Application - Qovery

Prerequisites

Before creating an application:

You have created an Environment

Overview

An application is a container unit that is part of a Project within an Environment. You can deploy multiple applications within the same environment, and they can communicate with each other and connect to databases.

Deployment Sources

Qovery supports two deployment sources for applications:

Git Repository

Qovery pulls your code from a Git repository, builds the application, and deploys it to your Kubernetes cluster. Supported Providers: GitHub, GitLab, Bitbucket

Container Registry

Qovery pulls a pre-built container image from your configured registry and deploys it to your Kubernetes cluster.

Container registries must be configured at the organization level by administrators. See Container Registry Management.

Creating an Application

Select Source

Choose Git Repository or Container Registry as your deployment source

Configure Source

For Git Repository:

Select Git provider
Choose repository
Select branch
Specify root application path (if not at repository root)
Configure Dockerfile path

For Container Registry:

Select configured registry
Enter image name
Specify image tag

Configure Resources

vCPU: Default 500m (0.5 cores)
RAM: Default 512MB
GPU: Optional GPU allocation (requires GPU-enabled cluster)
Auto-scaling: Configure min/max instances (optional)

Configure Ports

Internal Port: Port your application listens on
Protocol: HTTPS, gRPC, TCP, or UDP
Publicly Accessible: Enable to expose via load balancer

Health Checks (Optional)

Liveness Probe: Determines if container should be restarted
Readiness Probe: Determines if container should receive traffic

Environment Variables

Configure environment variables for your application

Review and Deploy

Review configuration and choose:

Create: Save configuration without deploying
Create and Deploy: Save and immediately deploy

Configuration

General Settings

Git Repository Configuration:

Git provider and repository
Branch selection
Root application path
Dockerfile path
Build arguments
Multi-stage build target selection

Container Registry Configuration:

Registry selection
Image name
Image tag
Entrypoint override
CMD arguments override

Resources

CPU and Memory

vCPU allocation: Configure in millicores (e.g., 500m = 0.5 cores)
RAM allocation: Configure in MB or GB
Adjust based on your application’s resource requirements

GPU

Requirements: AWS with Karpenter enabled

Number of GPUs per instance
Ideal for AI/ML workloads
Available GPU types depend on your cluster configuration

Instances & Autoscaling

Qovery offers three autoscaling modes to match your application’s needs:

Not recommended for production workloads. A single instance has no redundancy—if the pod fails or the node undergoes maintenance, your application will be temporarily unavailable.For high availability:

Set minimum instances to 2 if your app can run on 1 instance
Set minimum instances to 3 or higher if your app requires multiple instances to handle necessary traffic
This ensures redundancy during node maintenance or pod failures

Note: KEDA (Event-Driven) autoscaling supports setting minimum instances to 0 for scale-to-zero capability on sporadic workloads.

No Scaling
HPA (CPU/Memory)
KEDA (Event-Driven)

Fixed Number of Instances (No Autoscaling)

Your application runs with a fixed number of instances.Configuration:

Set the number of instances to run
No automatic scaling occurs

Use Cases:

Development environments
Proof of concepts (POC)
Testing and experimentation

Event-Driven Autoscaling with KEDA

Scale your application based on external event sources like message queues, streams, databases, and more.

Beta Feature - KEDA autoscaling is currently in beta and available to select customers only. Contact your Qovery account manager if you’re interested in early access.

AWS and GCP clusters only - KEDA autoscaling is currently available for applications deployed on AWS and GCP.

