> This page is for Multi-Tenant, version v3.0 (default).
> For other versions, use one of these documentation indexes:
> - v3.0 (default): https://docs.nvidia.com/run-ai/multi-tenant/v3.0/llms.txt
> - v2.26: https://docs.nvidia.com/run-ai/multi-tenant/v2.26/llms.txt
> - v2.25: https://docs.nvidia.com/run-ai/multi-tenant/v2.25/llms.txt
> - v2.24: https://docs.nvidia.com/run-ai/multi-tenant/v2.24/llms.txt
> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/run-ai/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/run-ai/_mcp/server.
# Control Plane Installation
This page describes how to install the NVIDIA Run:ai control plane on your Kubernetes cluster using Helm. The control plane provides the central management layer for NVIDIA Run:ai multi-tenant, handling tenant management, resource and access management, as well as workload submission and monitoring across tenant clusters.
This procedure includes:
* Configuring key settings such as domain name, ingress, and administrator credentials
* Deploying the control plane into the `runai-backend` namespace
By completing this process, the NVIDIA Run:ai control plane will be running in your cluster and accessible via the configured domain, ready to onboard tenants.
## Before You Start
Before installing the NVIDIA Run:ai control plane, complete [Preparations](/run-ai/multi-tenant/documentation/installation-and-configuration/install/manual-helm/preparations).
## Permissions
As part of the installation, you will be required to install the NVIDIA Run:ai control plane [Helm chart](https://helm.sh/). The Helm chart requires Kubernetes administrator permissions. You can review the exact objects that are created by the chart using the `--dry-run` flag.
## Installation
**Note**
* To customize the installation based on your environment, see [Advanced Control Plane Configurations](/run-ai/self-hosted/documentation/installation-and-configuration/config/advanced-control-plane-configuration).
* PostgreSQL and Keycloakx are installed with default usernames and passwords. To change the default credentials, see [Advanced Control Plane Configurations](/run-ai/self-hosted/documentation/installation-and-configuration/config/advanced-control-plane-configuration).
1. Create a `values.yaml` file using the appropriate example below based on your [metrics backend](/run-ai/multi-tenant/documentation/installation-and-configuration/system-requirements/metrics-store-requirements#supported-backends):
```yaml
global:
domain: # example: "runai.hostorg.com"
multiTenancy:
enabled: true
management:
user:
password:
metricsService:
config:
datasourceUrl: # example: https://prometheus-prod-13-prod-us-east-0.grafana.net/api/prom
tenantsManager:
config:
defaultMetricStore:
read:
auth:
basic:
password:
username: "1434499"
url: # example: https://prometheus-prod-13-prod-us-east-0.grafana.net/api/prom
useXscopeHeader: true
write:
auth:
basic:
password:
username: "1434499"
url: # example: https://prometheus-prod-56-prod-us-east-2.grafana.net/api/prom
thanos:
enabled: false
authorization:
config:
isCSPEnv: true
```
```yaml
global:
domain: # example: "runai.hostorg.com"
multiTenancy:
enabled: true
management:
user:
password:
metricsService:
config:
datasourceUrl: # example: http://mimir-query-frontend.monitoring.svc:8080/prometheus
tenantsManager:
config:
defaultMetricStore:
read:
auth:
basic:
password: ''
username: ''
url: # example: http://mimir-query-frontend.monitoring.svc:8080/prometheus
useXscopeHeader: true
write:
auth:
basic:
password: ''
username: ''
url: <> # example: http://mimir-distributor.monitoring.svc:8080/api/v1/push
thanos:
enabled: false
authorization:
config:
isCSPEnv: true
```
2. Run the following commands to install the NVIDIA Run:ai control plane using your configured `values.yaml` file. Use the commands for the release type you are installing.
**Open Source**
```bash
helm repo add runai https://helm.ngc.nvidia.com/nvidia/runai-oss --force-update
helm repo update
helm upgrade -i runai-backend -n runai-backend runai/control-plane \
-f /path/to/your/values.yaml
```
**Support-Entitled**
In addition to the values in your `values.yaml` file, update the Helm commands as follows:
* Add `--set supportEntitled=true` to the `helm upgrade` command.
* Add `--set telemetryService.enabled=true` to the `helm upgrade` command to enable telemetry.
* Add `--set global.imagePullSecrets[0].name=runai-reg-creds` to the `helm upgrade` command to reference the secret created in [Registry Credentials](/run-ai/multi-tenant/documentation/installation-and-configuration/install/manual-helm/preparations#registry-credentials).
* Optional: associate NVIDIA Run:ai's telemetry with your NCA ID by adding one of the following to the `helm upgrade` command. See [Registry Credentials](/run-ai/multi-tenant/documentation/installation-and-configuration/install/manual-helm/preparations#registry-credentials) for how to generate the NGC Service API Key. By default, telemetry is not associated with your organization.
- `--set telemetryService.telemetryAuth.token=` - Passes the Service API Key directly. Visible in shell history and process listings.
- `--set telemetryService.telemetryAuth.existingSecret= --set telemetryService.telemetryAuth.secretKey=` - References a secret you create yourself beforehand - the chart does not create or manage it.
```bash
helm repo add runai https://helm.ngc.nvidia.com/nvidia/runai-oss --force-update
helm repo update
helm upgrade -i runai-backend -n runai-backend runai/control-plane \
-f /path/to/your/values.yaml \
--set supportEntitled=true \
--set telemetryService.enabled=true \
--set global.imagePullSecrets[0].name=runai-reg-creds \
--set telemetryService.telemetryAuth.token=
```
**Note**
* To install a specific version, add `--version ` to the install command. You can find all available versions by using the `helm search repo runai/control-plane --versions` command.
* Use the `--dry-run` flag to gain an understanding of what is being installed before the actual installation.
## Next Steps
Set up [API Access](/run-ai/multi-tenant/documentation/installation-and-configuration/install/guided-installation/control-plane-setup/api-access-setup) to authenticate your API requests, then onboard your first tenant. See [Create a Tenant and Cluster](/run-ai/multi-tenant/documentation/installation-and-configuration/install/manual-helm/onboard-a-tenant/create-a-tenant-and-cluster). Before installing a tenant's cluster, make sure you have completed the required setup in [Prerequisites and Features](/run-ai/multi-tenant/documentation/installation-and-configuration/install/manual-helm/onboard-a-tenant/prerequisites-and-features) on that tenant's own Kubernetes cluster.