Azure Container Apps GPU Support - 2025 Features

Azure Container Apps GPU Support - 2025 Features

Complete knowledge base for Azure Container Apps with GPU support, serverless capabilities, and Dapr integration (2025 GA features).

Overview

Azure Container Apps is a serverless container platform with native GPU support, Dapr integration, and scale-to-zero capabilities for cost-efficient AI/​ML workloads.

Key 2025 Features (Build Announcements)

1. Serverless GPU (GA)

• Automatic scaling: Scale GPU workloads based on demand
• Scale-to-zero: Pay only when GPU is actively used
• Per-second billing: Granular cost control
• Optimized cold start: Fast initialization for AI models
• Reduced operational overhead: No infrastructure management

2. Dedicated GPU (GA)

• Consistent performance: Dedicated GPU resources
• Simplified AI deployment: Easy model hosting
• Long-running workloads: Ideal for training and continuous inference
• Multiple GPU types: NVIDIA A100, T4, and more

3. Dynamic Sessions with GPU (Early Access)

• Sandboxed execution: Run untrusted AI-generated code
• Hyper-V isolation: Enhanced security
• GPU-powered Python interpreter: Handle compute-intensive AI workloads
• Scale at runtime: Dynamic resource allocation

4. Foundry Models Integration

• Deploy AI models directly: During container app creation
• Ready-to-use models: Pre-configured inference endpoints
• Azure AI Foundry: Seamless integration

5. Workflow with Durable Task Scheduler (Preview)

• Long-running workflows: Reliable orchestration
• State management: Automatic persistence
• Event-driven: Trigger workflows from events

6. Native Azure Functions Support

• Functions runtime: Run Azure Functions in Container Apps
• Consistent development: Same code, serverless execution
• Event triggers: All Functions triggers supported

7. Dapr Integration (GA)

• Service discovery: Built-in DNS-based discovery
• State management: Distributed state stores
• Pub/​sub messaging: Reliable messaging patterns
• Service invocation: Resilient service-to-service calls
• Observability: Integrated tracing and metrics

Creating Container Apps with GPU

Basic Container App with Serverless GPU

# Create Container Apps environment
az containerapp env create \
  --name myenv \
  --resource-group MyRG \
  --location eastus \
  --logs-workspace-id <workspace-id> \
  --logs-workspace-key <workspace-key>

# Create Container App with GPU
az containerapp create \
  --name myapp-gpu \
  --resource-group MyRG \
  --environment myenv \
  --image myregistry.azurecr.io/​ai-model:latest \
  --cpu 4 \
  --memory 8Gi \
  --gpu-type nvidia-a100 \
  --gpu-count 1 \
  --min-replicas 0 \
  --max-replicas 10 \
  --ingress external \
  --target-port 8080

Production-Ready Container App with GPU

az containerapp create \
  --name myapp-gpu-prod \
  --resource-group MyRG \
  --environment myenv \
  \
  # Container configuration
  --image myregistry.azurecr.io/​ai-model:latest \
  --registry-server myregistry.azurecr.io \
  --registry-identity system \
  \
  # Resources
  --cpu 4 \
  --memory 8Gi \
  --gpu-type nvidia-a100 \
  --gpu-count 1 \
  \
  # Scaling
  --min-replicas 0 \
  --max-replicas 20 \
  --scale-rule-name http-scaling \
  --scale-rule-type http \
  --scale-rule-http-concurrency 10 \
  \
  # Networking
  --ingress external \
  --target-port 8080 \
  --transport http2 \
  --exposed-port 8080 \
  \
  # Security
  --registry-identity system \
  --env-vars "AZURE_CLIENT_ID=secretref:client-id" \
  \
  # Monitoring
  --dapr-app-id myapp \
  --dapr-app-port 8080 \
  --dapr-app-protocol http \
  --enable-dapr \
  \
  # Identity
  --system-assigned

