firewall rule to add. The PaletteAI console provides the kubeconfig for the cluster, but it does not host a terminal or a console session, so virtctl is required.
These tasks cover creating AI Virtual Machines, connecting to them, and managing them. For what an AI Virtual Machine is and how it relates to a Compute Pool, refer to AI Virtual Machines.
The built-in templates create a user named pai with a well-known password, so that you can sign in the first time. Replace that credential before you put anything important on the virtual machine.
This guide describes how to configure model pricing at the System scope. Model pricing is the install-wide catalog of per-model rates (USD per 1,000,000 tokens) that the Inference Launchpad gateway uses for cost accounting and that the Cost dimension of Inference Quotas is measured against. PaletteAI federates the catalog to every Launchpad-enabled spoke; each served model that appears in the catalog gets an explicit rate, and every other served model falls back to the Default rate. Without a Default and an explicit price for a model, that model's requests do not contribute to the Cost dimension of any quota.
Settings define integrations and configuration values used by Projects and Compute Pools. For integration types, prerequisites, and examples, refer to Settings and Integrations.
Create an AI Virtual Machine to get a GPU or CPU virtual machine with its own guest operating system on a Compute Pool. For what an AI Virtual Machine is and how it relates to a Compute Pool, refer to AI Virtual Machines.
This guide describes how to deploy an Inference Launchpad cluster from PaletteAI. An Inference Launchpad cluster is a standard Compute Pool whose Profile Bundle opts the spoke in to Token Metering by carrying the palette.ai/launchpad-enabled=true annotation. Once you have such a Compute Pool, deploying a vLLM-backed Model Deployment to it is the same workflow as any other Model Deployment — the Inference Launchpad gateway on the spoke automatically proxies the model.
This page covers how to reference outputs and user variables using PaletteAI's macro system in workload profiles. The different source types for macros include:
PaletteAI provides the controls that cover everyday use of an AI Virtual Machine: starting it, stopping it, cloning it, and deleting it. For management beyond those controls, use the VMO console on the Compute Pool's cluster. Refer to Manage the Virtual Machine in VMO.
This guide describes how to create, copy, edit, revoke, and delete API Keys for Token Metering. An API Key is a bearer credential that a client presents to the Inference Launchpad gateway and that draws against exactly one Inference Quota.
This guide describes how to create, edit, and delete Inference Quotas. An Inference Quota is a usage budget that the PaletteAI Inference Launchpad gateway enforces on inference requests. Each quota caps the volume a caller can consume over a rolling window, measured in Requests, Tokens, or Cost.
This section describes how to manage Token Metering from PaletteAI: creating and editing the Inference Quotas that cap metered usage, issuing the API Keys that attribute each caller's traffic to a quota, and configuring the Model Pricing that converts token counts into cost figures.
When an AI Virtual Machine does not reach Running, the reason is reported in the conditions on the resource. Inspect them first: