Azure

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Components and Sizing Recommendations

Component	Options	Sizing Recommendations
AI Gateway	Deploy in your AKS cluster using Helm charts.	Use AKS B2ms worker nodes, each providing at least 2 vCPUs and 4 GiB of memory. For high availability, deploy them across multiple Availability Zones.
Logs Store (optional)	Azure Blob Storage or S3-compatible storage	Each log document is ~10kb in size (uncompressed)
Cache (Prompts, Configs & Providers)	Built-in Redis or Azure Cache for Redis	Deployed within the same VNet as the Portkey Gateway.

Prerequisites

Ensure the following tools and resources are installed and available:

A running AKS cluster with at least 2 worker nodes. (Best Practice: Use 2 nodes, with one node in each Availability Zone, to ensure high availability.)
Azure CLI
Kubectl
Helm (v3 or above)

Create a Portkey Account

Go to the Portkey website.
Sign up for a Portkey account.
Once logged in, locate and save your Organisation ID for future reference. It can be found in the browser URL: https://app.portkey.ai/organisation/<organisation_id>/
Contact the Portkey AI team and provide your Organisation ID and the email address used during signup..
The Portkey team will share the following information with you:
- Docker credentials for the Gateway images (username and password).
- License: Client Auth Key.

Setup Project Environment

cluster_name=<AKS_CLUSTER_NAME>               # Specify the name of the AKS cluster where the gateway will be deployed.
namespace=<NAMESPACE>                         # Specify the namespace where the gateway should be deployed (for example, portkeyai).
service_account_name=<SERVICE_ACCOUNT_NAME>   # Provide a name for the Service Account to be associated with Gateway Pod (for example, gateway-sa)

mkdir portkey-gateway
cd portkey-gateway
touch values.yaml

Image Credentials Configuration

# Update the values.yaml file
imageCredentials:
  - name: portkey-enterprise-registry-credentials
    create: true
    registry: https://index.docker.io/v1/
    username: <PROVIDED BY PORTKEY>
    password: <PROVIDED BY PORTKEY>

  gatewayImage:
    repository: "docker.io/portkeyai/gateway_enterprise"
    pullPolicy: Always
    tag: "latest"
  dataserviceImage:
    repository: "docker.io/portkeyai/data-service"
    pullPolicy: Always
    tag: "latest"
  redisImage:
    repository: "docker.io/redis"
    pullPolicy: IfNotPresent
    tag: "7.2-alpine"
environment:
  create: true
  secret: true
  data:
    ANALYTICS_STORE: control_plane
    SERVICE_NAME: <SERVICE_NAME>                      # Specify a name for the service
    PORTKEY_CLIENT_AUTH: <PROVIDED BY PORTKEY>
    ORGANISATIONS_TO_SYNC: <ORGANISATION_ID>           # This is obtained after signing up for a Portkey account.
    

Configure Components

Based on the choice of components and their configuration update the values.yaml.

Cache Store

The Portkey Gateway deployment includes a Redis instance pre-installed by default. You can either use this built-in Redis or connect to an external cache like Azure Cache for Redis or Azure Managed Redis.

Built-in Redis

No additional permissions or network configurations are required.

## To use the built-in Redis, add the following configuration to the values.yaml file.
environment:
  data:
    CACHE_STORE: redis
    REDIS_URL: "redis://redis:6379"
    REDIS_TLS_ENABLED: "false"

Log Store

Azure Blob Storage

(Optional) If not already done, create an Azure Storage Account and a Blob Container to store LLM logs.

Set up access to the log store. The Gateway supports the following methods for connecting to the Blob Storage:

Managed Identity
Entra ID

Depending on the chosen Blob Storage access method, update values.yaml with the following configuration.

Managed Identity
Entra ID

## To enable Managed Identity update values.yaml with following details:-

environment:
  data:
    LOG_STORE: azure
    AZURE_STORAGE_ACCOUNT: <STORAGE_ACCOUNT_NAME>       # Specify the name of the storage account which will be used for storing LLM logs.
    AZURE_STORAGE_CONTAINER: <STORAGE_CONTAINER>        # Specify the name of the blob store container which will be used for storing LLM logs.
    AZURE_AUTH_MODE: managed

Network Configuration

Set up external access to the Gateway To ensure the Gateway service is accessible externally, create either an internal or internet-facing Load Balancer.

service:
  type: LoadBalancer
  port: 8787
  annotations: 
    # Specify the type of Load Balancer to create - internal or internet-facing 
    service.beta.kubernetes.io/azure-load-balancer-internal: "false"                      
    service.beta.kubernetes.io/azure-load-balancer-health-probe-protocol: "http"
    service.beta.kubernetes.io/azure-load-balancer-health-probe-request-path: "/v1/health"

    # Replace the cidr ranges to make it more restrictive
    service.beta.kubernetes.io/azure-allowed-ip-ranges: 0.0.0.0/0                                                       

Note: If you choose an internal load balancer, it must be exposed via a public-facing service (e.g., Application Gateway, Azure Front Door) to allow the Control Plane to communicate with the Data Plane. Additionally, Azure Load Balancer supports various annotations to fine-tune its configuration. For a complete list of supported annotations, see the Azure Load Balancer annotations.

Ensure Outbound Network Access

By default, Kubernetes allows full outbound access, but if your cluster has NetworkPolicies that restrict egress, configure them to allow outbound traffic. Example NetworkPolicy for Outbound Access:

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: allow-all-egress
  namespace: portkeyai
spec:
  podSelector: {}
  policyTypes:
  - Egress
  egress:
  - to:
    - ipBlock:
        cidr: 0.0.0.0/0

This allows the Gateway to access LLMs hosted within your VNet and outside as well. This also enables connection for the sync service to the Portkey Control Plane.

Deploying Portkey Gateway

# Add the Portkey AI Gateway helm repository
helm repo add portkey-ai https://portkey-ai.github.io/helm
helm repo update

# Install the chart
helm upgrade --install portkey-ai portkey-ai/gateway -f ./values.yaml -n $namespace --create-namespace

Verify the deployment

To confirm that the deployment was successful, follow these steps:

Verify that all pods are running correctly.

# 
kubectl get pods -n $namespace
# You should see all pods with a 'STATUS' of 'Running'.

Note: If pods are in a Pending, CrashLoopBackOff, or other error state, inspect the pod logs and events to diagnose potential issues.

Test Gateway by sending a cURL request.

Port-forward the Gateway pod

  kubectl port-forward  <POD_NAME> -n $namespace 9000:8787       # Replace <POD_NAME> with your Gateway pod's actual name.

Once port forwarding is active, open a new terminal window or tab and send a test request by running:

# Specify LLM provider and Portkey API keys
OPENAI_API_KEY=<OPENAI_API_KEY>                           # Replace <OPENAI_API_KEY> with an actual API key
PORTKEY_API_KEY=<PORTKEY_API_KEY>                         # Replace <PORTKEY_API_KEY> with Portkey API key which can be created from Portkey website(https://app.portkey.ai/api-keys).

# Configure and send the curl request
curl 'http://localhost:9000/v1/chat/completions'`\
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY"  \
-H "x-portkey-provider: openai" \
-H "x-portkey-api-key: $PORTKEY_API_KEY"  \
-d '{ 
    "model": "gpt-4o-mini", 
    "messages": [{"role": "user","content": "What is a fractal?"}]  
}'

Test gateway service integration with Load Balancer.

# Replace <LOAD_BALANCER_IP> and <LB_LISTENER_PORT_NUMBER> with the DNS name and listener port of the created load balancer, respectively.
curl 'http://<LOAD_BALANCER_IP>:<LB_LISTENER_PORT_NUMBER>/v1/chat/completions' \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY"  \
-H "x-portkey-provider: openai" \
-H "x-portkey-api-key: $PORTKEY_API_KEY"  \
-d '{
    "model": "gpt-4o-mini",
    "messages": [{"role": "user","content": "What is a fractal?"}]
}'

Integrating Gateway with Control Plane

Portkey supports the following methods for integrating the Control Plane with the Data Plane/Gateway:

IP Whitelisting

Allows control plane to access the Data Plane over the internet by restricting inbound traffic to specific IP address of Control Plane. This method requires the Data Plane to have a publicly accessible endpoint. To whitelist, add an inbound rule to the Azure NSG/Firewall allowing connections from the Portkey Control Plane’s IP (44.221.117.129) on required port. To integrate the Control Plane with the Data Plane, contact the Portkey team and provide the Public Endpoint of the Data Plane.

Verifying Gateway Integration with the Control Plane

Send a test request to Gateway using curl.
Go to Portkey website -> Logs.
Verify that the test request appears in the logs and that you can view its full details by selecting the log entry.

Uninstalling Portkey Gateway

helm uninstall portkey-ai --namespace $namespace

Setting up Permission

Azure Blob Storage

To allow the Portkey Gateway to access Azure Blob Storage for log storage, permissions must be granted. Follow the steps below to set up these permissions according to your selected access method.

Managed Identity
Entra Identity

Specify the details:

CLUSTER_NAME=<CLUSTER_NAME>                             # Specify name of AKS cluster
RESOURCE_GROUP=<RESOURCE_GROUP>                         # Specify the name of the resource group that contains the storage account.
STORAGE_ACCOUNT_NAME=<STORAGE_ACCOUNT_NAME>             # Specify the name of the storage account which will be used for storing LLM logs.
CONTAINER_NAME=<CONTAINER_NAME>                         # Specify the name of the blob store container which will be used for storing LLM logs.

Fetch Identity associated with the AKS cluster.

KUBELET_OBJECT_ID=$(az aks show \
 --resource-group $RESOURCE_GROUP \
 --name $CLUSTER_NAME \
 --query "identityProfile.kubeletidentity.objectId" \
 --output tsv)

Grant identity a role that allows it to access Blob Storage.

