Every enterprise with a legacy monolith knows the symptoms. A single bug takes down the entire application, not just the broken feature. Releases happen once a quarter because nobody wants to risk touching the whole system. Scaling means scaling everything on one Amazon EC2 fleet, even the one component that actually needs more capacity.
The instinct is to rewrite. The better move is to modernize in place, incrementally, using AWS's container, serverless, and data migration services to peel functionality away from the monolith without ever taking the system offline. This is what "monolith to microservices" actually means on AWS, and it looks nothing like the diagrams in most vendor whitepapers.
At SUDO Consultants, an AWS Premier Tier Partner working with teams across the UAE, Saudi Arabia, and the wider MENA region, we help organizations modernize legacy monoliths into microservices on a solid AWS cost-optimization and architecture foundation. The roadmap below comes from that hands-on work.
The Problem With Monoliths Isn't the Code. It's the Coupling.
A monolith isn't a technical failure. It was often the right choice years ago, when the team was small and the product was simple, running as a single deployable unit on a fixed set of servers. The problem shows up later, when the business grows faster than the architecture can support.
Three symptoms tend to show up together:
• Deployment risk: every release touches the entire codebase, so every release carries the risk of the entire codebase.
• Scaling inefficiency: if the checkout module needs more compute during a sale, the entire monolith scales with it on the same Auto Scaling group, driving up cost for capacity most of the system doesn't need.
• Team bottlenecks: multiple engineering teams working in one codebase and one deployment pipeline creates merge conflicts, release queues, and slower delivery cycles as headcount grows.
None of these are solved by rewriting the application from scratch. They are solved by decomposing it correctly, using the right AWS services in the right order.
Why the Big-Bang Rewrite Is the Most Common Mistake
The most tempting path is also the riskiest: freeze the legacy system, spend twelve months building a clean microservices architecture from zero, then cut over in one release.
This rarely works. Business requirements do not pause for a year while engineering rebuilds. The legacy system keeps evolving with bug fixes and urgent features, widening the gap between what the new system was designed for and what the business now needs.
The proven alternative is the strangler fig pattern, implemented on AWS using an Application Load Balancer (ALB) or Amazon API Gateway as the routing layer in front of both systems. The new microservices architecture is built around the edges of the monolith; traffic is routed incrementally to the new services, and the legacy system shrinks over time until there is nothing left to retire. The monolith keeps running on its existing infrastructure the entire time. The business never stops.
The Modernization Roadmap
The services below aren't listed up front. Each one shows up exactly where it earns its place in the sequence, since that sequencing, not the tool selection, is what makes or breaks a modernization program.
Phase 0: Runtime Readiness
Decomposition assumes the monolith can still be built and deployed. Often it can’t. A Java 8 application on Spring Boot 2.x with an AWS SDK v1 dependency tree cannot have a service cleanly extracted from it, because the extracted service inherits a runtime that is already out of support. This phase is unglamorous, and it is where modernization programs quietly stall.
AWS Transform custom handles this class of work as managed transformations: language version upgrades, Spring Boot 2.x to 3.x, SDK v1 to v2, and Log4j to SLF4J, applied across repositories rather than one at a time. The agent accumulates knowledge items from each execution, so the second hundred repositories cost less effort than the first ten. Run this to completion before any bounded context is extracted, not in parallel with it.
Phase 1: Domain Decomposition
Before provisioning any AWS resource, map the monolith into bounded contexts, the natural seams where one business capability ends and another begins (orders, payments, inventory, customer profile). This is a modeling exercise, not an infrastructure exercise, and it is the single most under-invested step in most modernization programs.
Getting this wrong means standing up ECS services or Lambda functions that mirror the monolith’s internal code structure instead of the business’s actual domains, which recreates the same coupling problems in a new, more expensive form.
Phase 2: Strangler Fig Routing
Once a bounded context is identified, extract it as an independent service running on Amazon ECS, Amazon EKS, or AWS Fargate, and place an Application Load Balancer (or Amazon CloudFront in front of the ALB for web-facing traffic) ahead of both the legacy monolith and the new service. Amazon API Gateway can front the new service directly if it’s exposed as a discrete API.