Prerequisite: Enable KEDA at Cluster LevelBefore using event-driven autoscaling, KEDA must be enabled on your cluster:

Navigate to your cluster settings
Go to the General tab
Enable the KEDA option
Redeploy your cluster for the changes to take effect

What is KEDA?KEDA (Kubernetes Event-Driven Autoscaler) extends Kubernetes autoscaling capabilities by scaling based on external metrics and events rather than just CPU/memory.Use Cases:

Queue-based workloads (SQS, RabbitMQ, Kafka)
Stream processing (Kinesis, Kafka)
Database polling (PostgreSQL, MySQL)
Custom metrics (Prometheus, Datadog)
Scheduled scaling (cron-based)

Migrating from HPA to KEDA

Beta Limitation: If your application is currently using HPA (CPU/Memory) autoscaling and you want to migrate to KEDA, follow these steps:

Switch your application to No Scaling mode
Redeploy your application
Switch to KEDA (Event-Driven) mode
Configure your KEDA scalers

Direct migration from HPA to KEDA is not currently supported and will not work properly.

General Configuration

Required Settings:

Minimum instances: Baseline number of pods (minimum: 0 for scale-to-zero)
Maximum instances: Upper scaling limit

Optional Settings:

Polling interval: How often KEDA checks the event source (default: 30 seconds)
Cooldown period: Wait time before scaling down after scale-up (default: 300 seconds)

Scale-to-Zero: KEDA supports setting minimum instances to 0, allowing your application to scale down completely when there are no events. When events are detected, KEDA automatically scales up from 0 to handle the workload. This is ideal for cost optimization on sporadic workloads.

Multiple scalers behavior: If you configure multiple scalers, KEDA uses the scaler that calculates the highest target number of instances. For example, if Scaler A monitors a queue with 15 messages and calculates 15 target instances (1 message per instance), and Scaler B monitors a queue with 50 messages and calculates 5 target instances (10 messages per instance), KEDA scales to 15 instances (the higher target).

Scaler Configuration

Each scaler monitors a specific event source. You can add multiple scalers to respond to different metrics.

Built-in scalers only: Qovery currently supports KEDA’s built-in scalers. External scalers are not supported at this time.

For Each Scaler:

Scaler Type: The event source type (e.g., aws-sqs-queue, rabbitmq, kafka, prometheus)
Scaler YAML Configuration: Source-specific parameters in YAML format
Trigger Authentication YAML (Optional): Authentication credentials if required

Scaler YAML Configuration accepts the following fields:

metadata - Scaler-specific parameters (required). Each scaler type has different metadata fields. See the KEDA Scalers documentation for available parameters per scaler type.
metricType - Metric target type: Value, AverageValue, or Utilization (optional)
useCachedMetrics - Whether to use cached metrics (optional, boolean)

Common Scaler Types:

aws-sqs-queue - AWS Simple Queue Service
aws-kinesis-stream - AWS Kinesis Data Streams
rabbitmq - RabbitMQ queues
kafka - Apache Kafka topics
prometheus - Prometheus metrics
postgresql - PostgreSQL queries
redis - Redis lists/streams
cron - Time-based scaling

For the complete list, see KEDA Scalers documentation.

Authentication Methods

KEDA supports multiple authentication methods. Each scaler type supports a subset of these methods.Common Authentication Methods:1. podIdentity - Use IAM roles (AWS) or managed identities (Azure)

podIdentity:
  provider: aws  # or azure
  identityOwner: workload  # inherit from application service account
  # OR
  roleArn: arn:aws:iam::123456789012:role/MyRole  # specify role directly

2. secretTargetRef - Reference Qovery environment variables or secrets

secretTargetRef:
  - parameter: connectionString  # Parameter name defined by the scaler
    key: qovery.env.MY_CONNECTION_STRING  # Qovery variable reference

Use qovery.env.VARIABLE_NAME to reference environment variables or secrets defined in your Qovery application.

For complete authentication options, see KEDA Authentication documentation.