Container Apps Environment Configuration

Environment with Zone Redundancy

az containerapp env create \
  --name myenv-prod \
  --resource-group MyRG \
  --location eastus \
  --logs-workspace-id <workspace-id> \
  --logs-workspace-key <workspace-key> \
  --zone-redundant true \
  --enable-workload-profiles true

Workload Profiles (Dedicated GPU)

# Create environment with workload profiles
az containerapp env create \
  --name myenv-gpu \
  --resource-group MyRG \
  --location eastus \
  --enable-workload-profiles true

# Add GPU workload profile
az containerapp env workload-profile add \
  --name myenv-gpu \
  --resource-group MyRG \
  --workload-profile-name gpu-profile \
  --workload-profile-type GPU-A100 \
  --min-nodes 0 \
  --max-nodes 10

# Create container app with GPU profile
az containerapp create \
  --name myapp-dedicated-gpu \
  --resource-group MyRG \
  --environment myenv-gpu \
  --workload-profile-name gpu-profile \
  --image myregistry.azurecr.io/​training-job:latest \
  --cpu 8 \
  --memory 16Gi \
  --min-replicas 1 \
  --max-replicas 5

GPU Scaling Rules

Custom Prometheus Scaling

az containerapp create \
  --name myapp-gpu-prometheus \
  --resource-group MyRG \
  --environment myenv \
  --image myregistry.azurecr.io/​ai-model:latest \
  --cpu 4 \
  --memory 8Gi \
  --gpu-type nvidia-a100 \
  --gpu-count 1 \
  --min-replicas 0 \
  --max-replicas 10 \
  --scale-rule-name gpu-utilization \
  --scale-rule-type custom \
  --scale-rule-custom-type prometheus \
  --scale-rule-metadata \
    serverAddress=http://prometheus.monitoring.svc.cluster.local:9090 \
    metricName=gpu_utilization \
    threshold=80 \
    query="avg(nvidia_gpu_utilization{app='myapp'})"

Queue-Based Scaling (Azure Service Bus)

az containerapp create \
  --name myapp-queue-processor \
  --resource-group MyRG \
  --environment myenv \
  --image myregistry.azurecr.io/​batch-processor:latest \
  --cpu 4 \
  --memory 8Gi \
  --gpu-type nvidia-t4 \
  --gpu-count 1 \
  --min-replicas 0 \
  --max-replicas 50 \
  --scale-rule-name queue-scaling \
  --scale-rule-type azure-servicebus \
  --scale-rule-metadata \
    queueName=ai-jobs \
    namespace=myservicebus \
    messageCount=5 \
  --scale-rule-auth connection=servicebus-connection

Dapr Integration

Enable Dapr on Container App

az containerapp create \
  --name myapp-dapr \
  --resource-group MyRG \
  --environment myenv \
  --image myregistry.azurecr.io/​myapp:latest \
  --enable-dapr \
  --dapr-app-id myapp \
  --dapr-app-port 8080 \
  --dapr-app-protocol http \
  --dapr-http-max-request-size 4 \
  --dapr-http-read-buffer-size 4 \
  --dapr-log-level info \
  --dapr-enable-api-logging true

Dapr State Store (Azure Cosmos DB)

# Create Dapr component for state store
apiVersion: dapr.io/​v1alpha1
kind: Component
metadata:
  name: statestore
spec:
  type: state.azure.cosmosdb
  version: v1
  metadata:
    - name: url
      value: "https://mycosmosdb.documents.azure.com:443/"
    - name: masterKey
      secretRef: cosmosdb-key
    - name: database
      value: "mydb"
    - name: collection
      value: "state"

# Create the component
az containerapp env dapr-component set \
  --name myenv \
  --resource-group MyRG \
  --dapr-component-name statestore \
  --yaml component.yaml

Dapr Pub/​Sub (Azure Service Bus)

apiVersion: dapr.io/​v1alpha1
kind: Component
metadata:
  name: pubsub
spec:
  type: pubsub.azure.servicebus.topics
  version: v1
  metadata:
    - name: connectionString
      secretRef: servicebus-connection
    - name: consumerID
      value: "myapp"

