Azure Integration

Integrate BroxiAI with Microsoft Azure cloud services and ecosystem

Learn how to integrate BroxiAI workflows with Microsoft Azure services for enterprise-grade AI applications with enhanced security, compliance, and scalability.

Overview

BroxiAI integrates seamlessly with Azure services to provide:

• Enterprise-grade security and compliance

• Global scale and high availability

• Advanced AI and cognitive services

• Hybrid and multi-cloud capabilities

• Cost-effective resource management

Core Azure Services Integration

Azure OpenAI Service

Connect BroxiAI to Azure's managed OpenAI service for enhanced security and compliance.

Configuration

{
  "provider": "azure_openai",
  "endpoint": "https://your-resource.openai.azure.com/",
  "api_key": "${AZURE_OPENAI_API_KEY}",
  "api_version": "2023-12-01-preview",
  "deployment_name": "gpt-4",
  "models": {
    "chat": "gpt-4",
    "embeddings": "text-embedding-ada-002",
    "completion": "gpt-35-turbo"
  }
}

Supported Models

• GPT-4 (8K, 32K)

• GPT-3.5 Turbo (4K, 16K)

• Text Embedding Ada 002

• DALL-E 3 (Image generation)

• Whisper (Speech-to-text)

Enterprise Features

• Virtual network integration

• Private endpoints

• Customer-managed keys

• Content filtering

• Abuse monitoring

Azure Blob Storage

Store and process documents, images, and other files securely.

Use Cases

• Document storage for RAG applications

• File upload/download workflows

• Batch processing of documents

• Data archival and compliance

• Content delivery networks

Integration Example

# BroxiAI workflow component
{
  "component": "AzureBlobLoader",
  "config": {
    "account_name": "mystorageaccount",
    "container_name": "documents", 
    "blob_name": "contracts/agreement.pdf",
    "connection_string": "${AZURE_STORAGE_CONNECTION_STRING}",
    "sas_token": "${AZURE_STORAGE_SAS_TOKEN}"
  }
}

Security Configuration

Azure Blob Security:
  Authentication:
    - Azure Active Directory
    - Shared Access Signatures (SAS)
    - Account keys (not recommended for production)
  
  Network Security:
    - Private endpoints
    - Network ACLs
    - Firewall rules
    - Virtual network integration
  
  Encryption:
    - Encryption at rest (AES-256)
    - Customer-managed keys
    - Encryption in transit (HTTPS)
    - Client-side encryption

Azure SQL Database

Connect to managed SQL databases for data retrieval and storage.

Configuration

{
  "database_type": "azure_sql",
  "server": "myserver.database.windows.net",
  "database": "production_db",
  "authentication": "azure_ad",
  "connection_options": {
    "encrypt": true,
    "trust_server_certificate": false,
    "connection_timeout": 30,
    "command_timeout": 600
  }
}

Authentication Methods

• Azure Active Directory integration

• SQL Server authentication

• Managed identity

• Service principal

SQL Agent Integration

• Natural language to SQL conversion

• Schema analysis and optimization

• Query performance monitoring

• Security and compliance checks

Azure Cosmos DB

Integrate with globally distributed NoSQL database.

Use Cases

• Session management and storage

• Real-time analytics

• Document storage and retrieval

• User preference management

• Conversation history

Configuration

{
  "cosmosdb_config": {
    "endpoint": "https://myaccount.documents.azure.com:443/",
    "primary_key": "${COSMOS_DB_PRIMARY_KEY}",
    "database_name": "broxi_sessions",
    "container_name": "conversations",
    "partition_key": "/user_id"
  }
}

Vector Search Capabilities

# Vector search in Cosmos DB
vector_search_config = {
  "embedding_dimension": 1536,
  "similarity_metric": "cosine",
  "index_type": "quantizedFlat",
  "vector_indexes": [
    {
      "path": "/embedding_vector",
      "type": "quantizedFlat"
    }
  ]
}

Azure Functions

Trigger BroxiAI workflows from serverless functions.