Getting the extracted service into a deployable container is work in its own right. AWS Transform can containerize source code directly from GitHub, Bitbucket, or GitLab, generating Dockerfiles and images with CVE scanning, then emitting deployment-ready Terraform and Helm charts for the ECS or EKS target. It handles monorepos, which is the usual shape of a monolith repository, and private dependency resolution through CodeArtifact.
Traffic is routed incrementally using ALB path-based or weighted target group routing: a percentage of requests, or specific endpoints, go to the new service while everything else continues through the monolith unchanged.
This is what makes the migration reversible. If something breaks, shifting the ALB weighting back to the legacy target group restores the original path immediately, with no user-facing disruption.
Phase 3: Data Decoupling
This is the phase most teams get wrong, and it deserves its own warning.
Splitting the application into ECS or Lambda-based services while leaving one shared Amazon RDS or on-premises database behind them does not create microservices. It creates a distributed monolith, the operational complexity of microservices, with all of the coupling problems of a monolith still intact.
The fix is a database-per-service model, implemented using AWS DMS to replicate data with minimal downtime as new services get their own data stores:
- Use AWS Schema Conversion Tool (SCT) to assess schema compatibility if migrating between engines (for example, a legacy SQL Server schema moving to Aurora PostgreSQL-compatible).
- Stand up the target data store: Amazon Aurora for services needing relational consistency, Amazon DynamoDB for services needing high-scale key-value or document access.
- Configure a DMS replication task for ongoing change data capture (CDC), keeping the new data store in sync with the legacy database during the transition.
- Cut the new service over to reading and writing its own data store once replication lag and validation checks confirm consistency.
- Decommission the shared table’s usage by that service, repeating for the next bounded context.
Phase 4: Asynchronous Communication
Early in decomposition, new services often call each other directly and synchronously through API Gateway, which just relocates the monolith’s tight coupling into network calls. As the architecture matures, direct calls are replaced with asynchronous, event-driven communication using Amazon SQS for point-to-point decoupling, Amazon SNS for fan-out notification patterns, or Amazon EventBridge for a broader event bus across services and, where relevant, third-party SaaS integrations.
This is also where service discovery and traffic management are formalized using Amazon VPC Lattice or AWS App Mesh, giving the architecture consistent, observable service-to-service traffic instead of ad hoc API calls scattered across teams.
Phase 5: Observability and Delivery Maturity
A system with fifteen services is only manageable if it is observable. AWS X-Ray provides distributed tracing across service boundaries, so a slow checkout flow can be traced to the specific service causing the delay instead of triggering a search across the entire system. Amazon CloudWatch unifies logs, metrics, and alarms across the legacy monolith and the new services during the period when both are running side by side.
On the delivery side, each new service should be independently deployable through its own AWS CodePipeline, using AWS CodeBuild for build and test stages. This is what actually delivers the outcome the whole roadmap is working toward: teams shipping independently, without waiting on a shared release train tied to the monolith’s deployment schedule.
MENA-Specific Considerations
For banks, insurers, and government-adjacent organizations across the UAE and Saudi Arabia, modernization has to happen without breaking data residency or compliance posture along the way.
During the transition period, legacy and new services often run in parallel, sometimes for a year or more. Both the legacy environment and the new ECS, EKS, or Lambda-based services need to remain within the approved compliant region for the duration of the migration, not just at the finish line. AWS Config rules can be used to continuously flag any resource, including DMS replication instances, that gets provisioned outside the approved region.
Teams working under SAMA or NESA frameworks should treat the strangler fig transition period as a compliance checkpoint in its own right: the hybrid state, with DMS replicating data between legacy and new systems, is where sovereignty gaps are most likely to appear.
Cost Considerations
A few cost patterns are worth planning for before the migration starts:
• Running two environments in parallel is temporary, not permanent. Budget for the overlap period explicitly rather than treating it as a surprise line item; this is typically the highest-cost phase of the entire program.
• Fargate reduces operational cost for smaller services where the overhead of managing EC2 instances for a handful of containers isn’t justified, but at higher, steady-state scale, ECS or EKS on EC2 with Savings Plans is often more cost-efficient.
• DMS replication instances should be sized for the migration window, not left running indefinitely. Decommission them once cutover is validated and stable.
