If you find yourself in need of shared persistent storage for applications running on OVHcloud Managed Kubernetes Service (MKS), then OVHcloud Enterprise File Storage (EFS) with Trident CSI offers you a practical way to provision and manage it.

This blog post explains how to create and connect OVHcloud EFS to your MKS cluster using Trident CSI, so you can dynamically provision persistent storage for Kubernetes workloads.

OVHcloud Enterprise File System (EFS)

EFS is a high-performance, fully managed file storage solution powered by NetApp ONTAP in an active-active architecture. It is designed for enterprise workloads requiring high availability, predictable performance, and seamless integration with cloud-native environments.

The service is available in multiple regions, including Roubaix, Gravelines, Strasbourg, Limbourg, and Beauharnois, with a strong SLA of 99.99% uptime. Storage capacity ranges from 50 GB up to 29 TB.

EFS delivers guaranteed performance with 4,000 IOPS and 64 MB/s throughput per TiB, scaling linearly with volume size thanks to NVMe SSD infrastructure.

Built for modern infrastructures, EFS integrates natively with Kubernetes via Trident CSI (compatible with MKS) and supports ReadWriteMany (RWX) access. It operates within a single availability zone (1AZ) and provides low-latency NFS storage over OVHcloud’s secure vRack network, ensuring strong security and compliance.

NetApp Trident CSI

Trident is an open-source, fully supported storage orchestration project maintained by NetApp. It is designed to help Kubernetes applications consume persistent storage using standard interfaces such as the Container Storage Interface (CSI).

Trident runs directly inside Kubernetes clusters as a set of Pods and enables dynamic provisioning and management of storage for containerized workloads. It allows applications to easily access persistent storage from NetApp’s ecosystem, including ONTAP systems (like the OVHcloud EFS).

Let’s do it!

EFS creation

We already have a MKS cluster, in GRA11 region, running inside a private network and a subnet, with a gateway.
We also already have a vRack and our Public Cloud Project attached to this vRack.
So in this blog post we will only create a new EFS in eu-west-rbx region, attached to a vRackServices, inside the same subnet that our existing MKS cluster.

Here you can see the architecture of all the services:

⚠️ EFS and MKS regions may differ; be aware that latency between different regions may impact your storage workloads performance. It’s highly recommended to keep your storage and compute as close as possible.

We will deploy the EFS in eu-west-rbx instead of in eu-west-gra region to show you that it is possible.

To deploy the EFS, we will use the Terraform OVHcloud EFS module.

The module we will use can deploy all the components necessary to use EFS with a MKS cluster (like you can see in the schema).

But in this blog post we will assume that we already deployed:

• a vRack
• a Private Network
• a Private Subnet
• a Gateway
• a MKS cluster

So using the Terraform module we will fill the existing resources information and ask Terraform to create:

• an OAuth2 credential
• an IAM policy
• an EFS
• a vRack Services

Let’s deploy our components with Terraform!

Create a provider.tf file and fill it with the information:

terraform {
  required_providers {
    ovh = {
      source  = "ovh/ovh"
      version = ">= 2.12.0"
    }
    null = {
      source  = "hashicorp/null"
      version = ">= 3.0.0"
    }
  }

  required_version = ">= 1.7.0"
}

provider "ovh" {
}

If you don’t define the provider information inside this file, as was shown in this example, you can instead set the environment variables with your credentials:

# OVHcloud provider needed keys
export OVH_ENDPOINT="ovh-eu"
export OVH_APPLICATION_KEY="xxx"
export OVH_APPLICATION_SECRET="xxx"
export OVH_CONSUMER_KEY="xxx"
export OVH_CLOUD_PROJECT_SERVICE="xxx"

Create a variable.tf.template file and fill it with these information:

# Existing services
variable "service_name" {
  default = "$OVH_CLOUD_PROJECT_SERVICE"
}

variable "vrack_id" {
  default = "pn-1234567" #ID of your existing vRack
}

variable "vlan_id" {
  default = "666" #ID of your VLAN
}

variable "private_network_id" {
  default = "d111cb65-1234-5678-9012-dac2e93b8944" #ID of your private network
}

variable "private_subnet_id" {
  default = "d8dc2469-1234-5678-9012-1f86551d3466" #ID of your subnet
}

variable "vrackservices_subnet_service_range_cidr" {
  default = "192.168.168.248/29" #CIDR of your private network
}

variable "private_subnet_cidr" {
  default = "192.168.168.0/24" #CIDR of your subnet
} 

variable "mks_region" {
  default = "GRA11" #Region of your existing MKS cluster
}

variable "mks_cluster_id" {
  default = "7c3e1e6e-1234-5678-9012-4fb5a5b145e7" #ID of your existing MKS cluster
}

# Services to create

variable "oauth2_client_name" {
  default = "efs-trident-client-example"
}

variable "oauth2_client_description" {
  default = "OAuth2 client for EFS Trident integration"
}

variable "iam_policy_name" {
  default = "efs-trident-policy-example"
}

variable "iam_policy_description" {
  default = "IAM policy for EFS Trident access"
}

variable "vrackservices_attach_to_efs" {
  description = "Whether to attach the EFS service endpoint to vRack Services. Set to false before destroying."
  type        = bool
  default     = true
}

variable "efs_region" {
  default = "eu-west-rbx"
}

variable "efs_name" {
  default = "my-efs-storage"
}

variable "efs_plan" {
  default = "enterprise-file-storage-premium-1tb"
}

⚠️ In the file, replace the IDs, CIDR & MKS region with your existing resources information.

Replace the value of the OVH_CLOUD_PROJECT_SERVICE environment variable in the variables.tf file:

envsubst < variables.tf.template > variables.tf

Create a efs.tf file and fill it with the information:

module "ovh_efs_trident" {
  source = "ovh/efs/ovh//modules/efs-trident"

  # OVH region for EFS and vRack Services
  region = var.efs_region

  # Public Cloud region for MKS and private network
  public_cloud_region = var.mks_region

  # VLAN ID must be the same for vRack Services and Public Cloud private network
  vlan_id = var.vlan_id

  # Set to false before destroying to detach endpoint first
  vrackservices_attach_to_efs = var.vrackservices_attach_to_efs

  # EFS creation
  storage_efs_name      = var.efs_name
  storage_efs_plan_code = var.efs_plan

  # --- vRack ---
  create_vrack       = false
  vrack_service_name = var.vrack_id

  # --- Cloud Project ---
  create_cloud_project        = false
  cloud_project_id            = var.service_name
  bind_vrack_to_cloud_project = false # Set to false if already bound

  # --- Private Network ---
  create_private_network      = false
  private_network_id = var.private_network_id

  # --- Private Subnet ---
  create_private_subnet      = false
  private_subnet_id = var.private_subnet_id

  # --- Gateway ---
  create_gateway = false  # Set to false only if existing network has gateway

  # --- MKS Cluster ---
  create_mks_cluster = false
  mks_cluster_id     = var.mks_cluster_id # mks-priv-gra11
  create_node_pool   = false # Set to false if using existing node pool

  # OAuth2 and IAM
  oauth2_client_name        = var.oauth2_client_name
  oauth2_client_description = var.oauth2_client_description
  iam_policy_name           = var.iam_policy_name
  iam_policy_description    = var.iam_policy_description

  # Network (shared between vRack Services and Public Cloud)
  private_network_subnet_cidr             = var.private_subnet_cidr
  vrackservices_subnet_service_range_cidr = var.vrackservices_subnet_service_range_cidr # EFS gets IPs here
}

Create an output.tf file with the following content:

output "client_id" {
    value = module.ovh_efs_trident.client_id
}

output "client_secret" {
    value = module.ovh_efs_trident.client_secret
    sensitive = true
}

output "efs_id" {
  value       = module.ovh_efs_trident.efs_id
}

The Terraform configuration is ready. Let’s init it:

terraform init

The output should be like this:

$ terraform init

Initializing the backend...
Initializing modules...
Initializing provider plugins...
- Reusing previous version of hashicorp/null from the dependency lock file
- Reusing previous version of ovh/ovh from the dependency lock file
- Using previously-installed hashicorp/null v3.2.4
- Using previously-installed ovh/ovh v2.13.1

Terraform has been successfully initialized!

You may now begin working with Terraform. Try running "terraform plan" to see
any changes that are required for your infrastructure. All Terraform commands
should now work.

If you ever set or change modules or backend configuration for Terraform,
rerun this command to reinitialize your working directory. If you forget, other
commands will detect it and remind you to do so if necessary.

Apply it:

terraform apply

The output should be like this:

$ terraform apply

module.ovh_efs_trident.data.ovh_me.my_account: Reading...
module.ovh_efs_trident.data.ovh_cloud_project_kube.existing[0]: Reading...
module.ovh_efs_trident.data.ovh_cloud_project.existing[0]: Reading...
module.ovh_efs_trident.data.ovh_me.my_account: Read complete after 1s [id=xx12345-ovh]
module.ovh_efs_trident.data.ovh_cloud_project.existing[0]: Read complete after 0s
module.ovh_efs_trident.data.ovh_order_cart.cart: Reading...
module.ovh_efs_trident.data.ovh_order_cart.cart: Read complete after 0s [id=d582ab7c-1234-5678-9012-4a6e702ea4c5]
module.ovh_efs_trident.data.ovh_cloud_project_kube.existing[0]: Read complete after 5s [id=7c3e1e6e-1234-5678-9012-4fb5a5b145e7]

Terraform used the selected providers to generate the following execution plan. Resource actions are indicated with the following symbols:
  + create

Terraform will perform the following actions:

  # module.ovh_efs_trident.null_resource.config_validation will be created
  + resource "null_resource" "config_validation" {
      + id = (known after apply)
    }

  # module.ovh_efs_trident.ovh_iam_policy.iam_policy will be created
  + resource "ovh_iam_policy" "iam_policy" {
      + allow       = [
          + "storageNetApp:apiovh:get",
          + "storageNetApp:apiovh:serviceInfos/get",
          + "storageNetApp:apiovh:share/accessPath/get",
          + "storageNetApp:apiovh:share/acl/create",
          + "storageNetApp:apiovh:share/acl/delete",
          + "storageNetApp:apiovh:share/acl/get",
          + "storageNetApp:apiovh:share/create",
          + "storageNetApp:apiovh:share/delete",
          + "storageNetApp:apiovh:share/edit",
          + "storageNetApp:apiovh:share/extend",
          + "storageNetApp:apiovh:share/get",
          + "storageNetApp:apiovh:share/revertToSnapshot",
          + "storageNetApp:apiovh:share/snapshot/create",
          + "storageNetApp:apiovh:share/snapshot/delete",
          + "storageNetApp:apiovh:share/snapshot/edit",
          + "storageNetApp:apiovh:share/snapshot/get",
        ]
      + created_at  = (known after apply)
      + description = "IAM policy for EFS Trident access"
      + id          = (known after apply)
      + identities  = (known after apply)
      + name        = "efs-trident-policy-example"
      + owner       = (known after apply)
      + read_only   = (known after apply)
      + resources   = (known after apply)
      + updated_at  = (known after apply)
    }

  # module.ovh_efs_trident.ovh_me_api_oauth2_client.api_oauth2_client will be created
  + resource "ovh_me_api_oauth2_client" "api_oauth2_client" {
      + client_id     = (known after apply)
      + client_secret = (sensitive value)
      + description   = "OAuth2 client for EFS Trident integration"
      + flow          = "CLIENT_CREDENTIALS"
      + id            = (known after apply)
      + identity      = (known after apply)
      + name          = "efs-trident-client-example"
    }