Function App Configuration

import azure.functions as func
import requests
import json
import os

def main(req: func.HttpRequest) -> func.HttpResponse:
    """Azure Function to trigger BroxiAI workflow"""
    
    try:
        # Get request data
        req_body = req.get_json()
        user_message = req_body.get('message')
        user_id = req_body.get('user_id')
        
        # Call BroxiAI workflow
        broxi_response = requests.post(
            "https://api.broxi.ai/v1/flows/customer-support/run",
            headers={
                "Authorization": f"Bearer {os.environ['BROXI_API_TOKEN']}",
                "Content-Type": "application/json"
            },
            json={
                "input": user_message,
                "variables": {
                    "user_id": user_id,
                    "source": "azure_function"
                }
            }
        )
        
        broxi_response.raise_for_status()
        result = broxi_response.json()
        
        return func.HttpResponse(
            json.dumps({
                "response": result["output"],
                "execution_time": result["execution_time"],
                "status": "success"
            }),
            status_code=200,
            mimetype="application/json"
        )
        
    except Exception as e:
        return func.HttpResponse(
            json.dumps({"error": str(e)}),
            status_code=500,
            mimetype="application/json"
        )

Timer-Triggered Function

import azure.functions as func
import datetime
import requests
import os

def main(mytimer: func.TimerRequest) -> None:
    """Scheduled workflow execution"""
    
    utc_timestamp = datetime.datetime.utcnow().replace(
        tzinfo=datetime.timezone.utc
    ).isoformat()
    
    # Trigger daily report generation
    response = requests.post(
        "https://api.broxi.ai/v1/flows/daily-report/run",
        headers={
            "Authorization": f"Bearer {os.environ['BROXI_API_TOKEN']}",
            "Content-Type": "application/json"
        },
        json={
            "input": "Generate daily analytics report",
            "variables": {
                "date": utc_timestamp,
                "report_type": "daily_summary"
            }
        }
    )
    
    if response.status_code == 200:
        print(f"Daily report triggered at {utc_timestamp}")
    else:
        print(f"Failed to trigger report: {response.text}")

Azure Service Bus

Message queuing and event-driven architecture.

Service Bus Configuration

{
  "service_bus": {
    "connection_string": "${AZURE_SERVICE_BUS_CONNECTION_STRING}",
    "queue_name": "broxi-workflow-queue",
    "topic_name": "broxi-events",
    "subscription_name": "workflow-processor"
  }
}

Message Processing

from azure.servicebus import ServiceBusClient, ServiceBusMessage
import json

class BroxiServiceBusHandler:
    def __init__(self, connection_string):
        self.client = ServiceBusClient.from_connection_string(connection_string)
    
    def send_workflow_request(self, workflow_id, input_data, user_id):
        """Send workflow request to Service Bus queue"""
        
        message_body = {
            "workflow_id": workflow_id,
            "input": input_data,
            "user_id": user_id,
            "timestamp": datetime.utcnow().isoformat(),
            "source": "service_bus"
        }
        
        message = ServiceBusMessage(json.dumps(message_body))
        
        with self.client.get_queue_sender("broxi-workflow-queue") as sender:
            sender.send_messages(message)
    
    def process_workflow_queue(self):
        """Process messages from workflow queue"""
        
        with self.client.get_queue_receiver("broxi-workflow-queue") as receiver:
            for message in receiver:
                try:
                    # Parse message
                    data = json.loads(str(message))
                    
                    # Execute BroxiAI workflow
                    result = self.execute_broxi_workflow(
                        data["workflow_id"],
                        data["input"],
                        data["user_id"]
                    )
                    
                    # Complete message
                    receiver.complete_message(message)
                    
                    # Send result notification
                    self.send_result_notification(data["user_id"], result)
                    
                except Exception as e:
                    print(f"Error processing message: {e}")
                    receiver.abandon_message(message)

Advanced Azure Integrations

Azure Cognitive Services

Enhance BroxiAI workflows with additional AI capabilities.

Speech Services

{
  "speech_config": {
    "subscription_key": "${AZURE_SPEECH_KEY}",
    "service_region": "eastus",
    "speech_recognition_language": "en-US",
    "voice_name": "en-US-AriaNeural"
  }
}

Text Analytics

# Sentiment analysis and key phrase extraction
{
  "component": "AzureTextAnalytics",
  "config": {
    "endpoint": "https://myaccount.cognitiveservices.azure.com/",
    "subscription_key": "${AZURE_TEXT_ANALYTICS_KEY}",
    "features": [
      "sentiment_analysis",
      "key_phrase_extraction", 
      "entity_recognition",
      "language_detection"
    ]
  }
}

Computer Vision

{
  "computer_vision": {
    "endpoint": "https://myaccount.cognitiveservices.azure.com/",
    "subscription_key": "${AZURE_COMPUTER_VISION_KEY}",
    "features": [
      "ocr",
      "image_analysis",
      "face_detection",
      "object_detection"
    ]
  }
}

Azure Logic Apps

Workflow orchestration and integration.

