diff --git a/menu/navigation.json b/menu/navigation.json
index 6b0905c5db..0b084d5902 100644
--- a/menu/navigation.json
+++ b/menu/navigation.json
@@ -900,6 +900,10 @@
"label": "Monitor a deployment",
"slug": "monitor-deployment"
},
+ {
+ "label": "Configure autoscaling",
+ "slug": "configure-autoscaling"
+ },
{
"label": "Manage allowed IP addresses",
"slug": "manage-allowed-ips"
diff --git a/pages/managed-inference/concepts.mdx b/pages/managed-inference/concepts.mdx
index 45a4dab29b..0ebec3bc72 100644
--- a/pages/managed-inference/concepts.mdx
+++ b/pages/managed-inference/concepts.mdx
@@ -71,6 +71,10 @@ LLMs are advanced artificial intelligence systems capable of understanding and g
These models, such as Llama-3, are trained on vast amounts of data to learn the patterns and structures of language, enabling them to generate coherent and contextually relevant responses to queries or prompts.
LLMs have applications in natural language processing, text generation, translation, and other tasks requiring sophisticated language understanding and production.
+## Node number
+
+The node number (or node count) defines the number of nodes, or Instances, that are running your Managed Inference deployment. [Increasing the node number](/managed-inference/how-to/configure-autoscaling/) scales your deployment, so that it can handle more load.
+
## Prompt
In the context of generative AI models, a prompt refers to the input provided to the model to generate a desired response.
diff --git a/pages/managed-inference/how-to/configure-autoscaling.mdx b/pages/managed-inference/how-to/configure-autoscaling.mdx
new file mode 100644
index 0000000000..63a56bc438
--- /dev/null
+++ b/pages/managed-inference/how-to/configure-autoscaling.mdx
@@ -0,0 +1,40 @@
+---
+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.
+
+
+This feature is currently in [Public Beta](https://www.scaleway.com/betas/).
+
+
+
+
+ - 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 deployment 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 > **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.
+
+ High availability is only guaranteed with two or more nodes.
+
+5. Click **Update node count** to update the number of nodes in your deployment.
+
+ Your deployment will be unavailable for 15-30 minutes while the node update is in progress.
+
\ No newline at end of file
diff --git a/pages/managed-inference/how-to/create-deployment.mdx b/pages/managed-inference/how-to/create-deployment.mdx
index ad5ed35260..9c98e3c4b6 100644
--- a/pages/managed-inference/how-to/create-deployment.mdx
+++ b/pages/managed-inference/how-to/create-deployment.mdx
@@ -28,12 +28,16 @@ dates:
- 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. Note that this feature is currently in [Public Beta](https://www.scaleway.com/betas/).
+
+ High availability is only guaranteed with two or more nodes.
+
+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.
- 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.
-6. Click **Deploy model** to launch the deployment process. Once the model is ready, it will be listed among your deployments.
\ No newline at end of file
+7. Click **Deploy model** to launch the deployment process. Once the model is ready, it will be listed among your deployments.
\ No newline at end of file
diff --git a/pages/managed-inference/quickstart.mdx b/pages/managed-inference/quickstart.mdx
index d95f94faeb..beed812381 100644
--- a/pages/managed-inference/quickstart.mdx
+++ b/pages/managed-inference/quickstart.mdx
@@ -43,6 +43,7 @@ Here are some of the key features of Scaleway Managed Inference:
- Choose the geographical **region** for the deployment.
- Specify the GPU Instance type to be used with your deployment.
+ - Choose the number of nodes for your deployment. Note that this feature is currently in [Public Beta](https://www.scaleway.com/betas/).
4. Enter a **name** for the deployment, along with optional tags to aid in organization.
5. Configure the **network** settings for the deployment:
- Enable **Private Network** for secure communication and restricted availability within Private Networks. Choose an existing Private Network from the drop-down list, or create a new one.