Scaler Examples by Type

AWS SQS
Redis
RabbitMQ

AWS SQS Queue Scaling

Let’s explore AWS SQS in detail as it’s a common use case.How KEDA Works with SQS:

KEDA reads queue metrics (number of messages) via AWS CloudWatch
KEDA does NOT consume messages from the queue
Your application pods consume and process the actual messages
KEDA only uses metrics to make scaling decisions

Authentication Requirements:AWS SQS requires IAM authentication. Qovery supports two authentication models:

Shared IAM Role
KEDA-Only IAM Role

Shared IAM Role (Application + KEDA)

When to use:

Your application already consumes from SQS
You want to minimize IAM roles
Simpler configuration

How it works:

Single IAM role attached to your application’s service account
Both application pods and KEDA use the same IAM role
No AWS credentials stored as secrets

Step-by-Step AWS Configuration

Create IAM Policy for SQS Access

This policy grants your application the necessary permissions to interact with SQS (consume, process messages) and allows KEDA to read queue metrics.

Go to IAM Console → Policies → Create policy
Select the JSON tab and paste:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "SqsFullAccess",
      "Effect": "Allow",
      "Action": [
        "sqs:GetQueueAttributes",
        "sqs:GetQueueUrl",
        "sqs:ReceiveMessage",
        "sqs:DeleteMessage",
        "sqs:ChangeMessageVisibility"
      ],
      "Resource": "arn:aws:sqs:<region>:<account-id>:<queue-name>"
    }
  ]
}

Name the policy: AppSqsFullAccessPolicy
Click Create policy

This policy includes both application permissions (ReceiveMessage, DeleteMessage) and KEDA metric reading permissions (GetQueueAttributes).

Create IAM Role and Service Account

Follow the complete tutorial to create an IAM role and configure your application’s service account with AWS IAM roles.

AWS IAM Roles Tutorial

Step-by-step guide to create IAM roles and service accounts for Qovery applications

When creating the role:

Attach the policy created in Step 1: AppSqsFullAccessPolicy
Name the role: AppSqsConsumerRole
Note the role ARN for later use

Configure Trust Relationship for KEDA

To allow KEDA to inherit the IAM role from your application’s service account, you must update the trust relationship.

Open the role you created: AppSqsConsumerRole
Go to the Trust relationships tab and click Edit trust policy
Update the JSON to allow both your application namespace AND the KEDA operator:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Federated": "arn:aws:iam::<account-id>:oidc-provider/oidc.eks.<region>.amazonaws.com/id/<oidc-id>"
      },
      "Action": "sts:AssumeRoleWithWebIdentity",
      "Condition": {
        "StringEquals": {
          "oidc.eks.<region>.amazonaws.com/id/<oidc-id>:aud": "sts.amazonaws.com",
          "oidc.eks.<region>.amazonaws.com/id/<oidc-id>:sub": [
            "system:serviceaccount:<your-namespace>:<service-account-name>",
            "system:serviceaccount:kube-system:keda-operator"
          ]
        }
      }
    }
  ]
}

Click Update policy

Replace <account-id>, <region>, <oidc-id>, <your-namespace>, and <service-account-name> with your specific values. The key addition is the keda-operator service account in the trust relationship.

Qovery Configuration

After completing the AWS configuration steps above, configure the KEDA scaler in Qovery.Scaler YAML (paste in “Configuration YAML” field):

metadata:
  queueURL: "https://sqs.us-east-1.amazonaws.com/123456789012/my-queue"
  awsRegion: "us-east-1"
  queueLength: "5"
  activationQueueLength: "1"

Trigger Authentication YAML (paste in “Trigger Authentication YAML” field):

podIdentity:
  provider: aws
  identityOwner: workload

With identityOwner: workload, KEDA automatically inherits the IAM role from your application’s service account. This requires the trust relationship configuration from Step 3.