Logic App Integration

{
  "logic_app_trigger": {
    "type": "HTTP",
    "inputs": {
      "method": "POST",
      "uri": "https://api.broxi.ai/v1/flows/approval-workflow/run",
      "headers": {
        "Authorization": "Bearer @{parameters('broxi_api_token')}",
        "Content-Type": "application/json"
      },
      "body": {
        "input": "@{triggerBody()['request_text']}",
        "variables": {
          "requester_id": "@{triggerBody()['user_id']}",
          "approval_level": "@{triggerBody()['level']}"
        }
      }
    }
  }
}

Conditional Workflow

{
  "condition": {
    "type": "If",
    "expression": {
      "and": [
        {
          "greater": [
            "@int(outputs('BroxiAI_Workflow')['body']['confidence_score'])",
            80
          ]
        }
      ]
    },
    "actions": {
      "auto_approve": {
        "type": "Http",
        "inputs": {
          "method": "POST",
          "uri": "https://approval-system.com/auto-approve",
          "body": "@outputs('BroxiAI_Workflow')['body']"
        }
      }
    },
    "else": {
      "actions": {
        "manual_review": {
          "type": "Http",
          "inputs": {
            "method": "POST",
            "uri": "https://approval-system.com/manual-review",
            "body": "@outputs('BroxiAI_Workflow')['body']"
          }
        }
      }
    }
  }
}

Azure Monitor and Application Insights

Comprehensive monitoring and observability.

Application Insights Configuration

{
  "application_insights": {
    "instrumentation_key": "${AZURE_APP_INSIGHTS_KEY}",
    "connection_string": "${AZURE_APP_INSIGHTS_CONNECTION_STRING}",
    "enable_live_metrics": true,
    "enable_adaptive_sampling": true,
    "sampling_percentage": 100
  }
}

Custom Telemetry

from applicationinsights import TelemetryClient
import time

class BroxiAzureMonitoring:
    def __init__(self, instrumentation_key):
        self.telemetry_client = TelemetryClient(instrumentation_key)
    
    def track_workflow_execution(self, workflow_id, execution_time, success, user_id):
        """Track workflow execution metrics"""
        
        # Track custom event
        self.telemetry_client.track_event(
            'WorkflowExecution',
            {
                'workflow_id': workflow_id,
                'user_id': user_id,
                'success': str(success)
            },
            {
                'execution_time': execution_time,
                'timestamp': time.time()
            }
        )
        
        # Track performance metric
        self.telemetry_client.track_metric(
            'WorkflowExecutionTime',
            execution_time,
            properties={'workflow_id': workflow_id}
        )
        
        # Track dependency
        self.telemetry_client.track_dependency(
            'BroxiAI',
            'ExecuteWorkflow',
            f'/workflows/{workflow_id}/run',
            start_time=time.time() - execution_time,
            duration=int(execution_time * 1000),  # milliseconds
            success=success
        )
        
        self.telemetry_client.flush()
    
    def track_workflow_error(self, workflow_id, error_message, user_id):
        """Track workflow errors"""
        
        self.telemetry_client.track_exception(
            Exception(error_message),
            properties={
                'workflow_id': workflow_id,
                'user_id': user_id,
                'error_type': 'WorkflowExecution'
            }
        )
        
        self.telemetry_client.flush()

Azure Monitor Alerts

Alert Rules:
  Workflow_Error_Rate:
    condition: "error_rate > 5%"
    time_window: "5 minutes"
    action_group: "broxi_alerts"
    severity: "Warning"
  
  High_Response_Time:
    condition: "avg_response_time > 10 seconds"
    time_window: "5 minutes"
    action_group: "performance_alerts"
    severity: "Error"
  
  API_Quota_Warning:
    condition: "api_calls > 80% of quota"
    time_window: "1 hour"
    action_group: "billing_alerts"
    severity: "Information"

Security and Compliance

Azure Active Directory Integration

Single Sign-On (SSO)

{
  "azure_ad_config": {
    "tenant_id": "${AZURE_TENANT_ID}",
    "client_id": "${AZURE_CLIENT_ID}",
    "client_secret": "${AZURE_CLIENT_SECRET}",
    "redirect_uri": "https://yourapp.com/auth/callback",
    "scopes": ["openid", "profile", "email", "User.Read"]
  }
}

