Microservices Migration
Stop fighting with tangled legacy code. We safely migrate your monolithic application into scalable, independent microservices.
A monolith that has grown for years eventually makes every change expensive. Deployments become high-risk events involving the whole system, teams block each other in the same codebase, and one component's resource demands force scaling of everything around it.
Impact
Release frequency falls as risk rises, so improvements queue up behind an increasingly nervous deployment process. Engineering time shifts from building to coordinating. Infrastructure costs grow because scaling is all-or-nothing rather than targeted.
We migrate incrementally rather than rewriting. Services are extracted along genuine business boundaries, one at a time, with the monolith continuing to run throughout. There is no cutover event and no period where the business waits for a rebuild to finish.
Technical Approach
Extraction typically follows a strangler-fig pattern, routing traffic progressively to new services while the original path remains available as a fallback. Services run on Kubernetes with distributed tracing in place before migration begins, because you cannot safely decompose a system whose behaviour you cannot observe.
Every change requires deploying the entire application, teams contend over shared code, and scaling one busy component means scaling everything.
Services deploy independently on their own cadence, teams own their boundaries, and scaling targets the component that actually needs it.
Strict adherence to global data privacy laws. We never train public AI models on your proprietary data.
Architecture designed to meet rigorous healthcare and enterprise security compliance standards natively.
Scalable cloud-native deployments via AWS and Vercel Edge networks ensuring 99.99% uptime.
Everything you need to know about our Microservices Migration process.
Database decoupling is the hardest part. We use patterns like 'Database-per-service' and implement event streaming (Kafka) to ensure data consistency across boundaries without creating tight coupling.
Often not, and this deserves an honest answer before any budget is committed. Microservices trade code complexity for operational and network complexity — that is a good trade when you have multiple teams blocking each other or genuinely divergent scaling needs, and a poor one otherwise. A well-structured monolith is the right architecture for a great many successful companies. If your problem is code organisation rather than team or scaling constraints, modularising the monolith is cheaper and lower-risk.
By never doing one. Migrations that stop feature development to rebuild wholesale fail at a well-documented rate, because the business cannot pause for the duration and the new system chases a moving target. We extract one service at a time behind routing that can be reverted, so the system is working at every point and value arrives before the migration completes.
Along business capabilities and data ownership, not technical layers. Boundaries drawn on technical lines produce services that must be deployed together, which is the worst of both architectures. We map how data is actually owned and changed before proposing any split, and we start with the components that are most independent.
Because it is done alongside continuing feature delivery rather than instead of it, and because observability and boundary analysis come before extraction. The range depends heavily on how entangled the data model is — systems where multiple components write to the same tables take considerably longer to separate safely. VERIFY: confirm against your delivery history.
Let's plan your microservices migration.
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