Resulting Behavior

Both application and KEDA use the same IAM role - The role is shared between your application pods and KEDA operator
Application can consume from SQS - Your application has full permissions to receive and process messages
KEDA monitors queue depth - KEDA uses the same role to read queue metrics for scaling decisions
Simplified IAM management - Only one role to manage and maintain

Separate IAM Role for KEDA

When to use:

Your application does NOT consume from SQS
KEDA scales based on queue depth, but another service processes messages
You want separate security boundaries

How it works:

Dedicated IAM role for KEDA’s scaling operations
Application has no AWS identity
IAM role ARN specified directly in trigger authentication

Step-by-Step AWS Configuration

Create the IAM Policy

This policy grants KEDA the minimum permissions to read SQS metrics (queue depth) without allowing it to process or delete messages.

Go to IAM Console → Policies → Create policy
Select the JSON tab and paste:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "KedaSqsMetricReadAccess",
      "Effect": "Allow",
      "Action": [
        "sqs:GetQueueAttributes",
        "sqs:GetQueueUrl"
      ],
      "Resource": "arn:aws:sqs:<region>:<account-id>:<queue-name>"
    }
  ]
}

Name the policy: KedaSqsMetricReadPolicy
Click Create policy

Not required: sqs:ReceiveMessage - KEDA only reads metrics, it does not consume or delete messages.

Create the IAM Role (OIDC Integration)

This step creates the AWS identity that the KEDA operator will assume using IRSA (IAM Roles for Service Accounts).

Navigate to IAM → Roles → Create role
Select Web Identity as the trusted entity type
Identity provider: Select your EKS cluster’s OIDC URL from the dropdown
Audience: Select sts.amazonaws.com
Click Next
Search for and attach the policy created in Step 1: KedaSqsMetricReadPolicy
Name the role: KedaOperatorScalingRole
Click Create role

Refine the Trust Relationship

To ensure maximum security, restrict this role so that only the KEDA operator pod in the kube-system namespace can use it.

Open the role you just created: KedaOperatorScalingRole
Go to the Trust relationships tab and click Edit trust policy
Update the JSON, replacing the placeholders with your cluster-specific info:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Federated": "arn:aws:iam::<account-id>:oidc-provider/oidc.eks.<region>.amazonaws.com/id/<oidc-id>"
      },
      "Action": "sts:AssumeRoleWithWebIdentity",
      "Condition": {
        "StringEquals": {
          "oidc.eks.<region>.amazonaws.com/id/<oidc-id>:aud": "sts.amazonaws.com",
          "oidc.eks.<region>.amazonaws.com/id/<oidc-id>:sub": "system:serviceaccount:kube-system:keda-operator"
        }
      }
    }
  ]
}

Click Update policy

Replace <account-id>, <region>, and <oidc-id> with your AWS account ID, EKS region, and OIDC provider ID. You can find the OIDC provider URL in your EKS cluster details under the Configuration tab.

Qovery Configuration

After completing the AWS configuration steps above, configure the KEDA scaler in Qovery with the IAM role ARN you created.Scaler YAML (paste in “Configuration YAML” field):

metadata:
  queueURL: "https://sqs.us-east-1.amazonaws.com/123456789012/my-queue"
  awsRegion: "us-east-1"
  queueLength: "5"
  activationQueueLength: "1"

Trigger Authentication YAML (paste in “Trigger Authentication YAML” field):

podIdentity:
  provider: aws
  roleArn: arn:aws:iam::<account-id>:role/KedaOperatorScalingRole

Replace <account-id> with your AWS account ID. You can find the complete role ARN in the IAM Console under the KedaOperatorScalingRole details page.

No ServiceAccount configuration is required on the application itself. The application runs without AWS identity.

Resulting Behavior

Only KEDA has AWS credentials - The dedicated IAM role is only accessible to KEDA operator
Application runs without AWS identity - Your application pods have no AWS permissions
Scaling is isolated from application permissions - KEDA can read queue metrics independently of the application’s capabilities

Redis List/Stream Scaling

Scale your application based on Redis list length or stream lag.Scaler Types:

redis - For Redis lists
redis-streams - For Redis streams

Authentication: Redis does NOT use IAM authentication. You can provide credentials directly in the configuration or reference Qovery environment variables/secrets.