Authentication Flow

from msal import ConfidentialClientApplication
import requests

class AzureADBroxiAuth:
    def __init__(self, tenant_id, client_id, client_secret):
        self.app = ConfidentialClientApplication(
            client_id,
            authority=f"https://login.microsoftonline.com/{tenant_id}",
            client_credential=client_secret
        )
    
    def get_token_for_user(self, username, password):
        """Get token using username/password (not recommended for production)"""
        
        result = self.app.acquire_token_by_username_password(
            username=username,
            password=password,
            scopes=["https://graph.microsoft.com/.default"]
        )
        
        if "access_token" in result:
            return result["access_token"]
        else:
            raise Exception(f"Authentication failed: {result.get('error_description')}")
    
    def get_token_for_app(self):
        """Get token for application (daemon scenario)"""
        
        result = self.app.acquire_token_for_client(
            scopes=["https://graph.microsoft.com/.default"]
        )
        
        if "access_token" in result:
            return result["access_token"]
        else:
            raise Exception(f"Authentication failed: {result.get('error_description')}")
    
    def call_broxi_with_azure_token(self, azure_token, workflow_id, input_data):
        """Call BroxiAI with Azure AD token"""
        
        # Exchange Azure AD token for BroxiAI token (implementation depends on your setup)
        broxi_token = self.exchange_token(azure_token)
        
        response = requests.post(
            f"https://api.broxi.ai/v1/flows/{workflow_id}/run",
            headers={
                "Authorization": f"Bearer {broxi_token}",
                "Content-Type": "application/json"
            },
            json={"input": input_data}
        )
        
        return response.json()

Azure Key Vault

Secure secret management for API keys and credentials.

Key Vault Configuration

from azure.keyvault.secrets import SecretClient
from azure.identity import DefaultAzureCredential

class AzureKeyVaultManager:
    def __init__(self, vault_url):
        credential = DefaultAzureCredential()
        self.client = SecretClient(vault_url=vault_url, credential=credential)
    
    def get_broxi_api_key(self):
        """Retrieve BroxiAI API key from Key Vault"""
        secret = self.client.get_secret("broxi-api-key")
        return secret.value
    
    def get_openai_api_key(self):
        """Retrieve OpenAI API key from Key Vault"""
        secret = self.client.get_secret("openai-api-key")
        return secret.value
    
    def rotate_api_key(self, secret_name, new_key):
        """Rotate API key in Key Vault"""
        self.client.set_secret(secret_name, new_key)
        return True

# Usage in BroxiAI configuration
kv_manager = AzureKeyVaultManager("https://myvault.vault.azure.net/")

broxi_client = BroxiClient(
    api_token=kv_manager.get_broxi_api_key()
)

Managed Identity Integration

Azure Resource Configuration:
  App Service:
    identity:
      type: "SystemAssigned"
    app_settings:
      AZURE_CLIENT_ID: "system_assigned"
      KEY_VAULT_URL: "https://myvault.vault.azure.net/"
  
  Key Vault Access Policy:
    object_id: "app_service_principal_id"
    permissions:
      secrets: ["get", "list"]
      keys: ["get", "list"]
      certificates: ["get", "list"]

Compliance and Governance

Azure Policy Integration

{
  "azure_policy": {
    "required_tags": {
      "Environment": "Production",
      "Application": "BroxiAI",
      "Owner": "AI-Team",
      "CostCenter": "Engineering"
    },
    "allowed_regions": [
      "East US 2",
      "West Europe",
      "Southeast Asia"
    ],
    "encryption_required": true,
    "backup_required": true
  }
}

Compliance Monitoring

from azure.mgmt.policyinsights import PolicyInsightsClient
from azure.identity import DefaultAzureCredential

class ComplianceMonitor:
    def __init__(self, subscription_id):
        credential = DefaultAzureCredential()
        self.client = PolicyInsightsClient(credential, subscription_id)
    
    def check_broxi_compliance(self):
        """Check compliance status for BroxiAI resources"""
        