  # module.ovh_efs_trident.ovh_storage_efs.efs[0] will be created
  + resource "ovh_storage_efs" "efs" {
      + created_at        = (known after apply)
      + iam               = (known after apply)
      + id                = (known after apply)
      + name              = "my-efs-storage"
      + order             = (known after apply)
      + ovh_subsidiary    = "FR"
      + performance_level = (known after apply)
      + plan              = [
          + {
              + configuration = [
                  + {
                      + label = "region"
                      + value = "eu-west-rbx"
                    },
                  + {
                      + label = "network"
                      + value = "vrack"
                    },
                ]
              + duration      = "P1M"
              + plan_code     = "enterprise-file-storage-premium-1tb"
              + pricing_mode  = "default"
            },
        ]
      + product           = (known after apply)
      + quota             = (known after apply)
      + region            = (known after apply)
      + service_name      = (known after apply)
      + status            = (known after apply)
    }

  # module.ovh_efs_trident.ovh_vrack_vrackservices.vrack-vrackservices-binding[0] will be created
  + resource "ovh_vrack_vrackservices" "vrack-vrackservices-binding" {
      + id             = (known after apply)
      + service_name   = "pn-1234567"
      + vrack_services = (known after apply)
    }

  # module.ovh_efs_trident.ovh_vrackservices.vrackservices[0] will be created
  + resource "ovh_vrackservices" "vrackservices" {
      + checksum        = (known after apply)
      + created_at      = (known after apply)
      + current_state   = (known after apply)
      + current_tasks   = (known after apply)
      + iam             = (known after apply)
      + id              = (known after apply)
      + order           = (known after apply)
      + ovh_subsidiary  = "FR"
      + plan            = [
          + {
              + configuration = [
                  + {
                      + label = "region_name"
                      + value = "eu-west-rbx"
                    },
                ]
              + duration      = "P1M"
              + plan_code     = "vrack-services"
              + pricing_mode  = "default"
            },
        ]
      + resource_status = (known after apply)
      + target_spec     = {
          + subnets = [
              + {
                  + cidr              = "192.168.168.0/24"
                  + service_endpoints = [
                      + {
                          + managed_service_urn = (known after apply)
                        },
                    ]
                  + service_range     = {
                      + cidr = "192.168.168.248/29"
                    }
                  + vlan              = 666
                    # (1 unchanged attribute hidden)
                },
            ]
        }
      + updated_at      = (known after apply)
    }

Plan: 6 to add, 0 to change, 0 to destroy.

Changes to Outputs:
  + client_id     = (known after apply)
  + client_secret = (sensitive value)
  + efs_id        = (known after apply)

Do you want to perform these actions?
  Terraform will perform the actions described above.
  Only 'yes' will be accepted to approve.

  Enter a value: yes

module.ovh_efs_trident.null_resource.config_validation: Creating...
module.ovh_efs_trident.null_resource.config_validation: Creation complete after 0s [id=8553589333890826101]
module.ovh_efs_trident.ovh_me_api_oauth2_client.api_oauth2_client: Creating...
module.ovh_efs_trident.ovh_storage_efs.efs[0]: Creating...
module.ovh_efs_trident.ovh_me_api_oauth2_client.api_oauth2_client: Creation complete after 0s [id=EU.xxxxxxxxxxxxx]
module.ovh_efs_trident.ovh_storage_efs.efs[0]: Still creating... [00m10s elapsed]
module.ovh_efs_trident.ovh_storage_efs.efs[0]: Still creating... [00m20s elapsed]
module.ovh_efs_trident.ovh_storage_efs.efs[0]: Still creating... [00m30s elapsed]
...
module.ovh_efs_trident.ovh_storage_efs.efs[0]: Still creating... [03m40s elapsed]
module.ovh_efs_trident.ovh_storage_efs.efs[0]: Still creating... [03m50s elapsed]
module.ovh_efs_trident.ovh_storage_efs.efs[0]: Creation complete after 3m52s [id=c2d759de-cd63-4e28-aaab-a7599aad2ca8]
module.ovh_efs_trident.ovh_vrackservices.vrackservices[0]: Creating...
module.ovh_efs_trident.ovh_iam_policy.iam_policy: Creating...
module.ovh_efs_trident.ovh_iam_policy.iam_policy: Creation complete after 0s [id=a434d1a4-1234-5678-9012-cf54251eee52]
module.ovh_efs_trident.ovh_vrackservices.vrackservices[0]: Still creating... [00m10s elapsed]
module.ovh_efs_trident.ovh_vrackservices.vrackservices[0]: Still creating... [00m20s elapsed]
...
module.ovh_efs_trident.ovh_vrackservices.vrackservices[0]: Still creating... [01m20s elapsed]
module.ovh_efs_trident.ovh_vrackservices.vrackservices[0]: Creation complete after 1m30s [id=vrs-a00-b11-c22-d33]
module.ovh_efs_trident.ovh_vrack_vrackservices.vrack-vrackservices-binding[0]: Creating...
module.ovh_efs_trident.ovh_vrack_vrackservices.vrack-vrackservices-binding[0]: Still creating... [00m10s elapsed]
module.ovh_efs_trident.ovh_vrack_vrackservices.vrack-vrackservices-binding[0]: Still creating... [00m20s elapsed]
...
module.ovh_efs_trident.ovh_vrack_vrackservices.vrack-vrackservices-binding[0]: Still creating... [01m40s elapsed]
module.ovh_efs_trident.ovh_vrack_vrackservices.vrack-vrackservices-binding[0]: Creation complete after 1m43s [id=vrack_pn-1234567-vrackServices_vrs-a00-b11-c22-d33]

Apply complete! Resources: 6 added, 0 changed, 0 destroyed.

Outputs:

client_id = "EU.xxxxxxxxxxxxx"
client_secret = <sensitive>
efs_id = "c2d759de-cd63-4e28-aaab-a7599aad2ca8"

Save the OAuth2 credentials in environment variables:

export EFS_CLIENT_ID=$(terraform output -raw client_id)
export EFS_CLIENT_SECRET=$(terraform output -raw client_secret)

Trident CSI Installation

Install the Trident operator in your MKS cluster:

helm repo add netapp-trident https://netapp.github.io/trident-helm-chart

helm install trident-operator netapp-trident/trident-operator \
  --version 100.2502.1 \
  --create-namespace \
  --namespace trident \
  --set tridentSilenceAutosupport=true \
  --set operatorImage="ovhcom/trident-operator:25.02.1-linux-amd64" \
  --set tridentImage="ovhcom/trident:25.02.1-linux-amd64"

You should have a result like this:

$ helm install trident-operator netapp-trident/trident-operator \
  --version 100.2502.1 \
  --create-namespace \
  --namespace trident \
  --set tridentSilenceAutosupport=true \
  --set operatorImage="ovhcom/trident-operator:25.02.1-linux-amd64" \
  --set tridentImage="ovhcom/trident:25.02.1-linux-amd64"

NAME: trident-operator
LAST DEPLOYED: Tue Apr 28 14:01:19 2026
NAMESPACE: trident
STATUS: deployed
REVISION: 1
TEST SUITE: None
NOTES:
Thank you for installing trident-operator, which will deploy and manage NetApp's Trident CSI
storage provisioner for Kubernetes.

Your release is named 'trident-operator' and is installed into the 'trident' namespace.
Please note that there must be only one instance of Trident (and trident-operator) in a Kubernetes cluster.

To configure Trident to manage storage resources, you will need a copy of tridentctl, which is
available in pre-packaged Trident releases.  You may find all Trident releases and source code
online at https://github.com/NetApp/trident.

To learn more about the release, try:

  $ helm status trident-operator
  $ helm get all trident-operator

Once the installation is complete, verify that all Trident pods are in Running state in the trident namespace before proceeding:

$ kubectl get pods -n trident

NAME                                  READY   STATUS    RESTARTS      AGE
trident-controller-5bf6c8d6f6-g95jq   6/6     Running   0             119s
trident-node-linux-4xtjr              2/2     Running   1 (82s ago)   119s
trident-node-linux-6w5ff              2/2     Running   1 (82s ago)   119s
trident-node-linux-r7hxp              2/2     Running   0             119s
trident-operator-859f59c58b-2z2ts     1/1     Running   0             2m31s

Trident Backend Creation

The Trident backend connects NetApp Trident to the OVHcloud EFS service using the IAM credentials previously created.

1. Secret Creation

Create a Kubernetes Secret containing the connection information that allows Trident to access the OVHcloud API. Create a trident-secret.yaml.template file with the following content:

apiVersion: v1
kind: Secret
metadata:
  name: ovh-efs-secret
type: Opaque
stringData:
  clientID: "$EFS_CLIENT_ID"         # your clientId
  clientSecret: "$EFS_CLIENT_SECRET" # your clientSecret

Replace the clientID and clientSecret values by the OAuth2 client we created with Terraform:

envsubst < trident-secret.yaml.template > trident-secret.yaml

Apply the secret in your cluster:

kubectl apply -f trident-secret.yaml -n trident

Check that the secret has been correctly created:

$ kubectl get secret ovh-efs-secret -n trident

NAME             TYPE     DATA   AGE
ovh-efs-secret   Opaque   2      3s

2. Trident Backend Creation

Create your backend with the command below:

cat <<EOF | kubectl create -n trident -f -
apiVersion: trident.netapp.io/v1
kind: TridentBackendConfig
metadata:
  name: ovh-efs-rbx
spec:
  version: 1
  backendName: backend-ovh-efs
  defaults:
    exportRule: "192.168.168.0/24"    # CIDR of your network for NFS ACLs
  storageDriverName: ovh-efs
  clientLocation: ovh-eu
  location: eu-west-rbx         # Location of your EFS service
  serviceLevel: premium
  nfsMountOptions: rw,hard,rsize=65536,wsize=65536,nfsvers=3,tcp
  credentials:
    name: ovh-efs-secret
  volumeCreateTimeout: "60" 
EOF

⚠️ The ovh-efs storage driver must be used. Replace exportRule, location, and other parameters with values matching your environment.

Verify that the backend has been created correctly with the command below:

$ kubectl get TridentBackendConfig -n trident

NAME          BACKEND NAME      BACKEND UUID                           PHASE   STATUS
ovh-efs-rbx   backend-ovh-efs   ace12d67-70ea-44e1-abd8-20d016f7f030   Bound   Success

Use EFS in your MKS cluster

This section describes how to expose Enterprise File Storage to Kubernetes workloads using Trident.

1. StorageClass

In a sc_efs.yaml file, define a StorageClass to enable dynamic provisioning via the Trident CSI driver:

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: ovh-efs-premium
provisioner: csi.trident.netapp.io
parameters:
  backendType: "ovh-efs"
  fsType: "nfs"
allowVolumeExpansion: true

Apply the StorageClass:

kubectl apply -f sc_efs.yaml

Check that the StorageClass has been created:

$ kubectl get sc ovh-efs-premium

NAME              PROVISIONER             RECLAIMPOLICY   VOLUMEBINDINGMODE   ALLOWVOLUMEEXPANSION   AGE
ovh-efs-premium   csi.trident.netapp.io   Delete          Immediate           true                   3h13m

This StorageClass allows volumes to be provisioned on demand and expanded dynamically.

