AWS has announced general availability of application code conversion from Azure SDK to AWS SDK in AWS Transform. The capability targets cloud-native .NET applications and is intended to accelerate a specific part of migration: identifying Azure service calls and converting them to corresponding AWS service code.
AWS says the feature can convert Azure Functions to AWS Lambda, Azure Blob Storage to Amazon S3, and Azure Queue Storage to Amazon SQS. Conversion from Azure Cosmos DB to Amazon DynamoDB is available in preview. The service first identifies Azure SDK usage, presents conversion options in an assessment report and modernization plan, and then validates that the selected changes are included in the transformed code. [1]
This is a meaningful development for organizations evaluating a move between cloud platforms, but it should not be mistaken for an automated portability guarantee. SDK conversion changes application integration points. It does not, by itself, reproduce the surrounding architecture, security model, data behavior, service limits, operating procedures or commercial assumptions.
What the tool actually does
The central benefit is pattern recognition. A team that has hundreds of Azure SDK calls in a .NET codebase may spend significant time locating those calls, mapping them to target services and making repetitive edits. AWS Transform is designed to automate part of that work while keeping an assessment and modernization plan in the loop.
The service’s examples are deliberately concrete. An Azure Functions application that uses Cosmos DB may be mapped toward Lambda and DynamoDB. Blob Storage calls may be mapped toward S3. Queue Storage calls may be mapped toward SQS. These services can occupy similar roles, but they are not interchangeable implementations. Their consistency models, event semantics, pricing, identity controls, monitoring and failure behavior differ.
The review stage therefore matters. Teams can accept the options presented by AWS Transform or adjust their preferences before modernization runs. That creates a governance point where architects should record why a target service was chosen and which assumptions must be tested.
Migration acceleration is not migration completion
The most important limitation is the boundary between code translation and system migration. A converted SDK call may compile and pass a narrow test while the application still fails under production conditions. Storage permissions may be incomplete. Queue retries may behave differently. Event ordering may change. An Azure-specific identity flow may not map cleanly to AWS IAM. Operational dashboards may no longer expose the same signals.
Data migration is a separate workstream. Teams must determine how data will be copied, synchronized and validated, and how the application will behave during a cutover. They also need a rollback plan. A code-conversion tool cannot answer whether the business can tolerate a period of read-only operation or how a transaction should be recovered if the two environments diverge.
Costs also require independent analysis. A target service may have a different billing unit, minimum charge, network path or support requirement. Savings claims based only on equivalent service names are unreliable. A migration business case should include engineering effort, data-transfer costs, dual-running time, testing, observability and training.
Why .NET teams may pay attention
The feature is most relevant to organizations with Azure-native .NET applications that have already decided to evaluate AWS. It may also help a company create a more credible multicloud option without rewriting every application from scratch. That does not mean that every application should be made portable. Portability has a cost, and some teams may prefer to deepen their investment in a single platform.
The decision should begin with business requirements rather than a cloud preference. A customer may need a second platform for regional capacity, acquisition integration, procurement strategy or a specific service capability. Another customer may discover that the better answer is modernization within Azure rather than a move away from it.
AWS Transform adds value when it is used as a controlled accelerator. It is less useful when it is treated as a substitute for architecture review.
A disciplined pilot approach
A migration team should start with a representative but non-critical .NET service. Record its Azure dependencies, test coverage, data flows, identity model and production SLOs. Run the assessment and save the modernization plan as an auditable artifact.
Next, review every proposed service mapping. Identify where the target service is only functionally similar rather than behaviorally equivalent. Build tests for data integrity, authentication, retries, timeouts, queue delivery, failure recovery and monitoring. Run the application against test data in the target environment and compare observable behavior rather than only build results.
The final pilot should include deployment automation, secrets handling, logging, alerting and rollback. If those controls are missing, the project has converted code but not produced an operable service.
AWS’s Azure-to-AWS conversion capability is a useful sign of how cloud migration tools are becoming more specialized. The practical result for technology buyers is not a one-click migration. It is a potentially faster first pass that still requires independent technical, commercial and governance validation. Guava Systems can help organizations compare cloud migration options and identify the work that automation does not remove.
References
[1]: https://aws.amazon.com/about-aws/whats-new/2026/10/aws-transform-azure-to-aws-code-conversion/ “AWS Transform announces Azure-to-AWS cloud-native code conversion” [2]: https://docs.aws.amazon.com/transform/latest/userguide/dotnet-azure-to-aws.html “Convert cloud-native code to AWS — AWS Transform User Guide” [3]: https://aws.amazon.com/transform/ “AWS Transform — AWS”
