# Scale the platform

Grow from one cluster to a multi-region fleet.

Source: https://docs.modelplane.ai/getting-started/scale-the-platform/


You have one L4 cluster with a running model. In this guide, you'll add two
larger-GPU clusters in different regions to grow the fleet available to the ML team.

Provisioning two more clusters takes about 10 to 15 minutes.

## Register more clusters

{{< tabs >}}
{{< tab "EKS" >}}
Register two more clusters with a bigger hardware class: `L40S` (`48 Gi`) in
`us-west` and `eu-central`:

{{< manifests "getting-started/eks/platform-scale.yaml" >}}

{{< hint "note" >}}
`g6e.xlarge` runs ~$2/hr on demand. Two of them plus the `L4` from earlier is a
few dollars for this tour. Clean up when you're done (see [Clean
up]({{< ref "getting-started/clean-up.md" >}})).
{{< /hint >}}
{{< /tab >}}
{{< tab "GKE" >}}
Register two more clusters with a bigger hardware class: `A100` (`40 Gi`) in
`us-west` and `us-east`. Apply the manifest, setting each cluster's `project` to
your GCP project:

{{< manifests path="getting-started/gke/platform-scale.yaml" apply="false" >}}

{{< editCode >}}
```bash
curl -fsSL {{< manifest-url "getting-started/gke/platform-scale.yaml" >}} \
  | sed 's/my-gcp-project/$@<your-gcp-project>$@/g' \
  | kubectl apply -f -
```
{{< /editCode >}}

{{< hint "note" >}}
`a2-highgpu-1g` runs ~$3.50/hr on demand. Two of them plus the `L4` from earlier
is a few dollars for this tour. Clean up when you're done (see [Clean
up]({{< ref "getting-started/clean-up.md" >}})).
{{< /hint >}}
{{< /tab >}}
{{< /tabs >}}

Modelplane provisions both clusters in parallel:

```bash
kubectl wait --for=condition=Ready ic --all --timeout=20m
```

## Your model keeps running

Growing the fleet doesn't disturb anything already deployed. `qwen-demo` stays
on its original cluster and the two new clusters add capacity the moment
they're `Ready` with no interruption for the ML team. A replica only moves if
its deployment changes in a way that no longer fits where it runs. 

## Next step

The fleet now spans three clusters across three regions. The ML team is next. [Scale the model]({{< ref "getting-started/scale-the-model.md" >}}) to serve it from two regions behind a single endpoint.