2. Volume Creation (PVC)

Create a PersistentVolumeClaim with ReadWriteMany (RWX) access mode. Create a pvc_efs.yaml file with this content:

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: premium-pvc-efs
spec:
  accessModes:
    - ReadWriteMany
  resources:
    requests:
      storage: 100Gi
  storageClassName: ovh-efs-premium

Apply it:

kubectl apply -f pvc_efs.yaml

Verify that the PVC has been created with the command below:

kubectl get pvc premium-pvc-efs

At this point, the EFS is creating a volume, attach the correct ACL to it and mount it in the PVC

After a little time, the output should show the PVC in Bound state:

$ kubectl get pvc

NAME              STATUS   VOLUME                                     CAPACITY   ACCESS MODES   STORAGECLASS      VOLUMEATTRIBUTESCLASS   AGE
premium-pvc-efs   Bound    pvc-faca364d-ad76-44ec-9bc9-959c0d33c515   100Gi      RWX            ovh-efs-premium   <unset>                 3m43s

The volume has been created through the PVC and you can now mount it in a Pod 🎉.

Conclusion

In this blog, we’ve explained how to create an EFS and use it in a MKS cluster through Trident CSI. This will give you a flexible, production-ready approach to persistent shared storage in Kubernetes.

We recommend you also take a look at our Cloud Roadmap & Changelog for an overview of all the coming features for OVHcloud Public Cloud products.

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We’ve opted for a flexible training model because we trust our customers. This means that you can train independently through e-learning, whenever you want, choosing the courses that are relevant to your business and the people who need to be trained, according to their role. This saves you time and money and, thanks to the expertise you’ll gain through training, you’ll be able to deploy more cost-effective and efficient cloud solutions.

Our training courses and certifications give you a more qualified workforce.

In addition to validating your technical skills, our certifications can help you develop your expertise. Plus, you can demonstrate this with our certification badges, which help you gain recognition.

Finally, OVHcloud offers a wide range of solutions. Our training courses will help you discover the scope of these solutions and when and how to use them. Each course is designed to help you deliver a great experience for your customers. Simply by having the right knowledge to effectively identify the best solution to meet their needs.

Once trained, you are also likely to have ideas for new projects.

Get certified and give your expertise a headstart.

Discover our Training Portal’s catalog to boost your expertise.

https://startup.ovhcloud.com/Get started with the OVHcloud Startup Program and accelerate your growth today. Apply now and discover how our program can help you achieve your goals: https://startup.ovhcloud.com/

Mia Experts is a new generation medical software platform designed by a physician, for physicians. From the very beginning, the product was built to integrate artificial intelligence in a way that is useful, secure, and aligned with the realities of medical practice.

Today, many doctors spend a significant part of their day dealing with administrative tasks rather than focusing on patient care and clinical decision-making. Existing medical software is often outdated, poorly designed, and disconnected from how physicians actually work.

Mia Experts aims to change that. By leveraging artificial intelligence, the platform automates repetitive tasks and structures medical data in a meaningful and usable way. The goal is simple: give physicians back their time.

The solution primarily targets private practitioners, particularly in general medicine and surgical specialties, where efficient data management, reliability, and time savings are critical.

Built from Real Medical Experience

The idea behind Mia Experts originated from the daily experience of Vincent Salabi, a surgeon who repeatedly encountered the same issue: medical software that was slow, repetitive, and time-consuming.

Instead of helping doctors, these tools often added friction to their workflow.

At the same time, a major technological shift was occurring: artificial intelligence was becoming accessible in a way that could be deployed securely and within a sovereign regulatory framework.

Mia Experts was born from the collaboration of three co-founders with complementary expertise — medical, technical, and entrepreneurial — united by a shared ambition: to fundamentally rethink the physician’s digital workspace.

Early Milestones and Key Achievements

From the earliest stages, several key milestones helped shape the development of Mia Experts.

One of the first successes was designing the software architecture. The team built a simple, modular, and scalable architecture capable of intelligently interacting with both patient and physician data.

The objective was clear: eliminate unnecessary repetition, ensure every piece of data has meaning, and enable reliable data usage — whether for prescription generation or reducing medical errors.

Operating in the highly regulated healthcare sector also required building an infrastructure compliant with Health Data Hosting (HDS) regulations. Mia Experts chose OVHcloud, ensuring health data sovereignty and providing a robust and secure cloud foundation.

Infrastructure management is handled in partnership with Lecpac Consulting, allowing the team to meet regulatory requirements while focusing on product development and innovation.

Another major milestone came through early presentations at medical conferences, particularly in orthopedic and urological surgery. The response from physicians was extremely positive. The software’s usability and clinical logic quickly generated word-of-mouth interest — even among doctors who had not been directly approached.

Mia Experts also achieved several regulatory and technological milestones:

• LAP certification for prescription software, obtained in collaboration with healthtech company Posos
• INSi compliance, enabling integration with national health identity standards

Even before official product launch, the startup received around 50 pre-orders purely through demonstrations and conference discussions.

The platform is now entering its beta testing phase, with the first deployments planned soon.

Core Values Driving the Product

The development of Mia Experts is guided by a set of strong principles:

• Simplicity – intuitive interfaces designed for real medical workflows
• Pragmatism – AI must deliver measurable time savings
• Data sovereignty – full control over hosting and infrastructure
• Health data security – non-negotiable protection standards
• Intelligent data structuring – ensuring reliable and actionable medical information

Business, Technical and Regulatory Complexity

Building a medical software platform involves navigating a unique combination of business, technological, and regulatory challenges.

From a business perspective, the first hurdle was securing funding while preserving technological independence. Mia Experts achieved this through an initial funding round involving physician investors, complemented by support from Bpifrance and the French Tech Grant program.

On the technical side, the strict healthcare regulatory environment posed significant challenges. Compliance with HDS standards required implementing strong guarantees around security, traceability, service availability, and access governance from the very beginning.

Another critical challenge involved health data interoperability. Medical data must follow standardized national frameworks and coding systems. Mia Experts needed to structure and transform this data so it could interact seamlessly with national health services such as secure messaging systems and health data platforms.

Yet the biggest challenge was balancing all these constraints with a smooth user experience.

The ambition was never to create software that was simply compliant but difficult to use. Instead, the goal was to design a platform that remains intuitive, efficient, and truly supportive of physicians’ daily work.

Why Mia Experts Chose the Cloud

Cloud infrastructure quickly became a natural choice for the project.

First, artificial intelligence requires scalable computing resources. Running AI endpoints, fine-tuning models, and processing medical voice data demand infrastructure that can scale dynamically while protecting sensitive data.

Second, the cloud offers strong advantages for security and regulatory compliance. As a medical software publisher, Mia Experts needed an infrastructure capable of guaranteeing both data sovereignty and regulatory compliance within the European framework.

Finally, the cloud enables a much more agile product strategy. Unlike traditional locally installed medical software, cloud-based architecture allows centralized updates and continuous product improvement without disrupting physicians’ workflows.

For a fast-growing startup, this flexibility is essential.

Leveraging OVHcloud to Build a Sovereign Health Infrastructure

Choosing OVHcloud was a strategic decision for Mia Experts, especially in a context where health data sovereignty is a critical issue.

Many solutions rely on non-European cloud providers. OVHcloud allowed the startup to build its infrastructure on a secure, sovereign European cloud, fully compliant with French and EU regulations.

This has become a strong differentiator — both from a regulatory standpoint and in terms of trust with physicians.

The OVHcloud Startup Program also played a key role during the early development phase by helping offset the high technical costs associated with innovation.

Mia Experts relies heavily on speech-to-text and AI models for generating medical reports. Fine-tuning these models to understand medical vocabulary requires substantial computing power. The program allowed the team to train and test these models without immediate financial pressure.

The Infrastructure Behind Mia Experts

Today, the platform runs on a robust cloud architecture built on OVHcloud services, including:

• Managed Kubernetes for Dev, Pre-production, and Production environments
• S3-compatible object storage for medical documents and AI models
• GPU instances supporting real-time medical speech transcription
• AI Endpoints for LLMs such as Mistral, Llama, and GPT-OSS
• Dedicated Public Cloud instances hosting GitHub CI/CD runners

All infrastructure is hosted in France, ensuring compliance with GDPR and HDS requirements.

One major advantage of OVHcloud AI endpoints is transparency: customer data is not used to train external models, a key concern in healthcare environments.

Tangible Results and Impact

The collaboration with OVHcloud has enabled several concrete achievements.

First, Mia Experts successfully deployed an infrastructure fully compliant with HDS health data hosting standards, guaranteeing high levels of security, availability, and traceability.

Second, the startup has been able to build and control its own AI capabilities, particularly around speech recognition and medical text generation. The voice recognition system has already been adapted to medical vocabulary, delivering strong accuracy in clinical contexts.

Another key outcome is AI sovereignty. By hosting AI inference within a controlled European environment, Mia Experts retains full control over its data, models, and algorithms.

Finally, the cloud infrastructure provides significant operational agility. The team can deploy updates quickly, iterate on AI models, and continuously improve application performance.

Accelerating Product Adoption

These technological choices have significantly strengthened Mia Experts’ positioning within the medical software ecosystem.

The cloud infrastructure makes the solution eligible for Ségur V2 standards, a key regulatory benchmark for healthcare software interoperability in France.

This strengthens credibility with physicians and facilitates integration into the national digital health ecosystem.

By maintaining full control over its AI pipeline — from hosting to model fine-tuning — Mia Experts can guarantee both data confidentiality and high-quality performance tailored to medical language.

What’s Next for Mia Experts

The next step is the progressive onboarding of the first users, with around 50 pre-registrations already secured before the official launch.

In the medium term, the startup aims to reach:

• 300 users within two years
• 500 users within three years

At the same time, Mia Experts plans to expand beyond surgical specialties with the launch of Mia Experts for General Practice, followed by extensions into additional medical disciplines.

The long-term vision is to build a modular medical platform adaptable to multiple specialties while sharing a unified technological foundation.

Advice for Other Startups

For startups building AI-driven products, the Mia Experts team highlights three key lessons.

First, anticipate your data strategy early. AI models are only as good as the data used to train them. Structuring and preparing datasets before accessing cloud resources can provide a major competitive advantage.

Second, do not underestimate regulatory complexity, especially in sectors like healthcare. Partnering with an experienced infrastructure manager can significantly accelerate deployment.

Finally, think of the cloud not only as hosting infrastructure but as a strategic platform for innovation and scalability.

Conclusion

The journey of Mia Experts shows that innovation in healthcare requires a careful balance between technological ambition, regulatory rigor, and practical usability.

By building on a sovereign and compliant cloud infrastructure from the outset, the startup has laid strong foundations for developing a medical platform that genuinely supports physicians.

The collaboration with OVHcloud has enabled Mia Experts to deploy a secure, scalable, and AI-ready infrastructure, ensuring full control over both health data and AI models.

For startups operating in highly regulated sectors, choosing the right cloud ecosystem can make all the difference — enabling innovation, accelerating growth, and building trust from day one.

Don’t let infrastructure costs limit your growth. We strongly urge other startups to join the OVHcloud Startup Program. Contact their team to build your own foundation for sustainable success.

If you’re a startup looking to transform your business, we encourage you to join the OVHcloud Startup Program or contact OVHcloud to discover how our solutions can support your journey!

This reference architecture demonstrates how to deploy a Large Language Model (LLM) inference system using vLLM on OVHcloud Managed Kubernetes Service (MKS). The solution leverages NVIDIA L40S GPUs to serve the Qwen3-VL-8B-Instruct multimodal model (vision + text) with OpenAI-compatible API endpoints.