# Fetch storage id of storage account
STORAGE_ID=$(az storage account show \
 --name $STORAGE_ACCOUNT_NAME \
 --resource-group $RESOURCE_GROUP --query id -o tsv)

# Grant Storage Blob Data Contributor to kubelet identity
az role assignment create \
 --assignee-object-id $KUBELET_OBJECT_ID \
 --assignee-principal-type ServicePrincipal \
 --role "Storage Blob Data Contributor" \
 --scope "$STORAGE_ID/blobServices/default/containers/$CONTAINER_NAME"

Examples

Built-in Redis The following sample values.yaml below shows how to configure the built-in Redis cache and Azure Blob Storage for log store using Entra ID.

images:
  gatewayImage:
    repository: "docker.io/portkeyai/gateway_enterprise"
    pullPolicy: Always
    tag: "latest"
  dataserviceImage:
    repository: "docker.io/portkeyai/data-service"
    pullPolicy: Always
    tag: "latest"
  redisImage:
    repository: "docker.io/redis"
    pullPolicy: IfNotPresent
    tag: "7.2-alpine"
imageCredentials:
  - name: portkeyenterpriseregistrycredentials
    create: true
    registry: https://index.docker.io/v1/
    username: <DOCKER_USERNAME>
    password: <DOCKER_PASSWORD>

environment:
  create: true
  secret: true
  data:
    ANALYTICS_STORE: control_plane
    SERVICE_NAME: gateway                                                  
    PORTKEY_CLIENT_AUTH: <CLIENT_AUTH>                      # REPLACE <CLIENT_AUTH> with client auth shared by Portkey team.
    ORGANISATIONS_TO_SYNC: <ORGANIZATION_ID>                # REPLACE <ORGANIZATION_ID> with organisation_id of your account.
    PORT: "8787"

    # Configuration for using built-in redis
    CACHE_STORE: redis
    REDIS_URL: "redis://redis:6379"
    REDIS_TLS_ENABLED: "false"
   
    # Configuration for enabling Entra ID access to Azure Blob Storage.
    LOG_STORE: azure
    AZURE_STORAGE_ACCOUNT: <STORAGE_ACCOUNT_NAME>       # Specify the name of the storage account which will be used for storing LLM logs.
    AZURE_STORAGE_CONTAINER: <STORAGE_CONTAINER>        # Specify the name of the blob store container which will be used for storing LLM logs.
    AZURE_AUTH_MODE: entra
    AZURE_ENTRA_CLIENT_ID: <ENTRA_CLIENT_ID>            # Specify client id of the app created during set up of Entra ID access for blob store.
    AZURE_ENTRA_CLIENT_SECRET: <ENTRA_CLIENT_SECRET>    # Specify client secret of the app created during set up of Entra ID access for blob store.
    AZURE_ENTRA_TENANT_ID: <ENTRA_TENANT_ID>            # Specify tenant id obtained during set up of Entra ID access for blob store.         


# Enabling Load Balancer to provide access outside of cluster
service:
  type: LoadBalancer
  port: 8787
  annotations: 
    service.beta.kubernetes.io/azure-load-balancer-internal: "false"                      
    service.beta.kubernetes.io/azure-load-balancer-health-probe-protocol: "http"
    service.beta.kubernetes.io/azure-load-balancer-health-probe-request-path: "/v1/health"
    service.beta.kubernetes.io/azure-allowed-ip-ranges: 0.0.0.0/0    

Introduction

Product

Self-Hosting

Support

Components and Sizing Recommendations

Prerequisites

Create a Portkey Account

Setup Project Environment

Image Credentials Configuration

Configure Components

Cache Store

Built-in Redis

Log Store

Azure Blob Storage

Network Configuration

Ensure Outbound Network Access

Deploying Portkey Gateway

Verify the deployment

Integrating Gateway with Control Plane

IP Whitelisting

Verifying Gateway Integration with the Control Plane

Uninstalling Portkey Gateway

Setting up Permission

Azure Blob Storage

Examples

Introduction

Product

Self-Hosting

Support

​Components and Sizing Recommendations

​Prerequisites

​Create a Portkey Account

​Setup Project Environment

​Image Credentials Configuration

​Configure Components

​Cache Store

​Built-in Redis

​Log Store

​Azure Blob Storage

​Network Configuration

​Ensure Outbound Network Access

​Deploying Portkey Gateway

​Verify the deployment

​Integrating Gateway with Control Plane

​IP Whitelisting

​Verifying Gateway Integration with the Control Plane

​Uninstalling Portkey Gateway

​Setting up Permission

​Azure Blob Storage

​Examples

Components and Sizing Recommendations

Prerequisites

Create a Portkey Account

Setup Project Environment

Image Credentials Configuration

Configure Components

Cache Store

Built-in Redis

Log Store

Azure Blob Storage

Network Configuration

Ensure Outbound Network Access

Deploying Portkey Gateway

Verify the deployment

Integrating Gateway with Control Plane

IP Whitelisting

Verifying Gateway Integration with the Control Plane

Uninstalling Portkey Gateway

Setting up Permission

Azure Blob Storage

Examples