Scaler YAML Example (paste in “Configuration YAML” field):

metadata:
  address: redis-queue.default.svc.cluster.local:6379
  listName: myqueue
  listLength: "1"
  databaseIndex: "0"

Trigger Authentication YAML (paste in “Trigger Authentication YAML” field):

secretTargetRef:
  - parameter: password
    key: qovery.env.REDIS_PASSWORD

You can reference Qovery environment variables or secrets using qovery.env.VARIABLE_NAME, or provide credentials directly in the YAML configuration.

Configuration Parameters:

address - Redis server address (host:port or Kubernetes service DNS)
listName - Name of the Redis list to monitor
listLength - Target list length per instance
databaseIndex - Redis database index (optional, default: “0”)

For complete parameter reference, see KEDA Redis Scaler documentation.

RabbitMQ Queue Scaling

Scale your application based on RabbitMQ queue depth.Authentication: RabbitMQ does NOT use IAM authentication. You can provide credentials directly in the configuration or reference Qovery environment variables/secrets.

Scaler YAML Example (paste in “Configuration YAML” field):

metadata:
  protocol: amqp
  queueName: myqueue
  mode: QueueLength
  value: "1"

Trigger Authentication YAML (paste in “Trigger Authentication YAML” field):

secretTargetRef:
  - parameter: host
    key: qovery.env.RABBITMQ_URL

You can reference Qovery environment variables or secrets using qovery.env.VARIABLE_NAME, or provide the connection string directly in the YAML configuration. The RabbitMQ URL format is: amqp://user:password@rabbitmq.example.com:5672/vhost

Configuration Parameters:

protocol - Connection protocol: amqp or http (optional, default: amqp)
queueName - Name of the RabbitMQ queue to monitor
mode - Scaling mode: QueueLength (default) or MessageRate
value - Target queue length per instance

For complete parameter reference, see KEDA RabbitMQ Scaler documentation.

Additional Resources

Testing your KEDA configuration: Start with a low queueLength value and a small maxInstances to test scaling behavior. Monitor your application logs and KEDA operator logs to verify scaling triggers work as expected.

Storage

Ephemeral Storage (default):

Container filesystem
Data lost on restart
Use for temporary files and caches

Persistent Storage:

Block storage volumes
Data persists across restarts
Configure mount path and size
SSD storage type (fast_ssd)
Typical range: 5GB-10GB

Persistent storage cannot be shared between replicas. For shared storage, use external services like S3 or blob storage.

Ports

Configure network exposure for your application: Port Configuration:

Internal Port: Port your application listens on
External Port: HTTPS defaults to 443, HTTP to 80
Protocol: HTTP/HTTPS, gRPC, TCP, UDP
Publicly Accessible: Toggle to expose via load balancer
URL Rewriting: Configure path rewriting (optional)

For HTTP and gRPC protocols, the external port is set to 443 by default with automatic TLS.

Connection Timeouts: Connections on public ports are automatically closed after 60 seconds by default. Configure custom timeouts in advanced settings for long-lived connections.

TCP/UDP Ports: Exposing TCP/UDP ports publicly requires provisioning a dedicated load balancer, which takes approximately 15 minutes and incurs additional cloud provider costs.

Health Checks

Liveness Probe:

Determines if container should be restarted
Configure endpoint, port, initial delay, and intervals

Readiness Probe:

Determines if container should receive traffic
Configure endpoint, port, initial delay, and intervals

Deployment Restrictions

Configure when deployments should be triggered:

File path filters: Deploy only when specific files change
Branch filters: Deploy only from specific branches

Internet Connectivity

Qovery-Assigned Domains

Every publicly accessible application receives an automatic domain with TLS certificate:

<app-name>-<random-id>.<cluster-id>.qovery.io

Custom Domains

Add your own custom domain to your application:

Add Domain

Navigate to application settings → Domains → Add Domain

Configure DNS

Create a CNAME record in your DNS provider:

CNAME: your-domain.com → <app-name>.<cluster-id>.qovery.io

Verify

Qovery automatically validates DNS configuration and provisions SSL/TLS certificate

CDN Proxy Mode

Enable CDN proxy mode for custom domains to route traffic through your CDN provider.

