Cloud migration between platforms can be a daunting task, especially when you’re moving from one major provider to another. Recently, I discovered how Claude, Anthropic’s AI assistant, can significantly streamline the process of porting AWS applications to Microsoft Azure by helping identify equivalent services and providing migration guidance. In this post, I’ll focus on my experience migrating four core AWS services, S3, Lambda, EC2, and RDS, to their Azure counterparts.
The Challenge of Cross-Cloud Migration
Moving applications from AWS to Azure isn’t just about finding service equivalents, it’s about understanding the architectural differences, naming conventions, and feature sets between two complex cloud ecosystems. When I started my migration project, I was overwhelmed by the prospect of researching equivalent services for every AWS component in my application stack.
How Claude Made the Transition Surprisingly Easy
What I expected to be weeks of research and documentation diving became a series of straightforward conversations with Claude. The AI’s ability to understand cloud architectures and instantly provide Azure equivalents, along with migration considerations, transformed my approach entirely.
Core Service Migrations: My Experience
- Amazon S3 to Azure Blob Storage
The Challenge: My application relied heavily on S3 for object storage, with complex bucket policies and lifecycle management rules.
Claude’s Guidance: When I asked Claude about migrating S3 to Azure, it immediately identified Azure Blob Storage as the equivalent and provided a detailed comparison:
- Storage tiers: S3’s Standard/IA/Glacier mapped to Azure’s Hot/Cool/Archive
- Access patterns: Explained how S3 bucket policies translate to Azure’s role-based access control
- SDK changes: Provided specific code examples for transitioning from boto3 to Azure SDK
Migration Ease: 9/10 Claude made this transition incredibly smooth by providing direct code translations and explaining the conceptual differences clearly.
2. AWS Lambda to Azure Functions
The Challenge: I had multiple Lambda functions handling API requests, scheduled tasks, and event-driven processing.
Claude’s Guidance: Claude explained that Azure Functions was the direct equivalent but highlighted key differences:
- Runtime environments: How Python runtimes compared between platforms
- Trigger mechanisms: Mapping AWS API Gateway triggers to Azure HTTP triggers
- Deployment models: Differences between AWS SAM and Azure Functions deployment
Migration Ease: 8/10 The concepts were very similar, but Claude helped me understand Azure-specific configuration nuances that would have taken hours to discover independently.
3. Amazon EC2 to Azure Virtual Machines
The Challenge: My application used EC2 instances for compute-intensive tasks and web hosting, with auto-scaling groups and load balancers.
Claude’s Guidance: Claude provided a comprehensive mapping:
- Instance types: Helped match EC2 instance families to Azure VM sizes
- Networking: Explained how VPCs translate to Azure Virtual Networks
- Scaling: Mapped Auto Scaling Groups to Azure VM Scale Sets
- Load balancing: Compared ELB to Azure Load Balancer
Migration Ease: 7/10 This was more complex due to networking considerations, but Claude’s step-by-step explanations made the architecture translation manageable.
Key Equivalents Claude Identified:
- EC2 Instances → Azure Virtual Machines
- Auto Scaling Groups → VM Scale Sets
- Elastic Load Balancer → Azure Load Balancer
- VPC → Virtual Network (VNet)
- Security Groups → Network Security Groups
4. Amazon RDS to Azure Database Services
The Challenge: My application used RDS for PostgreSQL with read replicas, automated backups, and specific performance configurations.
Claude’s Guidance: Claude presented multiple Azure options and helped me choose:
- Azure Database for PostgreSQL: Direct equivalent for managed PostgreSQL
- Azure SQL Database: If I wanted to transition to SQL Server
- Azure Cosmos DB: For NoSQL migration scenarios
For my PostgreSQL needs, Claude explained:
- How read replicas work in Azure
- Backup and recovery differences
- Performance tuning options
- Connection string changes needed in application code
Migration Ease: 8/10 Claude’s detailed comparison of database features and migration strategies made this surprisingly straightforward.
The Conversation-Driven Migration Process
What made using Claude so effective was the natural, conversation-driven approach:
Initial Architecture Review
Me: “I have an AWS application using S3, Lambda, EC2, and RDS. What are the Azure equivalents and what should I be concerned about?”
Claude: Provided a comprehensive service mapping table and highlighted key considerations for each service transition.
Deep-Dive Sessions
For each service, I could ask specific questions:
- “What are the pricing differences between S3 and Azure Blob Storage?”
- “How do I migrate my Lambda environment variables to Azure Functions?”
- “What’s the equivalent of AWS RDS parameter groups in Azure?”
Implementation Guidance
Claude provided actual code examples and configuration snippets, making the transition from research to implementation seamless.
Key Benefits I Experienced
Massive Time Savings
What traditionally takes weeks of documentation research and trial-and-error became a few hours of focused conversation with Claude. The AI’s ability to provide immediate, contextual answers accelerated my migration timeline by at least 80%.
Reduced Migration Risk
Claude’s explanations of service differences helped me avoid common pitfalls. For example, understanding that Azure Functions has different timeout limits than Lambda prevented runtime issues before they occurred.
Cost Optimisation Insights
By asking Claude about pricing models and service tiers, I could make cost-effective decisions during migration planning, potentially saving thousands in cloud costs.
Confidence in Decision-Making
Having an AI assistant that could explain the “why” behind service recommendations gave me confidence in my migration choices.
Best Practices for Using Claude in Service Migration
1. Start with Service Mapping
Begin each migration conversation by asking Claude to map your current AWS services to Azure equivalents with explanations of key differences.
2. Ask About Gotchas
Always inquire about potential issues: “What are the key differences I should watch out for when moving from Lambda to Azure Functions?”
3. Request Code Examples
Ask for specific code migration examples. Claude excels at showing before/after implementations.
4. Validate Pricing and Limits
Have Claude explain pricing models and service limits to avoid surprises during migration.
Conclusion
The transition from AWS to Azure using Claude as a guide exceeded my expectations. The AI’s deep understanding of both cloud platforms, combined with its ability to provide practical, actionable guidance, made the migration process not just possible but genuinely straightforward.
For the four core services I migrated (S3 to Blob Storage, Lambda to Functions, EC2 to VMs, and RDS to Azure Database) Claude provided the expertise and guidance that would have taken weeks to acquire through traditional research methods.
If you’re considering a similar migration, I highly recommend leveraging Claude’s capabilities. The time savings, reduced risk, and increased confidence in your migration decisions make it an invaluable tool in your cloud migration toolkit.
This post was written by Roshan Manic, one of our Consultants & DevOps Engineers at Cloud Elemental, as part of our ongoing series exploring how AI is transforming the way we build, ship, and scale our cloud solutions.
At Cloud Elemental, we’re committed to embracing tools like Claude not just for efficiency, but to raise the bar on quality, security, and developer experience across our service offerings.
If you’re exploring how to streamline your own API development workflows, or looking for a partner to help build cloud-native, AI-enabled solutions, we’d love to hear from you. Get in touch with the team at Cloud Elemental to start a conversation about how we can support your next project.