Service-to-Service Invocation

# Python example using Dapr SDK
from dapr.clients import DaprClient

with DaprClient() as client:
    # Invoke another service
    response = client.invoke_method(
        app_id='other-service',
        method_name='process',
        data='{"input": "data"}'
    )

    # Save state
    client.save_state(
        store_name='statestore',
        key='mykey',
        value='myvalue'
    )

    # Publish message
    client.publish_event(
        pubsub_name='pubsub',
        topic_name='orders',
        data='{"orderId": "123"}'
    )

AI Model Deployment Patterns

OpenAI-Compatible Endpoint

# Dockerfile for vLLM model serving
FROM vllm/​vllm-openai:latest

ENV MODEL_NAME="meta-llama/​Llama-3.1-8B-Instruct"
ENV GPU_MEMORY_UTILIZATION=0.9
ENV MAX_MODEL_LEN=4096

CMD ["--model", "${MODEL_NAME}", \
     "--gpu-memory-utilization", "${GPU_MEMORY_UTILIZATION}", \
     "--max-model-len", "${MAX_MODEL_LEN}", \
     "--port", "8080"]

# Deploy vLLM model
az containerapp create \
  --name llama-inference \
  --resource-group MyRG \
  --environment myenv \
  --image vllm/​vllm-openai:latest \
  --cpu 8 \
  --memory 32Gi \
  --gpu-type nvidia-a100 \
  --gpu-count 1 \
  --min-replicas 1 \
  --max-replicas 5 \
  --target-port 8080 \
  --ingress external \
  --env-vars \
    MODEL_NAME="meta-llama/​Llama-3.1-8B-Instruct" \
    GPU_MEMORY_UTILIZATION="0.9" \
    HF_TOKEN=secretref:huggingface-token

Stable Diffusion Image Generation

az containerapp create \
  --name stable-diffusion \
  --resource-group MyRG \
  --environment myenv \
  --image myregistry.azurecr.io/​stable-diffusion:latest \
  --cpu 4 \
  --memory 16Gi \
  --gpu-type nvidia-a100 \
  --gpu-count 1 \
  --min-replicas 0 \
  --max-replicas 10 \
  --target-port 7860 \
  --ingress external \
  --scale-rule-name http-scaling \
  --scale-rule-type http \
  --scale-rule-http-concurrency 1

Batch Processing Job

az containerapp job create \
  --name batch-training-job \
  --resource-group MyRG \
  --environment myenv \
  --trigger-type Manual \
  --image myregistry.azurecr.io/​training:latest \
  --cpu 8 \
  --memory 32Gi \
  --gpu-type nvidia-a100 \
  --gpu-count 2 \
  --parallelism 1 \
  --replica-timeout 7200 \
  --replica-retry-limit 3 \
  --env-vars \
    DATASET_URL="https://mystorage.blob.core.windows.net/​datasets/​train.csv" \
    MODEL_OUTPUT="https://mystorage.blob.core.windows.net/​models/" \
    EPOCHS="100"

# Execute job
az containerapp job start \
  --name batch-training-job \
  --resource-group MyRG

Monitoring and Observability

Application Insights Integration

az containerapp create \
  --name myapp-monitored \
  --resource-group MyRG \
  --environment myenv \
  --image myregistry.azurecr.io/​myapp:latest \
  --env-vars \
    APPLICATIONINSIGHTS_CONNECTION_STRING=secretref:appinsights-connection

Query Logs

# Stream logs
az containerapp logs show \
  --name myapp-gpu \
  --resource-group MyRG \
  --follow

# Query with Log Analytics
az monitor log-analytics query \
  --workspace <workspace-id> \
  --analytics-query "ContainerAppConsoleLogs_CL | where ContainerAppName_s == 'myapp-gpu' | take 100"

