Most microservices migrations that go badly went wrong at the boundaries. Services split along the wrong lines end up making constant calls to each other, which produces all the operational overhead of a distributed system with none of the independence that justifies it. TechEsperto migrates monoliths incrementally, extracting services along real business boundaries while the existing application keeps running. We will also tell you when your monolith does not need decomposing, which is more often than the industry admits.
legacy software modernization onto the roadmap.
We use incremental extraction rather than a parallel rewrite, because rewrites that run alongside an evolving system rarely catch up. The monolith keeps serving traffic while services are extracted one at a time, with routing shifted gradually and reversibly. No phase requires a big-bang cutover.
We map the business domains inside the existing system and define service boundaries around them, because boundaries drawn along technical layers produce chatty, tightly coupled services.
Services prioritized by value and risk, usually starting with components that are relatively independent and painful in their current form, to prove the pattern early.
Deciding which service owns which data and how consistency is maintained, since shared databases are the most common reason decomposition fails to deliver independence.
Each service built and deployed with traffic shifted gradually behind a routing layer, so any problem is a routing change away from being reversed.
Deployment pipelines, service discovery, distributed tracing, and monitoring established before extraction accelerates, since distributed systems are undebuggable without them.
The remaining monolith shrinks as functionality moves, and we assess honestly whether the final components are worth extracting or better left in place.
Decomposition is as much platform work as application work. The services are visible, but the infrastructure around them determines whether teams can actually operate what they own. The scope below reflects what a realistic migration engagement covers.
Extraction of identified components into independently deployable services with clear API contracts and defined ownership of their data.
A routing and gateway layer allowing traffic to move between monolith and services gradually, which is what makes incremental migration reversible.
Splitting shared databases, defining ownership, and implementing the events or synchronization needed where several services depend on related data.
Synchronous and asynchronous communication patterns chosen per interaction, with messaging infrastructure where eventual consistency is acceptable and preferable.
Distributed tracing, centralized logging, metrics, and alerting through our DevOps services practice, so failures across services remain diagnosable.
Independent pipelines per service with automated testing and controlled rollout, since the deployment independence is the point of the whole exercise.
The risk in decomposition is introducing distributed complexity faster than the team can absorb it. Our controls address that: extract one service at a time, keep the routing layer in front so traffic can be moved back, and build observability before the system becomes hard to reason about rather than after.
Extraction sequenced so each service is stable in production before the next begins, which keeps the number of simultaneous unknowns manageable.
Gradual routing with the ability to send traffic back to the monolith immediately, so rollback is a configuration change rather than a redeployment.
Automated contract tests so a change in one service cannot silently break a consumer, which is a failure mode monoliths simply do not have.
Running extracted services alongside the original code path and comparing outputs before switching traffic, particularly for calculation-heavy or financial logic.
Testing behavior under load and under simulated service failure, since distributed systems fail in patterns that never appear in single-process applications.
Cost is driven by coupling inside the existing system rather than by its size. A large but well-layered application decomposes far more easily than a smaller one where business logic, data access, and presentation are interwoven. The factors below set both timeline and price.
Tangled dependencies and shared mutable state require untangling before extraction, and this analysis work often exceeds the implementation effort itself.
Shared tables and cross-domain queries are the hardest part of most migrations, since splitting data requires decisions about consistency that touch the business directly.
Low coverage means building characterization tests before safe extraction, which adds time but prevents the silent behavior changes that erode trust in the project.
Teams already running containerized workloads with mature pipelines move much faster than those establishing that foundation alongside decomposition.
Cost scales with services extracted, which is why we recommend stopping when the remaining monolith is no longer causing pain rather than pursuing full decomposition.
The relevant evidence for a decomposition engagement is a project with comparable coupling and data entanglement, since that determines the effort far more than application size. Our modernization work spans legacy systems, phased extraction, and the platform engineering that supports it.
Engagements where services were extracted one at a time behind a routing layer, with the original system serving traffic continuously throughout.
Projects where splitting shared data was the central challenge, requiring consistency decisions made with the business rather than purely by engineering.
Engagements establishing pipelines, observability, and deployment automation before decomposition accelerated, which is what made the later phases sustainable.
Our work and story have been picked up by news outlets and databases worldwide.
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No. The existing application serves traffic throughout while services are extracted individually behind a routing layer. Traffic shifts gradually to each new service and can be routed back immediately if anything behaves unexpectedly.
Often not. The architecture pays off when multiple teams need to release independently and when components have genuinely different scaling needs. Below that threshold a well-structured monolith is cheaper to build and far cheaper to operate.
Extraction programs typically run over several months to a year or more, delivered incrementally so value arrives progressively. Timeline is driven by internal coupling and database entanglement rather than by application size.
Cost tracks coupling depth, database separation difficulty, existing test coverage, and how many services you extract. We assess during a modernization review and usually recommend extracting fewer services than teams initially expect.
Traffic routing is the rollback mechanism. Each extracted service runs behind a gateway, so reverting to the monolith path is a configuration change. Contract tests and parallel validation catch behavioral differences before traffic moves.
No, and usually you should not. Once the components causing delivery or scaling pain have been extracted, the remaining monolith is often the cheapest thing to keep. We recommend stopping at that point rather than pursuing completeness.
Tell us what your system does, where delivery is slow, and what scaling problems you are hitting. We will respond within one business day with a view on whether decomposition is warranted, which services to extract first, and what the platform work involves. Book a free consultation.
Tell us what you’re building. Our team will get back to you within one business day with a clear, no-obligation plan.
