Migrate OpenAI Workflows from Azure → Multi-Cloud Setup
Systematically migrate your existing Azure OpenAI integrations to a multi-cloud architecture for better reliability and cost optimization.
Workflow Steps
Notion
Inventory existing integrations
Create a comprehensive database in Notion listing all current Azure OpenAI API calls, endpoints, authentication methods, and dependent applications
Postman
Test API compatibility
Use Postman collections to test your existing API calls against AWS Bedrock and GCP's AI services, documenting any required parameter changes
Terraform
Provision multi-cloud resources
Create Terraform configurations for equivalent OpenAI resources on AWS and GCP, including API gateways, authentication, and monitoring setup
GitHub Actions
Deploy with blue-green strategy
Set up CI/CD pipeline that deploys to new cloud environments while keeping Azure as fallback, allowing gradual traffic migration
DataDog
Monitor performance across clouds
Configure unified monitoring across all three cloud providers to track latency, error rates, and costs in a single dashboard
PagerDuty
Alert on failover events
Set up intelligent alerting that automatically fails over to backup cloud providers when primary service degrades, with team notifications
Workflow Flow
Step 1
Notion
Inventory existing integrations
Step 2
Postman
Test API compatibility
Step 3
Terraform
Provision multi-cloud resources
Step 4
GitHub Actions
Deploy with blue-green strategy
Step 5
DataDog
Monitor performance across clouds
Step 6
PagerDuty
Alert on failover events
Why This Works
This systematic migration approach minimizes risk while maximizing the new multi-cloud flexibility, ensuring you can switch providers seamlessly if pricing or performance changes.
Best For
Engineering teams reducing dependency on single cloud provider for AI services
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