Metrics and Alerts

# Create metric alert for GPU usage
az monitor metrics alert create \
  --name high-gpu-usage \
  --resource-group MyRG \
  --scopes $(az containerapp show -g MyRG -n myapp-gpu --query id -o tsv) \
  --condition "avg Requests > 100" \
  --window-size 5m \
  --evaluation-frequency 1m \
  --action <action-group-id>

Security Best Practices

Managed Identity

# Create with system-assigned identity
az containerapp create \
  --name myapp-identity \
  --resource-group MyRG \
  --environment myenv \
  --system-assigned \
  --image myregistry.azurecr.io/​myapp:latest

# Get identity principal ID
IDENTITY_ID=$(az containerapp show -g MyRG -n myapp-identity --query identity.principalId -o tsv)

# Assign role to access Key Vault
az role assignment create \
  --assignee $IDENTITY_ID \
  --role "Key Vault Secrets User" \
  --scope /​subscriptions/<sub-id>/​resourceGroups/​MyRG/​providers/​Microsoft.KeyVault/​vaults/​mykeyvault

# Use user-assigned identity
az identity create --name myapp-identity --resource-group MyRG
IDENTITY_RESOURCE_ID=$(az identity show -g MyRG -n myapp-identity --query id -o tsv)

az containerapp create \
  --name myapp-user-identity \
  --resource-group MyRG \
  --environment myenv \
  --user-assigned $IDENTITY_RESOURCE_ID \
  --image myregistry.azurecr.io/​myapp:latest

Secret Management

# Add secrets
az containerapp secret set \
  --name myapp-gpu \
  --resource-group MyRG \
  --secrets \
    huggingface-token="<token>" \
    api-key="<key>"

# Reference secrets in environment variables
az containerapp update \
  --name myapp-gpu \
  --resource-group MyRG \
  --set-env-vars \
    HF_TOKEN=secretref:huggingface-token \
    API_KEY=secretref:api-key

Cost Optimization

Scale-to-Zero Configuration

az containerapp create \
  --name myapp-scale-zero \
  --resource-group MyRG \
  --environment myenv \
  --image myregistry.azurecr.io/​myapp:latest \
  --min-replicas 0 \
  --max-replicas 10 \
  --scale-rule-name http-scaling \
  --scale-rule-type http \
  --scale-rule-http-concurrency 10

Cost savings: Pay only when requests are being processed. GPU costs are per-second when active.

Right-Sizing Resources

# Start with minimal resources
--cpu 2 --memory 4Gi --gpu-count 1

# Monitor and adjust based on actual usage
az monitor metrics list \
  --resource $(az containerapp show -g MyRG -n myapp-gpu --query id -o tsv) \
  --metric "CpuPercentage,MemoryPercentage"

Use Spot/​Preemptible GPUs (Future Feature)

When available, configure spot instances for non-critical workloads to save up to 80% on GPU costs.

Troubleshooting

Check Revision Status

az containerapp revision list \
  --name myapp-gpu \
  --resource-group MyRG \
  --output table

View Revision Details

az containerapp revision show \
  --name <revision-name> \
  --app myapp-gpu \
  --resource-group MyRG

Restart Container App

az containerapp update \
  --name myapp-gpu \
  --resource-group MyRG \
  --force-restart

GPU Not Available

If GPU is not provisioning:

1. Check region availability: Not all regions support GPU
2. Verify quota: Request quota increase if needed
3. Check workload profile: Ensure GPU workload profile is created

Best Practices

✓ Use scale-to-zero for intermittent workloads ✓ Implement health probes (liveness and readiness) ✓ Use managed identities for authentication ✓ Store secrets in Azure Key Vault ✓ Enable Dapr for microservices patterns ✓ Configure appropriate scaling rules ✓ Monitor GPU utilization and adjust resources ✓ Use Container Apps jobs for batch processing ✓ Implement retry logic for transient failures ✓ Use Application Insights for observability

References

• Container Apps GPU Documentation
• Dapr Integration
• Scaling Rules
• Build 2025 Announcements

Azure Container Apps with GPU support provides the ultimate serverless platform for AI/​ML workloads!