This comprehensive guide shows you how to deploy, to scale automatically, and how to monitor vLLM-based LLM workloads on the OVHcloud infrastructure.

What are the key benefits?

• Cost-effectiveness: Leverage managed services to minimise operational overhead
• Real-time observability: Track Time-to-First-Token (TTFT), throughput, and resource utilisation
• Sovereign infrastructure: Keep all metrics and data within European datacentres
• Scalable by design: Automatically scale GPU inference replicas based on real workload demand

Context

Managed Kubernetes Service

OVHcloud MKS is a fully managed Kubernetes platform designed to help you deploy, operate, and scale containerised applications in production. It provides a secure and reliable Kubernetes environment without the operational overhead of managing the control plane.

How does this benefit you?

• Cost-efficient: Pay only for worker nodes and consumed resources, with no additional charge for the Kubernetes control plane
• Fully managed Kubernetes: Certified upstream Kubernetes with automated control plane management, provided upgrades and high availability
• Production-ready by design: Built-in integrations with OVHcloud Load Balancers, networking, and persistent storage
• Scalable and flexible: Scale workloads easily, node pools to match application demand
• Open and portable: Based on standard Kubernetes APIs, enable seamless integration with open-source ecosystems and avoid vendor lock-in

In the following guide, all services are deployed within the OVHcloud Public Cloud.

Architecture overview

This reference architecture demonstrates a basic deployment of vLLM for vision-language model inference on OVHcloud Managed Kubernetes Service, featuring:

• High-availability deployment with 2 GPU nodes (NVIDIA L40S)
• Optimised GPU utilisation with proper driver configuration
• Scalable infrastructure supporting vision-language models
• Comprehensive monitoring using Prometheus, Grafana, and DCGM
• Full observability for both application and hardware metrics

Data flow:

1. Inference request:
   • User → LoadBalancer → Gateway → NGINX Ingress → “Qwen3 VL” Service → vLLM Pod → GPU
   • Response follows reverse path with streaming support
2. Metrics collection:
   • vLLM Pods expose /metrics endpoint (port 8000)
   • DCGM Exporters expose GPU metrics (port 9400)
   • Prometheus scrapes both endpoints every 30 seconds
   • Grafana queries Prometheus for visualization
3. Load distribution
   • NGINX Ingress uses cookie-based session affinity
   • vLLM Service uses ClientIP session affinity
   • Anti-affinity ensures 1 pod per GPU node

Prerequisites

Before you begin, ensure you have:

• An OVHcloud Public Cloud account
• An OpenStack user with the Administrator role
• Hugging Face access – create a Hugging Face account and generate an access token
• kubectl already installed and helm installed (at least version 3.x)

🚀 Now you have all the ingredients, it’s time to deploy the recipe for Qwen/Qwen3-VL-8B-Instruct using vLLM and MKS!

Architecture guide: Native GPU deployment of vLLM on MKS with full stack observability

This reference architecture describes a Large Language Model deployment using vLLM inference server and Kubernetes, to enjoy the benefits of a service that’s both highly available and monitorable in real time.

Step 1 – Create MKS cluster and Node pools

From OVHcloud Control Panel, create a Kubernetes cluster using the MKS.

Navigate to: Public Cloud → Managed Kubernetes Service → Create a cluster

1. Configure cluster

Consider using the following configuration for the current use case:

• Name: vllm-deployment-l40s-qwen3-8b
• Location: 1-AZ Region – Gravelines (GRA11)
• Plan: Free (or Standard)
• Network: attach a Private network (e.g. 0000 - AI Private Network)
• Version: Latest stable (e.g. 1.34)

2. Create GPU Node pool

During the cluster creation, configure the vLLM Node pool for GPUs:

• Node pool name: vllm
• Flavor: L40S-90
• Number of nodes: 2
• Autoscaling: Disabled (OFF)

Why L40S-90?

• Cost-effective for single-model deployment (1 GPU per node)
• Sufficient RAM (90GB) for Qwen3-VL-8B model

You should see your cluster (e.g. vllm-deployment-l40s-qwen3-8b) in the list, along with the following information:

You can now set up the node pool dedicated to monitoring.

3. Create CPU Node pool

From your cluster, click on Add a node pool and configure it as follow:

• Node pool name: monitoring
• Flavor: B2-15
• Number of nodes: 1
• Autoscaling: Disabled (OFF)

✅ Note

Monitoring stack can run on GPU nodes if cost is a concern. Dedicated CPU node provides better isolation and resource management.

If the status is green with the OK label, you can proceed to the next step.

4. Configure Kubernetes access

Once your nodes have been provisioned, you can download the Kubeconfig file and configure kubectl with your MKS cluster.

# configure kubectl with your MKS cluster
export KUBECONFIG=/path/to/your/kubeconfig-xxxxxx.yml

# verify cluster connectivity
kubectl cluster-info
kubectl get nodes

Returning:

NAME                     STATUS   ROLES    AGE   VERSION
monitoring-node-xxxxxx   Ready    <none>   1d   v1.34.2
vllm-node-yyyyyy         Ready    <none>   1d   v1.34.2
vllm-node-zzzzzz         Ready    <none>   1d   v1.34.2

Before going further, add a label to the CPU node for monitoring workloads.

CPU_NODE=$(kubectl get nodes -o json | \
  jq -r '.items[] | select(.status.allocatable."nvidia.com/gpu" == null) | .metadata.name')
kubectl label node $CPU_NODE node-role=monitoring

Finally, check with the following command:

NAME                     GPU      ROLE
monitoring-node-xxxxxx   <none>   monitoring
vllm-node-yyyyyy         1        <none>
vllm-node-zzzzzz         1        <none>

Once both nodes are in Ready status, you can proceed to the next step.

Step 2 – Install GPU operator

To start, consider setting up the GPU operator.

✅ Note

This step is based on this OVHcloud documentation: Deploying a GPU application on OVHcloud Managed Kubernetes Service

1. Add NVIDIA helm repository and create namespace

Add NVIDIA helm repo:

helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
helm repo update

And create Namespace as follow.

kubectl create namespace gpu-operator

2. Install GPU operator with correct configuration

The GPU Operator must be configured with specific driver versions to ensure compatibility with vLLM containers.

However, the default installation uses recent drivers (580.x with CUDA 13.x) which are incompatible with vLLM containers (CUDA 12.x).

Solution: Force driver version 535.183.01 (CUDA 12.2).

helm install gpu-operator nvidia/gpu-operator \
  -n gpu-operator \
  --set driver.enabled=true \
  --set driver.version="535.183.01" \
  --set toolkit.enabled=true \
  --set operator.defaultRuntime=containerd \
  --set devicePlugin.enabled=true \
  --set dcgmExporter.enabled=true \
  --set dcgmExporter.image="dcgm-exporter" \
  --set dcgmExporter.version="3.1.7-3.1.4-ubuntu20.04" \
  --set gfd.enabled=true \
  --set migManager.enabled=false \
  --set nodeStatusExporter.enabled=true \
  --set validator.driver.enable=false \
  --set validator.toolkit.enable=false \
  --set validator.plugin.enable=false \
  --timeout 20m

✅ Note

Specifying the DCGM version may only be necessary if you encounter problems with the default image (e.g. ‘ImagePullBackOff’). If this is the case, add the following parameters:
--set dcgmExporter.repository="nvcr.io/nvidia/k8s"
--set dcgmExporter.image="dcgm-exporter"
--set dcgmExporter.version="3.1.7-3.1.4-ubuntu20.04"

kubectl get pods -n gpu-operator

Note that all pods should reach Running state in 5-10 minutes.

You can also check the GPU availability:

kubectl get nodes -o json | jq -r '.items[] | select(.status.allocatable."nvidia.com/gpu" != null) | "\(.metadata.name): \(.status.allocatable."nvidia.com/gpu") GPU(s)"'

Returning:

vllm-node-yyyyyy: 1 GPU(s)
vllm-node-zzzzzz: 1 GPU(s)

And you can test to run nvidia-smi:

DRIVER_POD=$(kubectl get pods -n gpu-operator -l app=nvidia-driver-daemonset -o name | head -1)
kubectl exec -n gpu-operator $DRIVER_POD -- nvidia-smi

If GPU tests are working properly, you can move on DCGM service configuration.

3. Configure DCGM service

Why is DCGM Exporter required?

DCGM (Data Centre GPU Manager) is NVIDIA’s official tool for monitoring GPUs in production. The goal is to be able to collect and display metrics from both GPU nodes.

The metrics provided are:

• DCGM_FI_DEV_GPU_UTIL – GPU utilisation (%)
• DCGM_FI_DEV_GPU_TEMP – GPU temperature (°C)
• DCGM_FI_DEV_FB_USED – VRAM used (MB)
• DCGM_FI_DEV_FB_FREE – Free VRAM (MB)
• DCGM_FI_DEV_POWER_USAGE – Power consumption (W)
• And 50+ other GPU metrics

Next, ensure DCGM service has the correct labels and port configuration:

kubectl patch svc nvidia-dcgm-exporter -n gpu-operator --type merge -p '{
  "metadata": {
    "labels": {
      "app": "nvidia-dcgm-exporter"
    }
  },
  "spec": {
    "ports": [
      {
        "name": "metrics",
        "port": 9400,
        "targetPort": 9400,
        "protocol": "TCP"
      }
    ]
  }
}'

Verify the endpoints (should show 2 IPs, one per GPU node).

kubectl get endpoints nvidia-dcgm-exporter -n gpu-operator

NAME                   ENDPOINTS                       AGE
nvidia-dcgm-exporter   x.x.x.x:9400,x.x.x.x:9400   17d

Step 3 – Deploy Qwen3 VL 8B with vLLM inference server

The deployment of the Qwen 3 VL 8B model on two L40S GPU nodes is carried out in several stages.

1. Create namespace and Hugging Face secret

Start by creating Namespace:

kubectl create namespace vllm

Next, you must retrieve your Hugging Face token and replace the HF_TOKEN value by your own:

export HF_TOKEN="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

Create your secret as follow:

kubectl create secret generic huggingface-secret \
  --from-literal=token=$HF_TOKEN \
  --namespace=vllm

Verify you obtain the following output by launching:

kubectl get secret huggingface-secret -n vllm

NAME                 TYPE     DATA   AGE
huggingface-secret   Opaque   1      14d

2. Create vLLM deployment configuration

First, you can create vllm-deployment-2nodes.yaml file.

Deploy vLLM:

kubectl apply -f vllm-deployment-2nodes.yaml

You can monitor the deployment (it should take 8-10 minutes for model download and loading).

kubectl get pods -n vllm -o wide -w

Expected output after 10 minutes:

NAME               READY  STATUS   RESTARTS  AGE  IP       NODE  
qwen3-vl-xxxx-yyy  1/1    Running  0         1d   X.X.X.X  vllm-node-yyyyyy
qwen3-vl-xxxx-zzz  1/1    Running  0         1d   X.X.X.X  vllm-node-zzzzzz

You can also check the container logs:

kubectl logs -f -n vllm <pod-name>

You should find in the logs: “Uvicorn running on http://0.0.0.0:8000“

Is everything installed correctly? Then let’s move on to the next step.

