Decompose monolithic applications into independently deployable microservices using the strangler fig pattern — incremental migration that keeps the monolith running throughout, with zero big-bang risk.
Monolithic architecture isn't inherently wrong — but there are clear signals that the architecture is now a constraint on how fast you can build, deploy, and scale.
Change one module, redeploy the whole application. That means a full regression test cycle, a coordinated release window, and the risk that your fix breaks something in a completely unrelated area of the codebase.
Merge conflicts aren't occasional — they're a daily battle. Multiple teams working on the same codebase creates coordination overhead that slows everyone down. Feature development waits for other teams to finish and merge.
Your checkout service is under load, but you have to scale the entire application — including the reporting module, the admin panel, and the notification service — because there's no way to isolate the one thing that needs more capacity.
A new team wants to use Python ML inference. Another needs a real-time websocket layer. But the monolith's tech stack is locked. Upgrading one component means upgrading — or rigorously testing — everything that touches it.
Testing the whole system before release takes weeks. Coordinating deployments across teams takes more. The business waits months for features that would take days to build — and the competitive window closes.
We decompose monoliths incrementally — one service at a time — so the monolith keeps running throughout and customers experience no disruption.
Map bounded contexts using Domain-Driven Design. Identify the natural seams in your monolith — where domains are loosely coupled and extraction would be minimally disruptive.
Event storming workshops, aggregate identification, dependency graph analysis.
Extract the highest-value, lowest-risk service first. Route traffic through an API gateway. The monolith keeps running — customers see nothing change.
API gateway setup, strangler proxy routing, parallel data access.
Service by service, the monolith shrinks. Each extracted service is independently deployable, independently testable, and independently scalable.
Per-service CI/CD, data decomposition, event-driven sync between services.
The remaining monolith handles only core shared concerns — or is fully decomposed. Most teams stop here. A small, well-understood monolith core is not a problem.
Decommission decision, shared service extraction, operational stabilisation.
Not every monolith needs full decomposition. Sometimes extracting 3–4 high-value services is enough to solve the scaling and delivery problems you have. We'll tell you when to stop.
Microservices are powerful — but they come with real operational complexity. Here are the situations where we'd tell you to hold off:
Your engineering team is fewer than 10 engineers — the operational overhead of microservices (separate deployments, distributed tracing, service mesh) may outweigh the benefits.
The monolith is well-structured with clean module boundaries and good test coverage — refactoring the internals may be all you need.
You don't have CI/CD, containerisation, or observability in place — get the DevOps foundation right before decomposing.
The performance problem is in the database, not the application — decomposing won't fix a slow query or a missing index.
You're doing this because 'microservices are the right architecture' rather than to solve a specific scaling or team delivery problem.
We'll be honest about whether decomposition is right for your situation. Sometimes the best advice is "not yet" — and that's advice worth paying for.
Four specialised services that cover every phase of monolith-to-microservices migration.
Event storming workshops and bounded context mapping to identify the right service boundaries before any code is extracted.
Learn morePhased extraction of services from the monolith using the strangler fig pattern — API gateway routing, parallel running, and validated cutover.
Learn moreSplitting the shared database — the hardest part of going microservices. Per-service databases, event-driven sync, and eventual consistency strategies.
Learn moreAPI gateway implementation for external routing, service mesh for internal communication, distributed tracing, and inter-service security.
Learn moreSix phases from initial domain analysis through operationally stable independently deployable services.
Event storming workshops, bounded context mapping, aggregate identification using Domain-Driven Design.
Trace runtime dependencies, shared database tables, and integration points across the monolith.
Prioritise which services to extract first — high business value + loose coupling = first candidates.
Strangler fig implementation — API gateway routing, parallel running, data migration per service.
Split the shared database into per-service databases with event-driven sync and eventual consistency.
Per-service CI/CD pipelines, distributed tracing, service mesh, SLOs per service.
We use cloud-agnostic, battle-tested tooling across all decomposition projects — so you're never dependent on a single vendor.
Backend Services
API Gateway
Service Mesh
Event Streaming
Containers
Observability
AI-Assisted Decomposition
We use AI-powered code analysis to accelerate boundary identification — automated dependency graph generation, AI-assisted service boundary suggestions based on code coupling analysis, and intelligent test generation for extracted services that previously had no test coverage.
Real monolith decompositions — measured in release velocity, performance improvement, and zero customer-facing downtime.
Decomposed a 500,000-line PHP monolith into 12 microservices using the strangler fig pattern, achieving independent deployments and 4× faster release velocity — with zero customer-facing downtime during the 8-month migration.
Read Case StudyExtracted billing, authentication, and notification services from a .NET monolith into independently scalable .NET 8 microservices, reducing billing processing time by 60% and enabling teams to deploy independently for the first time.
Read Case StudyMigrated a legacy Java EE monolith to Spring Boot microservices, enabling real-time fleet tracking that was architecturally impossible with the monolith's batch processing model — delivered in 10-week strangler fig migration.
Read Case StudyIncremental migration that keeps the monolith running throughout — not a risky big-bang rewrite that takes two years and delivers nothing.
We start with domain modelling and event storming, not technology choices. Getting the service boundaries right upfront prevents expensive rework later.
We'll tell you if microservices aren't right for your situation — even if that's not the answer you came in wanting to hear.
From domain analysis through Kubernetes-deployed, observable microservices. We don't hand off the hard parts to your team.
Splitting the shared database is the hardest part of going microservices. We've done it — with per-service databases, event-driven sync, and validated cutover.
Common questions about monolith-to-microservices migration — answered directly.