        # Get policy states
        policy_states = self.client.policy_states.list_query_results_for_subscription(
            "latest",
            subscription_id=self.subscription_id,
            filter="resourceType eq 'Microsoft.Web/sites' and contains(resourceId, 'broxi')"
        )
        
        compliance_status = {
            "compliant": 0,
            "non_compliant": 0,
            "violations": []
        }
        
        for state in policy_states.value:
            if state.compliance_state == "Compliant":
                compliance_status["compliant"] += 1
            else:
                compliance_status["non_compliant"] += 1
                compliance_status["violations"].append({
                    "resource": state.resource_id,
                    "policy": state.policy_definition_name,
                    "reason": state.compliance_reason_code
                })
        
        return compliance_status

Deployment Patterns

Azure Resource Manager (ARM) Templates

Complete BroxiAI Infrastructure

{
  "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
  "contentVersion": "1.0.0.0",
  "parameters": {
    "environmentName": {
      "type": "string",
      "defaultValue": "production"
    },
    "broxiApiToken": {
      "type": "securestring"
    }
  },
  "variables": {
    "resourcePrefix": "[concat('broxi-', parameters('environmentName'))]"
  },
  "resources": [
    {
      "type": "Microsoft.Web/serverfarms",
      "apiVersion": "2021-02-01",
      "name": "[concat(variables('resourcePrefix'), '-plan')]",
      "location": "[resourceGroup().location]",
      "sku": {
        "name": "P1v3",
        "tier": "PremiumV3"
      },
      "properties": {
        "reserved": true
      }
    },
    {
      "type": "Microsoft.Web/sites",
      "apiVersion": "2021-02-01",
      "name": "[concat(variables('resourcePrefix'), '-app')]",
      "location": "[resourceGroup().location]",
      "dependsOn": [
        "[resourceId('Microsoft.Web/serverfarms', concat(variables('resourcePrefix'), '-plan'))]"
      ],
      "properties": {
        "serverFarmId": "[resourceId('Microsoft.Web/serverfarms', concat(variables('resourcePrefix'), '-plan'))]",
        "siteConfig": {
          "linuxFxVersion": "PYTHON|3.9",
          "appSettings": [
            {
              "name": "BROXI_API_TOKEN",
              "value": "[parameters('broxiApiToken')]"
            },
            {
              "name": "AZURE_ENVIRONMENT",
              "value": "[parameters('environmentName')]"
            }
          ]
        }
      }
    },
    {
      "type": "Microsoft.Storage/storageAccounts",
      "apiVersion": "2021-09-01",
      "name": "[concat(variables('resourcePrefix'), 'storage')]",
      "location": "[resourceGroup().location]",
      "sku": {
        "name": "Standard_LRS"
      },
      "kind": "StorageV2",
      "properties": {
        "encryption": {
          "services": {
            "blob": {
              "enabled": true
            }
          },
          "keySource": "Microsoft.Storage"
        }
      }
    }
  ]
}

Azure DevOps Integration

Pipeline Configuration (azure-pipelines.yml)

trigger:
  branches:
    include:
    - main
    - develop

variables:
  azureServiceConnection: 'azure-subscription'
  resourceGroupName: 'broxi-production-rg'
  
stages:
- stage: Build
  jobs:
  - job: BuildApplication
    pool:
      vmImage: 'ubuntu-latest'
    
    steps:
    - task: UsePythonVersion@0
      inputs:
        versionSpec: '3.9'
    
    - script: |
        pip install -r requirements.txt
        pip install broxi-ai
      displayName: 'Install dependencies'
    
    - script: |
        python -m pytest tests/ --junitxml=test-results.xml
      displayName: 'Run tests'
    
    - task: PublishTestResults@2
      inputs:
        testResultsFiles: 'test-results.xml'
        testRunTitle: 'Python Tests'

- stage: Deploy
  dependsOn: Build
  condition: and(succeeded(), eq(variables['Build.SourceBranch'], 'refs/heads/main'))
  
  jobs:
  - deployment: DeployToAzure
    environment: 'production'
    pool:
      vmImage: 'ubuntu-latest'
    
    strategy:
      runOnce:
        deploy:
          steps:
          - task: AzureResourceManagerTemplateDeployment@3
            inputs:
              deploymentScope: 'Resource Group'
              azureResourceManagerConnection: '$(azureServiceConnection)'
              resourceGroupName: '$(resourceGroupName)'
              location: 'East US 2'
              templateLocation: 'Linked artifact'
              csmFile: 'infrastructure/arm-template.json'
              overrideParameters: |
                -environmentName "production"
                -broxiApiToken "$(BROXI_API_TOKEN)"
          