3. Add service label

Ensure service has the correct label for ServiceMonitor discovery.

kubectl label svc qwen3-vl-service -n vllm app=qwen3-vl --overwrite

You can now verify by launching the following command.

kubectl get svc qwen3-vl-service -n vllm --show-labels | grep "app=qwen3-vl"

Returning:

qwen3-vl-service  ClusterIP  X.X.X.X  <none> 8000/TCP 1d  app=qwen3-vl

Step 4 – Install NGINX ingress controller

⚠️ Moving beyond Ingress

Follow this tutorial if you want to use Gateway instead of Ingress.

1. Add helm repository and configure Ingress

First of all, add helm repository:

helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx
helm repo update

Create configuration file with ingress-nginx-values.yaml.

Then, install NGINX Ingress:

helm install ingress-nginx ingress-nginx/ingress-nginx \
  --namespace ingress-nginx \
  --create-namespace \
  -f ingress-nginx-values.yaml \
  --wait

Wait for LoadBalancer IP. The external IP assignment should take 1-2 minutes.

kubectl get svc -n ingress-nginx ingress-nginx-controller -w

Once <EXTERNAL-IP> is no longer , Ctrl+C and export it:

export EXTERNAL_IP=<EXTERNAL-IP>
echo "API URL: http://$EXTERNAL_IP"

2. Create vLLM Ingress resource

Next, create vLLM Ingress using vllm-ingress.yaml.

Apply it as follow:

kubectl apply -f vllm-ingress.yaml

You can now test different API calls to verify that your deployment is functional.

3. Test API

Firstly, check if the model is available:

curl http://$EXTERNAL_IP/v1/models | jq

{
  "object": "list",
  "data": [
    {
      "id": "qwen3-vl-8b",
      "object": "model",
      "created": 1772472143,
      "owned_by": "vllm",
      "root": "Qwen/Qwen3-VL-8B-Instruct",
      "parent": null,
      "max_model_len": 8192,
      "permission": [
        {
          "id": "modelperm-8fb35cdd3208b068",
          "object": "model_permission",
          "created": 1772472143,
          "allow_create_engine": false,
          "allow_sampling": true,
          "allow_logprobs": true,
          "allow_search_indices": false,
          "allow_view": true,
          "allow_fine_tuning": false,
          "organization": "*",
          "group": null,
          "is_blocking": false
        }
      ]
    }
  ]
}

Next, test inference using the following request:

curl http://$EXTERNAL_IP/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3-vl-8b",
    "messages": [{"role": "user", "content": "Count from 1 to 10."}],
    "max_tokens": 100
  }' | jq '.choices[0].message.content'

"1, 2, 3, 4, 5, 6, 7, 8, 9, 10"

Great! You’re almost there…

Step 5 – Install Prometheus stack

Now, set up the monitoring stack that provides complete observability for application-level (vLLM) and hardware-level (GPU) metrics:

1. Add helm repository and create namespace

Add Prometheus helm repo:

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo update

Then, create the monitoring Namespace.

kubectl create namespace monitoring

2. Create Prometheus deployment configuration and installation

First, create prometheus.yaml file.

Install Prometheus stack:

helm install prometheus prometheus-community/kube-prometheus-stack \
  -n monitoring \
  -f prometheus.yaml \
  --timeout 10m \
  --wait

Now, monitor its installation and wait until the pods are ready:

kubectl get pods -n monitoring -w

If all pods are running successfully, you can proceed to the next step.

3. Check that the installation is operational

First access Grafana in background:

kubectl port-forward -n monitoring svc/prometheus-grafana 3000:80 &

Test Grafana health:

curl -s http://localhost:3000/api/health | jq

{
  "database": "ok",
  "version": "12.3.3",
  "commit": "2a14494b2d6ab60f860d8b27603d0ccb264336f6"
}

You can now access to Grafana locally via http://localhost:3000. You will have to use:

• Login: admin
• Password: Admin123!vLLM

Well done! You can now proceed to the configuration step.

Step 6 – Configure ServiceMonitors

The ServiceMonitors is used to tell Prometheus which endpoints to scrape for metrics.

1. Create vLLM ServiceMonitor

Retrieve the file from the GitHub repository: vllm-servicemonitor.yaml.

Next, apply and check that the ServiceMonitor vllm-metrics exists:

kubectl apply -f vllm-servicemonitor.yaml
kubectl get servicemonitor -n vllm

2. Create DCGM ServiceMonitor

First, create the dcgm-servicemonitor.yaml file.

Once again, apply and verify:

kubectl apply -f dcgm-servicemonitor.yaml
kubectl get servicemonitor -n gpu-operator

gpu-operator                  1d
nvidia-dcgm-exporter          1d
nvidia-node-status-exporter   1d

3. Configure Prometheus for Cross-Namespace discovery

Apply a patch to allow Prometheus to discover ServiceMonitors in all namespaces.

kubectl patch prometheus prometheus-kube-prometheus-prometheus -n monitoring --type merge -p '{
  "spec": {
    "serviceMonitorNamespaceSelector": {},
    "podMonitorNamespaceSelector": {}
  }
}'

Now you have to restart Prometheus.

1. Delete Prometheus pod to force configuration reload
2. Wait for Prometheus to restart

kubectl delete pod prometheus-prometheus-kube-prometheus-prometheus-0 -n monitoring

kubectl wait --for=condition=Ready \
  pod/prometheus-prometheus-kube-prometheus-prometheus-0 \
  -n monitoring \
  --timeout=180s

Wait about 2 minutes for discovery and finally, verify targets:

kubectl port-forward -n monitoring \
  prometheus-prometheus-kube-prometheus-prometheus-0 9090:9090 &

You can open in browser: http://localhost:9090/targets and search for:

• vllm
• dcgm

Note that the expected targets are:

• serviceMonitor/vllm/vllm-metrics/0 (2/2 UP)
• serviceMonitor/gpu-operator/nvidia-dcgm-exporter/0 (2/2 UP)

Step 7 – Create Grafana dashboards

In this final step, the goal is to create two Grafana dashboards to track both the software side with vLLM metrics and the hardware metrics that will monitor the GPU consumption and system.

1. vLLM application metrics

The dashboard provides insights into vLLM application performance, request handling, and resource utilization based on the following metrics:

This vLLM Grafana dashboard is composed of 23 panels:

The dashboard provides insights into LLM application performance, request handling, and resource utilisation based on the previous metrics.

Now create the dashboard using vllm-app-dashboard.json. Then, launch:

echo "Importing vLLM application dashboard..."
curl -X POST \
  'http://localhost:3000/api/dashboards/db' \
  -H 'Content-Type: application/json' \
  -u 'admin:Admin123!vLLM' \
  -d @vllm-app-dashboard.json | jq '.status, .url'

Next, you an access the vLLM dashboard and follow metrics in real time:

This dashboard is also essential to track hardware consumption for comprehensive monitoring.

2. GPU hardware metrics

Take advantage of the most useful DCGM metrics to check both the functioning and consumption of your hardware resources:

This hardware Grafana dashboard is composed of 13 panels with GPU hardware and system metrics. A detailed view is also available GPU util (%), temperature (°C), vRAM (GB) and power (Watt).

Please refer to hardware-dashboard.json by loading it as follows:

echo "Importing hardware dashboard..."
curl -X POST \
  'http://localhost:3000/api/dashboards/db' \
  -H 'Content-Type: application/json' \
  -u 'admin:Admin123!vLLM' \
  -d @hardware-dashboard.json | jq '.status, .url'

Finally, track resource consumption using this hardware dashboard:

Congratulations! Everything is working. You can now test your model and track the various metrics in real time.

Step 8 – LLM testing and performance tracking

Start by installing Python dependencies:

pip3 install openai tqdm

Replace the <EXTERNAL_IP> by the vLLM service external IP and launch the performance test thanks to the following Python code:

import time
import threading
import random
from statistics import mean
from openai import OpenAI
from tqdm import tqdm

APP_URL = "http://94.23.185.22/v1"
MODEL = "qwen3-vl-8b"

CONCURRENT_WORKERS = 500          # concurrency
REQUESTS_PER_WORKER = 10
MAX_TOKENS = 200                  # generation pressure

# some random prompts
SHORT_PROMPTS = [
    "Summarize the theory of relativity.",
    "Explain what a transformer model is.",
    "What is Kubernetes autoscaling?"
]

MEDIUM_PROMPTS = [
    "Explain how attention mechanisms work in transformer-based models, including self-attention and multi-head attention.",
    "Describe how vLLM manages KV cache and why it impacts inference performance."
]

LONG_PROMPTS = [
    "Write a very detailed technical explanation of how large language models perform inference, "
    "including tokenization, embedding lookup, transformer layers, attention computation, KV cache usage, "
    "GPU memory management, and how batching affects latency and throughput. Use examples.",
]

PROMPT_POOL = (
    SHORT_PROMPTS * 2 +
    MEDIUM_PROMPTS * 4 +
    LONG_PROMPTS * 6    # bias toward long prompts
)

# openai compliance
client = OpenAI(
    base_url=APP_URL,
    api_key="foo"
)

# basic metrics
latencies = []
errors = 0
lock = threading.Lock()

# worker
def worker(worker_id):
    global errors
    for _ in range(REQUESTS_PER_WORKER):
        prompt = random.choice(PROMPT_POOL)

        start = time.time()
        try:
            client.chat.completions.create(
                model=MODEL,
                messages=[{"role": "user", "content": prompt}],
                max_tokens=MAX_TOKENS,
                temperature=0.7,
            )
            elapsed = time.time() - start

            with lock:
                latencies.append(elapsed)

        except Exception as e:
            with lock:
                errors += 1

# run
threads = []
start_time = time.time()

print("\n-> STARTING PERFORMANCE TEST:")
print(f"Concurrency: {CONCURRENT_WORKERS}")
print(f"Total requests: {CONCURRENT_WORKERS * REQUESTS_PER_WORKER}")

for i in range(CONCURRENT_WORKERS):
    t = threading.Thread(target=worker, args=(i,))
    t.start()
    threads.append(t)

for t in threads:
    t.join()

total_time = time.time() - start_time

# results
print("\n-> BENCH RESULTS:")
print(f"Total requests sent: {len(latencies) + errors}")
print(f"Successful requests: {len(latencies)}")
print(f"Errors: {errors}")
print(f"Total wall time: {total_time:.2f}s")

if latencies:
    print(f"Avg latency: {mean(latencies):.2f}s")
    print(f"Min latency: {min(latencies):.2f}s")
    print(f"Max latency: {max(latencies):.2f}s")
    print(f"Throughput: {len(latencies)/total_time:.2f} req/s")

Returning:

-> STARTING PERFORMANCE TEST:
Concurrency: 500
Total requests: 5000

-> BENCH RESULTS:
Total requests sent: 5000
Successful requests: 5000
Errors: 0
Total wall time: 225.54s
Avg latency: 21.45s
Min latency: 6.06s
Max latency: 25.19s
Throughput: 22.17 req/s

Don’t forget to track GPU and vLLM metrics in your Grafana dashboards!

Conslusion

This reference architecture demonstrates a vLLM deployment on OVHcloud Managed Kubernetes Service (MKS) with comprehensive GPU monitoring. Benefits include:

• High Performance: GPU-accelerated inference with L40S
• Scalability: Kubernetes-native, horizontal scaling-ready
• Reliability: Health checks, auto-restart, monitoring
• API Compatibility: OpenAI-compatible endpoints
• Multimodality: Vision & text capabilities
• Full stack monitoring: Complete vLLM application and hardware dashboards

Going Further

Your current architecture is functional. However, if desired, it could be improved into a full production-ready solution.

Wish to take production hardening a step further?

