feat(infr): add scaling

menu/navigation.json

@@ -900,6 +900,10 @@
                     "label": "Monitor a deployment",
                     "slug": "monitor-deployment"
                   },
+                  {
+                    "label": "Scale a deployment",
+                    "slug": "scale-deployment"
+                  },
                   {
                     "label": "Manage allowed IP addresses",
                     "slug": "manage-allowed-ips"

pages/managed-inference/how-to/create-deployment.mdx

@@ -28,12 +28,16 @@ dates:
         </Message>
     - Choose the geographical **region** for the deployment.
     - Specify the GPU Instance type to be used with your deployment.
-4. Enter a **name** for the deployment, and optional tags.
-5. Configure the **network connectivity** settings for the deployment:
+4. Choose the number of nodes for your deployment.
+    <Message type="note">
+      High availability is only guaranteed with two or more nodes.
+    </Message>
+5. Enter a **name** for the deployment, and optional tags.
+6. Configure the **network connectivity** settings for the deployment:
     - Attach to a **Private Network** for secure communication and restricted availability. Choose an existing Private Network from the drop-down list, or create a new one.
     - Set up **Public connectivity** to access resources via the public internet. Authentication by API key is enabled by default.
     <Message type="important">
       - Enabling both private and public connectivity will result in two distinct endpoints (public and private) for your deployment.
       - Deployments must have at least one endpoint, either public or private.
     </Message>
-6. Click **Deploy model** to launch the deployment process. Once the model is ready, it will be listed among your deployments.
+7. Click **Deploy model** to launch the deployment process. Once the model is ready, it will be listed among your deployments.

pages/managed-inference/how-to/scale-deployment.mdx

@@ -0,0 +1,37 @@
+---
+meta:
+  title: How to scale Managed Inference deployments
+  description: This page explains how to scale Managed Inference deployments in size
+content:
+  h1: How to manage scale Managed Inference deployments
+  paragraph: This page explains how to scale Managed Inference deployments in size
+tags: managed-inference ai-data ip-address
+dates:
+  validation: 2025-06-03
+  posted: 2025-06-03
+categories:
+  - ai-data
+---
+
+You can scale your Managed Inference deployment up or down to match it to the incoming load of your deployment.
+
+
+<Macro id="requirements" />
+
+  - A Scaleway account logged into the [console](https://console.scaleway.com)
+  - A [Managed Inference deployment](/managed-inference/quickstart/)
+  - [Owner](/iam/concepts/#owner) status or [IAM permissions](/iam/concepts/#permission) allowing you to perform actions in the intended Organization
+
+## How to scale a Managed Inference deployement in size
+
+1. Click **Managed Inference** in the **AI** section of the [Scaleway console](https://console.scaleway.com) side menu. A list of your deployments displays.
+2. Click a deployment name or <Icon name="more" /> > **More info** to access the deployment dashboard.
+3. Click the **Settings** tab and navigate to the **Scaling** section.
+4. Click **Update node count** and adjust the number of nodes in your deployment.
+    <Message type="note">
+      High availability is only guaranteed with two or more nodes.
+    </Message>
+5. Click **Update node count** to update the number of nodes in your deployment.
+    <Message type="note">
+      Your deployment will be unavailable for 15-30 minutes while the node update is in progress.
+    </Message>