          - task: AzureWebApp@1
            inputs:
              azureSubscription: '$(azureServiceConnection)'
              appType: 'webAppLinux'
              appName: 'broxi-production-app'
              package: '$(Pipeline.Workspace)/drop'

Cost Optimization

Resource Management

Cost Optimization Strategies

Azure Cost Optimization:
  Compute:
    - Use Azure Functions for sporadic workloads
    - Implement auto-scaling for App Services
    - Use spot instances for batch processing
    - Right-size VM instances

  Storage:
    - Use appropriate storage tiers (Hot/Cool/Archive)
    - Implement lifecycle management policies
    - Enable data deduplication
    - Use managed disks efficiently

  Networking:
    - Optimize data transfer costs
    - Use Azure CDN for static content
    - Implement traffic routing optimization
    - Monitor bandwidth usage

  Monitoring:
    - Set up cost alerts and budgets
    - Use Azure Cost Management
    - Implement resource tagging
    - Regular cost reviews

Azure Cost Management Integration

from azure.mgmt.consumption import ConsumptionManagementClient
from azure.identity import DefaultAzureCredential

class AzureCostMonitor:
    def __init__(self, subscription_id):
        credential = DefaultAzureCredential()
        self.client = ConsumptionManagementClient(credential, subscription_id)
        self.subscription_id = subscription_id
    
    def get_broxi_costs(self, start_date, end_date):
        """Get costs for BroxiAI resources"""
        
        scope = f"/subscriptions/{self.subscription_id}"
        
        usage_details = self.client.usage_details.list(
            scope=scope,
            filter=f"properties/usageStart ge '{start_date}' and properties/usageEnd le '{end_date}' and contains(properties/resourceName, 'broxi')"
        )
        
        total_cost = 0
        cost_breakdown = {}
        
        for detail in usage_details:
            service_name = detail.properties.consumed_service
            cost = detail.properties.cost
            
            total_cost += cost
            
            if service_name not in cost_breakdown:
                cost_breakdown[service_name] = 0
            cost_breakdown[service_name] += cost
        
        return {
            "total_cost": total_cost,
            "breakdown": cost_breakdown,
            "period": f"{start_date} to {end_date}"
        }

Monitoring and Analytics

Azure Monitor Dashboard

Custom Dashboard Configuration

{
  "dashboard_config": {
    "name": "BroxiAI Production Monitoring",
    "tiles": [
      {
        "type": "metric",
        "title": "Workflow Execution Rate",
        "metric": "custom/WorkflowExecutions",
        "aggregation": "count",
        "time_range": "PT1H"
      },
      {
        "type": "metric", 
        "title": "Average Response Time",
        "metric": "requests/duration",
        "aggregation": "average",
        "time_range": "PT1H"
      },
      {
        "type": "log",
        "title": "Recent Errors",
        "query": "traces | where severityLevel >= 3 | top 10 by timestamp desc",
        "time_range": "PT1H"
      }
    ]
  }
}

Best Practices

Security Best Practices

Network Security

• Use Azure Virtual Networks for isolation

• Implement Network Security Groups (NSGs)

• Use Azure Firewall for advanced protection

• Enable DDoS protection

• Use private endpoints for PaaS services

Identity and Access

• Implement Azure AD integration

• Use Managed Identities where possible

• Follow principle of least privilege

• Enable Multi-Factor Authentication

• Regular access reviews

Data Protection

• Enable encryption at rest and in transit

• Use customer-managed keys when required

• Implement data loss prevention (DLP)

• Regular security assessments

• Compliance monitoring

Performance Best Practices

Application Optimization

• Use Azure CDN for global content delivery

• Implement caching strategies

• Optimize database queries

• Use connection pooling

• Monitor performance metrics

Scalability Planning

• Design for auto-scaling

• Use Azure Load Balancer

• Implement circuit breakers

• Plan for traffic spikes

• Regular capacity planning

Next Steps

After Azure integration:

1. Security Review: Conduct security assessment

2. Performance Testing: Load test the integration

3. Cost Optimization: Monitor and optimize costs

4. Compliance Validation: Ensure regulatory compliance

5. Disaster Recovery: Test DR procedures

Related Guides

• AWS Integration: Multi-cloud strategies

• Security: Security best practices

• Monitoring: Comprehensive monitoring

---

Azure integration provides enterprise-grade security, compliance, and scalability for BroxiAI applications. Follow Microsoft's Well-Architected Framework for optimal results.