Go further with the following enhancements:

1. Authentication & authorization
   • vLLM API authentication
   • Grafana authentication
   • Prometheus security
2. High availability & load balancing
   • Grafana high availability with multiple replicas and shared storage
   • Prometheus high availability
   • vLLM Horizontal Pod Autoscaling (HPA) based on custom metrics
3. Data persistence & backup
   • Prometheus long-term storage with persistent storage
   • Grafana Dashboard Backup
4. Observability enhancements
   • Distributed tracing by adding OpenTelemetry for request tracing
   • Alerting rules with production-ready alert rules

Blockchain is a decentralized database technology that tries to approach very important questions in a different way to traditional (centralized) databases. Ultimately, these questions boil down to:

• What is the truth?
• Who gets the last word on what the truth is?

In information management circles, we’ve tried to find a way to the ‘single source of truth’ for decades. Information sprawls. Being able to have a definitive answer on any one matter – whether that’s the last version of a memo or the real-time stock in a retail store – is incredibly valuable.

After all, if you’ve worked in an office for any length of time, you’ve probably renamed a file to something like ‘version 2.5 final final THIS ONE’ – and inevitably, it’s not ‘final’. This analogy doesn’t quite work because blockchain isn’t a file storage technology, but we’ll come back to that later.

Blockchain’s answer to defining truth is democratic. As its name suggests, information is stored in blocks, and these blocks are linked together in a chain. The order of this chain is documented, so once a change is made, it becomes an indelible part of ‘history.’ This establishes ‘truth’ by tying historical facts (or transactions) together so that one is linked to the next – and altering one event means altering the entire chain of events, rather like needing to rewrite an entire history book from the change of one small detail.

Let’s look at an example in practice.

When a change occurs in a blockchain network (like buying a cup of coffee with cryptocurrency) the information is sent to the network and preliminary checks are done – for example, checking that you have enough money to buy the coffee.

If there are any blockchain experts reading this, let’s assume it was a very expensive and significant coffee, because smaller transactions are often handled slightly differently in blockchain, in what we call Layer 2 Networks. We’ll come back to these later in the series.

Once the transaction has been verified, it’s then packaged up with other transactions to form a block and linked to the previous block in turn.

Next, the network has to make sure that the block is valid. This is done through a process called consensus, or validation, and is handled slightly differently depending on the network. Bitcoin uses a system called ‘Proof of Work,’ where different users work to verify complex equations that prove that the block’s contents haven’t been altered and that it does really link to the previous block. If anything has changed in the block since it was submitted, the equation doesn’t work and the block is rejected.

This is a computationally intensive process and requires a lot of electricity, particularly at scale. At one point, Bitcoin used as much electricity as all of Norway, so there are many other alternatives. For example, Solana uses ‘Proof of Stake,’ where the parties involved in verifying the block put forward assets (in this case, actual cryptocurrency). Then, the lead validator builds the block and adds a digital signature to it so as to say that they checked it, and that they have a real stake in the matter (i.e. money). This data is then re-checked by other validators, who vote on the validity. If there are enough ‘yes’ votes, then the block is agreed upon.

Of course, this does rely on trust, but there are usually big penalties for validators who ‘cheat.’ For example, they can have their stake removed, and/or they can be restricted from contributing to validation in future.

Solana also uses a system called ‘Proof of History’ to help it process a large number of transactions, which is a bit different to other systems.

But once approved, the block is then officially part of the chain, and the transaction can be completed – and you can get your Americano.

To define blockchain in another way: it creates a system of establishing trust and control without one single authority. Trust is distributed between all the different parties involved in the network. In fact, almost anyone can become a blockchain validator, although each network has its own requirements to do so.

As we’ve seen, blockchain is much bigger than just Bitcoin; the distributed database structure can be applied to all kinds of settings. We’ve seen organizations using blockchain to verify where tuna fish were caught, to ensure that they were ethically fished, and that the handling of said tuna could be traced from supermarket to distributor, and back to the person who caught them in the ocean.

Another major application for blockchain is in identity verification. Blockchain systems can help to check the validity of something without violating its privacy.

For example, imagine that you’ve discovered the famous Narnia wardrobe. You’ve decided to try to prevent people from sneaking in and causing chaos, so you’ve created a password-protected way to get in. You’re trying to qualify for state support to redevelop your house into a tourist attraction, so you need to prove that the wardrobe is the real deal. On the other hand, you don’t want to just give out the password because you’ll have government officials popping in and out all the time, conjuring infinite amounts of Turkish Delight and smuggling talking badgers out under their coats.

Blockchain’s answer to this is simple: you pop inside and take a selfie with Aslan and Mr. Tumnus, showing conclusively that you have access. This is how Self-sovereign identity works, which requires blockchain to link the wardrobe with the selfie. The two need to be linked – after all, you could have just popped into your friend’s magical wardrobe and taken a photo of some other lion and faun!

Many age-verification systems work in similar ways. For example, in many countries it’s illegal to give credit to someone under 18, so having a credit card is proof that you’re older than this, without having to show your passport or driving license. And in this case, it’s vital to show that the credit card is linked to the person using it, or the system doesn’t work.

Finally, it’s worth knowing that there are a number of different ecosystems within blockchain. Although they’re not strictly specialized, we can generalize that Bitcoin is focused on currency, Solana tends to being more of a platform for decentralized apps, and Ethereum is known for facilitating transactions quickly and making use of smart contracts – an ecosystem similar to Avalanche, but Avalanche has a modular approach focused on speed.

This picture is further muddied by the fact that there are public and private blockchains, which are also subdivided, but which essentially dictate who can see the blockchain and who has permission to be on it.

In our next blog post, we’ll go into a bit more detail about the blockchain world, focusing on the truths that IT leaders need to know. In the meantime, if you want a clearer, more practical view of how blockchain fits into real-world infrastructure, you can explore our resources and customer success stories on our website.

Introduction

Ethereum is quickly becoming known as one of the most efficient and flexible blockchain networks in the world, enabling financial organisations and individuals alike to create smart contracts that can power the likes of decentralised finance (DeFi) applications and NFT ecosystems. Ethereum is one of the foundations of Web3, providing a decentralised, scalable and secure network where builders can create the digital economy of the future.

But how do you get started? In this tutorial, we provide a guide to deploying and operating an Ethereum node on an OVHcloud public server. By the end of this guide, you will have a fully functional Ethereum node running on OVHcloud, following best practices for reliability and maintainability.

The security hardening and operational best practices required to fully protect an Ethereum node are beyond the scope of this tutorial. Users are strongly advised to implement additional security measures — such as firewall configuration, key management, and monitoring — according to their organizational requirements and industry best practices.

All set? Let’s get started.

Launching an instance

Navigate to the OVHcloud Control Panel, and under the Public Cloud section, select Instances. From there, click on Create an Instance to begin the provisioning process.

To run an Ethereum node reliably, the hardware specifications must meet the client software’s minimum requirements. According to the Ethereum Foundation documentation, an Ethereum node requires at least:

● Execution client: 2 CPU cores, 16 GB of RAM, and 1 TB of fast SSD storage.

● Consensus client: 2 CPU cores, 8 GB of RAM, and access to the same storage.

For production environments and long-term stability, higher specifications are strongly recommended. In this tutorial, we will provision an instance with 2 vCores and 30 GB of RAM, which satisfies the minimum combined requirements for running both the execution and consensus clients on a single machine.

Within the Memory Optimized category, locate the instance type named R2-30 and select it for deployment.

Next, select the geographic location for your instance. The choice of region may affect latency, network performance, and regulatory considerations. For the purposes of this tutorial, we will deploy the node in the Frankfurt data center, which provides low-latency connectivity within Europe and is well-suited for Ethereum node operations.

It is worth noting that maintaining nodes in different geographic regions contributes to the overall resilience and decentralization of the Ethereum network. Geographical diversity helps reduce the risk of localized outages and improves redundancy across the infrastructure.

Now, select the operating system image for your instance. For this tutorial, we will use Ubuntu 24.04 LTS.

Next, select the public SSH key that will be associated with your instance. This key will be installed on the server during provisioning and will serve as the primary authentication method for secure remote access. After the instance is launched, you will be able to connect to it via SSH using the corresponding private key. This approach eliminates the need for password-based logins and significantly enhances security by enforcing key-based authentication.

Assign a descriptive name to your instance (e.g., eth-node-fra-01) following your organization’s naming convention. If your workflow uses tags or labels, add ones that identify the role (execution/consensus), environment (prod/stage), and region (FRA). OVHcloud also allows attaching a post-installation script (cloud-init user data) to automate provisioning; however, this tutorial proceeds step-by-step, so leave that field empty.

For the purposes of this tutorial, we will simply assign the instance the name OVHnode.

Configure the network access by selecting Public mode, which will assign the instance a public IP address and enable external connectivity. This setting is required for the node to participate in the Ethereum network. Next, choose your preferred billing method.

Finally, click on Create an Instance to launch the deployment process. OVHcloud will provision the server with the selected configuration, and within a few minutes, the instance will be available and running. At this point, your Ethereum node host environment is ready for initial access and further configuration.

Adding Storage

The next step is to provision persistent storage for the Ethereum blockchain data. In the OVHcloud Control Panel, navigate to the Block Storage section from the side menu, and click on Create a Volume. This volume will serve as the primary data store for the Ethereum chain, ensuring durability and scalability independent of the compute instance.

When creating the storage volume, ensure that you select the same region as your compute instance. This is essential to guarantee low-latency access and to allow the volume to be attached directly to your node without cross-region performance penalties.

OVHcloud provides three categories of block storage volumes:

• Classic HDD – 500 IOPS guaranteed.
• High-Speed SSD – up to 3,000 IOPS.
• High-Speed Gen2 – up to 20,000 IOPS.

Running an Ethereum node requires high-performance storage due to the constant read and write operations needed to synchronize the blockchain and access block and state data. For this reason, we strongly recommend selecting a High-Speed Gen2 volume, which delivers the necessary throughput and input/output operations per second (IOPS) to ensure stable node performance.

Both the Ethereum Execution Layer (EL) client and the Consensus Layer (CL) client require significant disk capacity to store blockchain data, state, and historical records. A minimum of 2 TB of storage is recommended to operate a full mainnet node reliably. When creating the block storage volume in OVHcloud, set the capacity to 2 TB and select the High-Speed Gen2 option to ensure sufficient performance for synchronization and long-term operation.

Assign a descriptive name to the storage volume to simplify management and identification. For example, you may name it chaindata to clearly indicate its role as the primary data store for the node.

Once all parameters have been configured — including the region, storage type, capacity, and name — click on Create Volume. OVHcloud will provision the block storage resource, which will then be available to attach to your Ethereum node instance.

Mounting the Volume

After the new volume appears as available in the OVHcloud Control Panel, attach it to your Ethereum node instance. To do this, open the ⋮ (more options) menu next to the volume entry and select Attach to an Instance. From the list of instances, choose the node you previously created. The volume will then be linked to that instance and accessible as an additional block device.

Now, establish a secure connection to your server via SSH. Use the following command from your local terminal, replacing IPaddress with the public IP assigned to your instance:

ssh ubuntu@IPaddress

By default, OVHcloud instances provisioned with the Ubuntu image create a user named ubuntu, which you should use for the initial connection. Authentication will be handled through the SSH key you configured during instance creation.