Additional Features

Service Connections

Applications can connect to:

Databases within the same environment
Other applications within the same environment
External services via environment variables

Connection details are automatically injected as environment variables. See Environment Variables.

SSH Access

Access running containers via SSH using the Qovery CLI:

qovery shell

Clone Service

Duplicate an application configuration to the same or different environment. Cloning copies:

General configuration
Resource settings
Port configuration
Storage configuration
Build settings

Cloning excludes custom domains and some built-in environment variables to prevent conflicts.

Advanced Settings

Configure advanced options including:

Build timeout and resources
Deployment strategy
Network settings
Node affinity
Annotations and labels

See Service Advanced Settings for details.

Delete Application

To delete an application:

Navigate to application settings
Click Delete Application
Confirm deletion

Deleting an application is permanent and cannot be undone. All associated data and configurations will be removed.

Deploy Your First App

Step-by-step deployment guide

Environment Variables

Manage configuration and secrets

Databases

Connect databases to your application

Auto-Deploy

Set up automatic deployments

Overview

Account & Organization

Clusters & Cloud

Source Control

Projects & Environments

Services

Deployment & CI/CD

Secrets Management

Observability

Notifications

Networking & Advanced

​Prerequisites

​Overview

​Deployment Sources

​Git Repository

​Container Registry

​Creating an Application

​Configuration

​General Settings

​Resources

​CPU and Memory

​GPU

​Instances & Autoscaling

​Fixed Number of Instances (No Autoscaling)

​Classic Horizontal Pod Autoscaling

​Event-Driven Autoscaling with KEDA

​Migrating from HPA to KEDA

​General Configuration

​Scaler Configuration

​Authentication Methods

​Scaler Examples by Type

​AWS SQS Queue Scaling

​Shared IAM Role (Application + KEDA)

​Step-by-Step AWS Configuration

AWS IAM Roles Tutorial

​Qovery Configuration

​Resulting Behavior

​Separate IAM Role for KEDA

​Step-by-Step AWS Configuration

​Qovery Configuration

​Resulting Behavior

​Redis List/Stream Scaling

​RabbitMQ Queue Scaling

​Additional Resources

​Storage

​Ports

​Health Checks

​Deployment Restrictions

​Internet Connectivity

​Qovery-Assigned Domains

​Custom Domains

​CDN Proxy Mode

​Additional Features

​Service Connections

​SSH Access

​Clone Service

​Advanced Settings

​Delete Application

​Related Resources

Deploy Your First App

Environment Variables

Databases

Auto-Deploy

Prerequisites

Overview

Deployment Sources

Git Repository

Container Registry

Creating an Application

Configuration

General Settings

Resources

CPU and Memory

GPU

Instances & Autoscaling

Fixed Number of Instances (No Autoscaling)

Classic Horizontal Pod Autoscaling

Event-Driven Autoscaling with KEDA

Migrating from HPA to KEDA

General Configuration

Scaler Configuration

Authentication Methods

Scaler Examples by Type

AWS SQS Queue Scaling

Shared IAM Role (Application + KEDA)

Step-by-Step AWS Configuration

Qovery Configuration

Resulting Behavior

Separate IAM Role for KEDA

Step-by-Step AWS Configuration

Qovery Configuration

Resulting Behavior

Redis List/Stream Scaling

RabbitMQ Queue Scaling

Additional Resources

Storage

Ports

Health Checks

Deployment Restrictions

Internet Connectivity

Qovery-Assigned Domains

Custom Domains

CDN Proxy Mode

Additional Features

Service Connections

SSH Access

Clone Service

Advanced Settings

Delete Application

Related Resources