• AWS Compute Optimizer is worth running against the new services once they stabilize, since initial sizing during migration is often conservative and leaves rightsizing opportunity on the table.
What We’ve Seen in the Field
In one modernization program for a regional financial services client, the team’s first instinct was to decompose the order management and payments modules simultaneously on Amazon ECS, since both were high priority. The bigger constraint turned out to be the database: both modules shared several core tables in the same Amazon RDS instance, and splitting the services without first resolving the data layer through DMS would have created exactly the distributed monolith problem described above.
Resequencing the work to decouple the data layer first, then extract the services, added a few weeks up front but avoided months of rework later. The lesson holds broadly: the roadmap’s value is in the sequence, not just the AWS services chosen.
Practical Next Steps
For teams evaluating where to start:
• Run AWS Transform custom against the legacy codebase first to clear runtime and dependency debt, before extracting a single service. Skipping this step is the most common reason modernization programs stall before Phase 1 even begins.
• Map bounded contexts before provisioning any AWS resource. This is a business modeling exercise that should occur before any service is extracted.
• Start with the highest-change, lowest-risk module first, using Fargate or Lambda to keep the initial footprint small while the pattern is validated.
• Resolve the data layer with AWS DMS before splitting services, not after. This is the single most common sequencing mistake.
• Instrument AWS X-Ray and CloudWatch from day one of the transition, not after the first outage makes it necessary.
• Use AWS Config to enforce residency and compliance controls on the hybrid state itself, since the transition period, not just the end state, is where compliance gaps typically appear.
Where This Goes Next
Monolith-to-microservices modernization is not a project with a finish line so much as a capability an engineering organization builds on AWS. The enterprises that get the most value from it are the ones that treat the roadmap and the underlying service choices as a repeatable pattern, applying it to the next legacy system once the first one is done, rather than a one-time initiative.
What does your team’s biggest bottleneck look like today: deployment risk, scaling cost, or team velocity? The answer usually points to which AWS service should come first.
How SUDO Consultants can help
As an AWS Premier Tier Partner, SUDO Consultants helps organizations put the modernization foundation in place, runtime and dependency remediation, bounded-context modeling, strangler fig routing, and data decoupling with AWS DMS that makes a monolith-to-microservices program succeed, and integrates it into your existing engineering and delivery workflows. If your team wants to modernize on a solid footing, we can help you get there.
Through our AWS Cloud Migration & Modernization and DevOps as a Service engagements, we plan and run the full monolith-to-microservices roadmap for you and embed modern, independently deployable services into your day-to-day cloud operations across the UAE, Saudi Arabia, and the wider MENA region.
Get in touch at sudoconsultants.com or reach@sudoconsultants.com.
Frequently asked questions
What does “monolith to microservices” mean on AWS?
It means decomposing a single deployable application into independently deployable services, incrementally, using AWS container, serverless, and data-migration services, so functionality is peeled away from the monolith without taking the system offline.
What is the strangler fig pattern?
A migration approach where new microservices are built around the edges of the monolith and traffic is routed to them incrementally through an Application Load Balancer or Amazon API Gateway, until the legacy system shrinks to nothing. It keeps the migration reversible and the business online throughout.
How do you avoid creating a distributed monolith?
Give each service its own data store (database-per-service) instead of sharing one database. AWS DMS replicates data with minimal downtime while each service cuts over to its own store, so you get real service isolation rather than microservices bolted onto a shared database.
In what order should you modernize a monolith on AWS?
Clear runtime and dependency debt first, map bounded contexts, apply strangler fig routing, decouple the data layer, move to asynchronous communication, then add observability and independent delivery. The sequence, not the tool choice, is what makes or breaks the program.
Does this work for regulated organizations in the UAE and Saudi Arabia?
Yes, provided legacy and new services stay within the approved compliant region for the entire transition, not just at the end. AWS Config can flag any resource provisioned outside the approved region, and teams under SAMA or NESA frameworks should treat the hybrid state as a compliance checkpoint.
Keywords: monolith to microservices on AWS, legacy application modernization AWS, strangler fig pattern, database-per-service, AWS DMS migration, microservices decomposition MENA