Once connected to the instance, run the following command to list all available block devices:

lsblk

This will display a tree view of the system’s storage devices, including the root disk and the newly attached block storage volume. Identify the additional device (e.g., /dev/sdb) that corresponds to the 2 TB volume you created in OVHcloud. This device will later be formatted and mounted to store the Ethereum blockchain data.

Next, create a partition on the newly attached volume. Replace /dev/sdb with the device name identified in the previous step if it differs. Execute:

sudo fdisk /dev/sdb

This command will open the partitioning utility for the selected block device. From there, you can create a new primary partition that spans the entire disk. Once complete, the partition will typically be available as /dev/sdb1.

Once the partition has been created (e.g., /dev/sdb1), format it with the ext4 filesystem so it can be mounted and used by the operating system. Run the following command:

sudo mkfs.ext4 /dev/sdb1

This will initialize the partition with an ext4 filesystem, which is a stable and widely supported choice for storing Ethereum chain data.

Now, create a dedicated directory to serve as the mount point for the formatted volume, then mount it and verify that it has been successfully attached to the filesystem. Execute the following commands:

sudo mkdir -p /mnt/chaindata

sudo mount /dev/sdb1 /mnt/chaindata

df -h

• mkdir -p /mnt/chaindata creates the mount directory (using -p ensures no error if intermediate directories are missing).
• mount /dev/sdb1 /mnt/chaindata mounts the formatted partition to the directory.
• df -h displays all mounted filesystems in a human-readable format, allowing you to confirm that /dev/sdb1 is correctly mounted at /mnt/chaindata with the expected capacity (approximately 2 TB).

At this point, your disk is mounted and ready for use. However, the configuration is not persistent: if the server restarts, the volume will need to be mounted manually. To ensure the volume is mounted automatically at boot, we must configure it in the /etc/fstab file.

First, retrieve the UUID (Universally Unique Identifier) of your volume by running:

sudo blkid

This command lists all block devices and their associated attributes. Identify the entry corresponding to your new partition (e.g., /dev/sdb1) and copy the value of its UUID field, which will be used in the fstab configuration.

Next, edit the /etc/fstab file to configure the automatic mount. Open the file with your preferred text editor, for example:

sudo nano /etc/fstab

Then add the following line at the end of the file, replacing the UUID with the one obtained from the previous blkid command:

UUID=e146e498-bba2-4574-9b47-b54fffea77e7 /mnt/chaindata ext4 nofail 0 0

This entry ensures that the partition will be mounted at /mnt/chaindata automatically on every system boot. The nofail option allows the system to continue booting even if the device is unavailable.

Your storage volume is now fully configured and ready to be used for storing Ethereum blockchain data. With the automatic mount entry in place, the system will make the volume available at /mnt/chaindata on every reboot, ensuring a stable and persistent data directory for your node.

Create a dedicated user

Next, create a dedicated user account to manage all Ethereum node operations. This practice improves security by separating node processes from the default system user. Run the following commands:

sudo useradd -s /bin/bash -d /home/node_admin/ -m -G sudo node_admin

echo 'node_admin:MySuperPassword123' | sudo chpasswd

sudo mkdir -p /home/node_admin/.ssh

• useradd creates a new user named node_admin, with a home directory, bash shell, and membership in the sudo group.
• chpasswd sets an initial password for the new user (replace with a strong secret or configure key-based login).
• mkdir -p creates the .ssh directory for SSH configuration in the user’s home directory.

Now, configure SSH key-based authentication for the new user by adding your public SSH key to the authorized_keys file. Replace the placeholder with your actual public key:

sudo sh -c "echo 'My-public-SSH-key' > /home/node_admin/.ssh/authorized_keys"

This command creates the authorized_keys file under /home/node_admin/.ssh/ and writes your public key into it, allowing secure, passwordless login as the node_admin user.

Ethereum Preliminaries

Operating a fully functional Ethereum node requires the coordinated operation of two key software components:

1. Execution Client (EL) – responsible for processing transactions and maintaining the Ethereum state.

2. Consensus Client (CL) – responsible for reaching consensus with the rest of the network through the Ethereum proof-of-stake protocol.

These two components must run in tandem and communicate securely to maintain synchronization with the Ethereum mainnet.

The Ethereum ecosystem supports multiple client implementations, each developed independently but compliant with the Ethereum specification. The most widely used options include:

Execution Clients (EL):

• Geth
• Nethermind
• Reth
• Besu
• Erigon

Consensus Clients (CL):

• Lighthouse
• Prysm
• Teku
• Nimbus
• Lodestar

For this guide, we will use the following combination:

● Execution Client: Nethermind

● Consensus Client: Lighthouse

Installing Nethermind (Execution Client)

Step 1: Add the Nethermind APT Repository

To install Nethermind via APT, begin by adding the official repository:

sudo add-apt-repository ppa:nethermindeth/nethermind

If the above command is not found, install the required tools first:

sudo apt-get install software-properties-common

Step 2: Update the Package Index

Refresh your package list to include packages from the newly added repository:

sudo apt-get update

⚠️This may take a few minutes depending on your system and network speed.

Step 3: Install Nethermind

Now install the Nethermind binary:

sudo apt-get install nethermind -y

Installing Lighthouse (Consensus Client)

Step 1: Download the Latest Stable Release

Navigate to the Lighthouse GitHub Releases page and locate the latest stable release.

As of September 29, 2025, version 8.0.0 is marked as a Pre-release, so we will use the stable version 7.1.0.

Use the following curl command to download the Lighthouse client binary archive:

curl -LO https://github.com/sigp/lighthouse/releases/download/v7.1.0/lighthouse-v7.1.0-x86_64-unknown-linux-gnu.tar.gz

Step 2: Extract the Archive

Once downloaded, extract the archive:

tar -xvf lighthouse-v7.1.0-x86_64-unknown-linux-gnu.tar.gz

This will place the lighthouse binary in your current directory.

Step 3: Verify and Move the Binary

Test the binary to ensure it is working:

./lighthouse –version

Move it to a directory included in your system’s $PATH for global access:

sudo cp lighthouse /usr/bin

Create the JWT Secret File

A shared JWT secret is required for secure authenticated communication between the execution and consensus clients.

Step 1: Create a Secrets Directory

sudo mkdir -p /secrets

Step 2: Generate the JWT Token

openssl rand -hex 32 | tr -d "\n" | sudo tee /secrets/jwt.hex > /dev/null

You should now have the file /secrets/jwt.hex containing a 32-byte hexadecimal secret.

Using screen for Session Persistence

When running on a remote server (such as an OVH cloud instance), any disconnection from your SSH session will terminate running processes unless they are managed through tools like screen.

Check if screen is installed:

screen –version

If not installed:

sudo apt-get install screen

Setting Ownership for /mnt/chaindata

When running Ethereum clients (Nethermind and Lighthouse), it’s essential that the user executing the commands has the necessary access to the data directory. To avoid permission issues and maintain security, you should assign ownership of the previously mounted directory to your current user.

sudo chown $USER:$USER /mnt/chaindata

This command sets the user and group ownership of /mnt/chaindata to the currently logged-in user. It ensures that you (and your Ethereum clients, if run under your user account) have full access to read, write, and modify files in this directory.

Now we are ready to launch both the execution and consensus clients in separate screen sessions for persistent background operation.

Launching Nethermind (Execution Client)

Step 1: Create a Screen Session

screen -S nethermind

Step 2: Run Nethermind

nethermind -c mainnet \ 
--data-dir /mnt/chaindata/nethermind \ 
--JsonRpc.Enabled true \ 
--HealthChecks.Enabled true \ 
--HealthChecks.UIEnabled true \ 
--JsonRpc.EngineHost=127.0.0.1 \ 
--JsonRpc.EnginePort=8551 \ 
--JsonRpc.JwtSecretFile=/secrets/jwt.hex 

You should see logs indicating the client is running. Eventually, the message:

Waiting for Forkchoice message from Consensus Layer

will appear, indicating the execution client is waiting to pair with the consensus client.

Step 3: Detach the Session

Press: Ctrl-a then d to detach from the session and return to the main shell.

Step 4: List Active Sessions (Optional)

screen -ls

To reattach later:

screen -r nethermind

Launching Lighthouse (Consensus Client)

Step 1: Create a New Screen Session

screen -S lighthouse

Step 2: Run Lighthouse Beacon Node

lighthouse bn \ 
--network mainnet \ 
--execution-endpoint http://127.0.0.1:8551 \ 
--execution-jwt /secrets/jwt.hex \ 
--checkpoint-sync-url https://mainnet.checkpoint.sigp.io \ 
--http \ 
--datadir /mnt/chaindata/lighthouse

After some initial setup, Lighthouse should begin syncing with the network.

Step 3: Detach the Session

Use the same key combination: Ctrl-a then d.

Verifying Synchronization Between EL and CL

At this stage, both the execution client (Nethermind) and the consensus client (Lighthouse) should be running in separate screen sessions. To confirm that they are properly connected and synchronization is underway, reattach to the Nethermind session and inspect the logs.

Step 1: Reattach to the Nethermind Session

screen -r nethermind

This command brings you back into the Nethermind screen session, where you can observe real-time logs.

Step 2: Check for Communication Messages

If Lighthouse is running and connected correctly, Nethermind’s logs should no longer display:

Waiting for Forkchoice message from Consensus Layer

Instead, you should see logs indicating that the Engine API communication has been established and blocks are being processed. Look for messages like:

Received ForkChoice: ...
Syncing...

These logs confirm that Nethermind is receiving block proposals and fork choice updates from Lighthouse and that the node is syncing correctly with the Ethereum mainnet.

Conclusion

And you’re done! You have deployed a fully functional Ethereum node on OVHcloud.

While this guide focused on the technical deployment, it is important to complement the setup with proper security hardening, monitoring, and maintenance practices to ensure long-term stability. With the foundation now in place, you can extend the node’s functionality, integrate it into larger infrastructures, or use it as a base for research, development, and staking operations.

This is the first guide in our mini-series and will shortly be followed by a tutorial on how to deploy an Ethereum node on a bare metal server and monitor it in real-time. Stay tuned for more.

Since autumn 2025, the global memory market has been going through a major disruption. Although barely noticeable to end users, these developments are radically changing the cost of computer hardware and, as a direct result, the cost of the cloud.

This article will decipher this structural crisis, its real-life impacts, and the strategic choices that OVHcloud is implementing to mitigate its effects.

An industrial shift towards GPUs

Globally, the three major memory manufacturers have redirected a significant portion of their production capacity to meet the massive demand for GPUs, particularly for AI-related and high-bandwidth computing applications.

This reallocation took place without a corresponding reduction in the historical demand for RAM and storage, generating pressure on several market segments simultaneously.

The consequences of this were immediate and noticeable:

• pressure on supply, with reduced stock and extended lead times
• continuous rise in RAM and disk prices since September 2025
• long-term market instability, which is not expected to find a new balance until late 2026

A sustained inflation of memory components

Even after the market stabilises, prices are not expected to return to their historical levels before 2028, the amount of time needed for new production capacities to become truly operational.

This development profoundly disrupts the economic fundamentals of computer hardware, both for on-premises infrastructures and for the cloud. Depending on configurations, the prices related to RAM and storage could increase by 15% to 300% compared to 2025 prices, depending on the volumes of memory and disk capacity deployed.

This change of scale is both abrupt and unprecedented, with no recent equivalent in the global market.

A market under pressure, even with higher prices

Paradoxically, the rise in prices is not enough to secure the availability of components. Currently, to guarantee the delivery of their desired volume of RAM or disks, cloud providers need to order up to 12 months in advance, without being told the final price at the time of purchase.

In practice, prices are only communicated one to two months after delivery, depending on the changes in supply and demand during the quarter in question. This uncertainty places unprecedented pressure on industry players and cloud providers, simultaneously affecting production and distribution.

Towards a new global balance of demand

This situation will inevitably have repercussions on the volumes ordered. Some customers will find the prices too high and limit their investments, while others, lacking alternatives, will continue to place orders regardless.

This interplay of opposing forces should lead to a new global balance, but at a significantly higher price point. Current projections anticipate a 250% to 300% increase in the price of RAM by the end of 2026, compared to September 2025.

Our strategy to soften the blow

In light of this reality, OVHcloud has chosen not to automatically pass on the entire price increase of components to its customers.

For the cloud deployed between 2026 and 2028 (including Public Cloud, Private Cloud, and Bare Metal), the average price increase will be limited – between 9% and 11% – despite significantly higher RAM and disk costs.

To offset this gap, a moderate increase of 2% to 6% is planned for solutions deployed before 2025, depending on the age of the equipment, as well as a change in IPv4 pricing. The latter should not have a significant impact on our customers’ budgets, as the cost of IP addresses is a small share compared to other resources in a cloud project.

Our objective is clear: to maintain pricing consistency across the entire range from 2021 to 2028, and to prepare for a gradual return to normal in 2029.

Continuous investments and developing solutions

Beyond pricing adjustments, this period will be characterised by sustained investments in our solutions and in the customer experience.

Despite the strong pressure from rising component costs, we are continuing to develop our services to provide more value to our customers.

In practical terms, this will result in:

• a gradual strengthening of support mechanisms
• an increase in resources included in certain ranges
• a modernization of our computing and storage infrastructures

These initiatives demonstrate our commitment to not reduce this period to merely a consequence of cost increases, but to maintain a dynamic of improving our services, even in a constrained economic context.

Time frame and implementation procedures

Our clients have already received emails detailing the precise impacts on their services. The new prices will come into effect on 1 April 2026.

Until that date, it is possible to renew services at the current rates for a duration of up to 2 years. In all cases, the new prices will only apply at the end of the current contractual period.

A time of uncertainty and a strategic advantage

We are going through an exceptionally unpredictable period, where market visibility rarely lasts longer than one to two weeks. There remains hope that prices will stabilize on a long-term basis from 2026, so that we can avoid further unfavorable announcements.
In this tense context, having a global supply chain and two internal production facilities is a major strategic advantage. This allows us to continue receiving components and producing servers, while the memory shortage affects a large part of the market.

Our Prices

You will find below our new pricing:
– Public Cloud: Prices below are displayed on an hourly basis and with Linux OS. Please, find on our Prices web page our monthly-consumed virtual machine instances (b2, c2, r2) and Savings Plan options (b3, c3, r3) as well as prices with Windows licences.
– All our VPS, Floating IPs, and Additional IP pricing.
– Bare Metal: The displayed prices correspond to a 1-month commitment; additional discounts apply for 12- or 24-month prepayments. The prices for options are for new orders only. The renewal of options, which has been communicated by email to our customers, will be limited to +10% for disk options and +15% for RAM options.

For existing subscriptions renewed before April 1st, you can secure your current pricing for the full duration of the commitment you choose, effective from your renewal date.

Please note that the following product categories are not affected by our pricing evolution:
– Public Cloud – Compute : Cloud GPUs and Metal Instances
– Public Cloud – Container : Managed Kubernetes, Managed Registries & Managed Rancher
– Public Cloud – Network : Load Balancer, Gateway. Public and Private network traffic remains included.
– Public Cloud – Storage : Object Storage, Block Storage.
– Public Cloud – Analytics : Data Platform
– Public Cloud – AI & Machine Learning : AI Solutions (AI Notebook, AI Training, AI Deploy) and AI Endpoints
– Public Cloud – Quantum : Emulators & QPUs
– Bare Metal : Kimsufi et SoYouStart ranges
– Bare Metal : All storage (Veeam Enterprise plus, HYCU, Back-up Agent, NAS-HA, Cloud Disk Array)
– Private Cloud : All VMware offers, all storage offers (Veeam Enterprise plus, HYCU, Back-up Agent)

Un basculement industriel vers les GPU

À l’échelle mondiale, les trois grands fabricants de mémoire ont réorienté une part importante de leurs capacités de production pour répondre à la demande massive en GPU, en particulier pour les usages liés à l’IA et au calcul haute performance.

Cette réallocation s’est effectuée sans réduction équivalente de la demande historique en mémoire vive et en stockage, générant une pression simultanée sur plusieurs segments du marché.

Les conséquences sont immédiates et visibles :

• tension sur l’offre, avec des stocks réduits et des délais d’approvisionnement allongés ;
• hausse continue des prix de la RAM et des disques depuis septembre 2025 ;
• instabilité durable du marché, qui ne devrait retrouver un nouvel équilibre qu’à l’horizon fin 2026.

Une inflation durable des composants mémoire

Même après la stabilisation du marché, les prix ne devraient pas retrouver leurs niveaux historiques avant 2028, le temps nécessaire pour que de nouvelles capacités de production soient réellement opérationnelles.

Cette évolution bouleverse profondément les fondamentaux économiques du matériel informatique, tant pour les infrastructures on-premise que pour le cloud. Selon les configurations, l’impact tarifaire lié à la RAM et au stockage pourrait atteindre +15 % à +300 % par rapport aux prix de 2025, en fonction des volumes de mémoire et de capacité disque déployés.

Ce changement d’échelle est à la fois brutal et inédit, sans équivalent récent sur le marché mondial.

Un marché sous tension même à prix élevé

Paradoxalement, la hausse des prix ne suffit pas à sécuriser la disponibilité des composants. Aujourd’hui, pour garantir la livraison de volumes de RAM ou de disques, il est nécessaire pour les fournisseurs de cloud de passer commande jusqu’à 12 mois à l’avance, sans connaître le prix final au moment de l’achat.

En pratique, les tarifs ne sont communiqués qu’un à deux mois après la livraison, selon l’évolution de l’offre et de la demande sur le trimestre concerné. Cette incertitude exerce une pression inédite sur les acteurs industriels et les fournisseurs de cloud, affectant simultanément la production et la distribution.

Vers un nouvel équilibre mondial de la demande

Cette situation aura inévitablement des répercussions sur les volumes commandés. Certains clients jugeront les prix trop élevés et limiteront leurs investissements, tandis que d’autres, faute d’alternative, continueront à passer commande malgré tout.

Ce jeu de forces opposées devrait conduire à un nouvel équilibre mondial, mais à un niveau de prix nettement supérieur. Les projections actuelles anticipent une augmentation de la RAM de +250 % à +300 % à la fin 2026, par rapport à septembre 2025.

Notre stratégie pour amortir le choc

Face à cette réalité, OVHcloud a choisi de ne pas répercuter automatiquement l’intégralité de la hausse des composants sur ses clients.

Pour le cloud déployé entre 2026 et 2028 — incluant le Public Cloud, le Private Cloud et le Bare Metal — l’augmentation moyenne des prix sera limitée, entre +9 % et +11 %, malgré des coûts de RAM et de disques nettement plus élevés.

Pour compenser cet écart, un ajustement modéré est prévu sur les offres déployées avant 2025, de +2 % à +6 %, en fonction de l’ancienneté du matériel, ainsi qu’une évolution des tarifs des IPv4. Cette dernière ne devrait pas avoir d’impact significatif sur le budget de nos clients, le coût des adresses IP représentant une part limitée par rapport aux autres ressources d’un projet cloud.

Notre objectif est clair : préserver une cohérence tarifaire sur l’ensemble des gammes 2021-2028 et préparer un retour progressif à la normale en 2029.

Investissements continus et évolution des offres

Au-delà des ajustements tarifaires, cette période se caractérise par des investissements soutenus dans nos offres et dans l’expérience client.

Malgré la forte pression liée à l’augmentation des coûts des composants, nous continuons à faire évoluer nos services afin d’apporter davantage de valeur à nos clients.

Concrètement, cela se traduit par :

• un renforcement progressif des dispositifs de support ;
• une augmentation des ressources incluses dans certaines gammes ;
• une modernisation de nos infrastructures de calcul et de stockage.

Ces initiatives témoignent de notre volonté de ne pas réduire cette phase à une simple répercussion des hausses de coûts, mais de maintenir une dynamique d’amélioration de nos services, même dans un contexte économique contraint.

Calendrier et modalités d’application

Nos clients ont déjà reçu des emails détaillant les impacts précis sur leurs services. Les nouveaux tarifs seront appliqués à compter du 1^er avril 2026.

Jusqu’à cette date, il est possible de renouveler les services aux tarifs actuels pour une durée pouvant aller jusqu’à 2 ans. Dans tous les cas, les nouveaux prix ne s’appliqueront qu’à l’issue de la période d’engagement en cours.

Une période d’incertitude et un avantage stratégique

Nous traversons une phase exceptionnellement imprévisible, où la visibilité sur les marchés dépasse rarement une à deux semaines. L’espoir demeure que les prix se stabilisent durablement dès 2026, afin d’éviter de nouvelles annonces défavorables.

Dans ce contexte tendu, disposer d’une chaîne d’approvisionnement mondiale et de deux usines de production internes constitue un avantage stratégique majeur. Cela nous permet de continuer à recevoir des composants et à produire des serveurs, là où la pénurie de mémoire touche une grande partie du marché.

Nos tarifs

Vous trouverez ci-dessous nos nouveaux tarifs :
– Public Cloud : les prix ci-dessous sont affichés à l’heure et avec OS Linux. Vous trouverez sur notre page Tarifs les prix des instances de machines virtuelle consommées au mois (b2, c2, r2) ou en Savings Plan (b3, c3, r3) ainsi que les tarifs avec licences Windows.
– Tous nos tarifs VPS, Floating IPs & IP additionnelles.
– Bare Metal : les prix affichés correspondent à un engagement d’un mois ; des remises supplémentaires sont appliquées en cas de prépaiement sur 12 ou 24 mois. Les prix des options sont ceux des nouvelles commandes uniquement. Le renouvellement d’options qui a été communiqué par email à nos clients sera quant à lui limité à +10% sur les options de disques, +15% sur les options de RAM.

Pour toutes les souscriptions existantes renouvelées avant le 1er avril, vous pouvez conserver votre tarif actuel pendant toute la durée d’engagement choisie, à compter de votre date de renouvellement.

Veuillez noter que les catégories de produits suivantes ne sont pas concernées par l’évolution de nos tarifs :
– Public Cloud – Compute : Cloud GPUs et Metal Instances
– Public Cloud – Container : Managed Kubernetes, Managed Registries & Managed Rancher
– Public Cloud – Network : Load Balancer, Gateway. Le trafic réseau Public et Privé reste inclus.
– Public Cloud – Storage : Object Storage, Block Storage.
– Public Cloud – Analytics : Data Platform
– Public Cloud – AI & Machine Learning : AI Solutions (AI Notebook, AI Training, AI Deploy) et AI Endpoints
– Public Cloud – Quantum : Emulators & QPUs
– Bare Metal – stockage : Veeam Enterprise Plus, HYCU, Back-up Agent, NAS-HA, Cloud Disk Array
– Bare Metal : Gammes Kimsufi et SoYouStart
– Private Cloud : offres VMware et offres de stockage (Veeam Enterprise plus, HYCU, Back-up Agent)