• Every deployment is a full-application release that takes hours and requires the whole engineering team to be on standby?

  • One module under load forces you to scale the entire monolith -- paying for capacity you do not need in 80% of the codebase?

Microservices Migration

A monolithic application that worked fine at 10 engineers and 50,000 users starts to cause problems at 40 engineers and 500,000 users. Deployment takes hours because everything deploys together. A bug in one module takes down the whole application. Scaling requires scaling the entire application even when only one component is under load. The codebase becomes too large for any individual to fully understand.
We decompose monolithic applications into microservices -- identifying bounded contexts, defining service boundaries, building the API gateway and service mesh, and migrating incrementally so the business keeps running while the architecture improves. Deployed to Kubernetes on AWS EKS, Azure AKS, or GCP GKE.

  • Bounded context analysis to identify the right service boundaries before any code is split -- avoiding a distributed monolith

  • Incremental strangler fig migration so the existing application keeps running as services are extracted one at a time

  • Kubernetes deployment on EKS, AKS, or GKE with full observability from day one -- metrics, logs, and distributed tracing

  • Data ownership strategy for each service -- solving the shared database problem before it becomes a production incident

RaftLabs migrates monolithic applications to microservices -- identifying service boundaries through domain analysis, containerising services with Docker, deploying to Kubernetes on AWS EKS, Azure AKS, or GCP GKE, and migrating incrementally using the strangler fig pattern to keep the existing application running throughout. Microservices migration projects typically cost $40,000 to $150,000 or more depending on monolith size and complexity.

Vodafone
Aldi
Nike
Microsoft
Heineken
Cisco
Calorgas
Energia Rewards
GE
Bank of America
T-Mobile
Valero
Techstars
East Ventures

The problem with microservices is not building them. It is migrating to them from a monolith that is in production, serving real users, and cannot be taken offline for a six-month rewrite. The teams that attempt a big-bang rewrite -- freeze the monolith, build the microservices in parallel, cut over on a date -- typically discover that the date keeps moving and the monolith keeps getting features because the business does not stop while the rewrite is happening.

Incremental migration is harder to design but it is the only approach that works for production systems. Each extracted service must work alongside the monolith, not instead of it, until enough of the architecture has moved that the monolith can be retired on a schedule the business controls.

What we build

Monolith decomposition and service boundary design

Domain-driven analysis of the existing monolith to identify bounded contexts -- the natural service boundaries within the codebase that correspond to distinct business domains. Codebase analysis to identify coupling: which modules share data models, which modules call each other directly, and which modules could be extracted with minimal changes to the rest of the codebase. Target architecture design specifying each service's responsibility, API contract, and data ownership. Extraction priority sequencing based on independence, business impact, and team ownership. The architecture definition that guides the migration and prevents extracting services in the wrong order or at the wrong granularity.

Containerisation with Docker

Containerisation of extracted services and the remaining monolith into Docker containers for consistent deployment across development, staging, and production environments. Dockerfile authoring with multi-stage builds to minimise image size and surface area. Docker Compose configuration for local development so engineers can run the full service mesh on their laptops. Container image build pipelines integrated into CI/CD. Image security scanning for known vulnerabilities in base images and dependencies. Container registry setup with AWS ECR, Azure Container Registry, or Google Artifact Registry. The containerisation that makes every service deployable consistently regardless of the environment it runs in.

Kubernetes deployment and orchestration

Kubernetes cluster setup and service deployment on AWS EKS, Azure AKS, or GCP GKE. Namespace design for environment and team isolation. Deployment manifests with resource requests and limits for each service. Horizontal Pod Autoscaler configuration for services with variable load. Rolling update strategy for zero-downtime deployments. PodDisruptionBudgets to maintain availability during node drains and cluster upgrades. Persistent volume configuration for stateful workloads. RBAC configuration for least-privilege cluster access. The Kubernetes environment that runs the service mesh reliably rather than adding operational overhead to the engineering team.

API gateway and service mesh setup

API gateway deployment to handle routing, authentication, rate limiting, and request transformation at the edge of the microservices architecture. Kong, AWS API Gateway, or Azure API Management configured to route traffic to the correct service and version. Service mesh deployment with Istio or Linkerd for east-west service-to-service traffic management -- mutual TLS, traffic shaping, and circuit breaking between services. Service discovery configuration so services find each other without hardcoded addresses. The gateway and mesh that make the microservices architecture manageable rather than a network configuration problem for individual service teams.

Incremental strangler fig migration

Migration orchestration following the strangler fig pattern -- extracting services one domain at a time while the monolith handles everything else. API gateway routing configuration that sends specific paths to new services and falls back to the monolith for everything not yet migrated. Shared database decomposition strategy: each extracted service gets its own database, with CDC replication or event-based sync maintaining consistency during the transition period. Feature flag integration for canary routing -- sending a percentage of traffic to the new service before full cutover. Monolith decommission plan by domain, with each extraction reducing the monolith's responsibility until it handles nothing and can be retired.

Service communication and event-driven architecture

Inter-service communication design -- synchronous (REST or gRPC) for request-response interactions where the caller needs an immediate response, and asynchronous (event-driven via Kafka, SQS, or RabbitMQ) for workflows where the service publishes an event and does not need to wait for downstream processing. Event schema design and evolution strategy so services can consume events from different versions of producers. Dead letter queue configuration for failed message handling. Distributed transaction patterns for operations that span multiple services -- saga pattern or outbox pattern depending on consistency requirements. Observability across service boundaries: distributed tracing with Jaeger or AWS X-Ray, structured logging with correlation IDs, and service-level metrics in Prometheus and Grafana.

Which part of your monolith is causing the most pain -- deployment, scaling, or team independence?

Tell us the monolith's technology stack, size, and the specific problems driving the migration decision. We will assess the service boundaries and give you a phased migration plan.

Talk about your microservices migration

  • DevOps -- CI/CD pipelines for microservices deployments

  • Legacy Modernisation -- legacy system modernisation alongside or before microservices migration

Frequently asked questions

Service extraction priority follows two criteria: independence and impact. Independence means the candidate service has clear data ownership and minimal runtime coupling to the rest of the monolith -- it can be extracted without requiring simultaneous changes across the rest of the codebase. Impact means extracting the service solves a real problem: a module that needs independent scaling, a component that causes deployment conflicts, or a bounded context that a separate team owns and wants to deploy independently. The first extraction is deliberately conservative -- pick a well-understood module with clean boundaries, not the most complex part of the codebase. The first extraction proves out the deployment pipeline, service mesh, observability stack, and data ownership pattern. Subsequent extractions are faster because the infrastructure is proven. We work through the monolith incrementally, with each extraction adding to a target state architecture rather than extracting services in a random order.

The strangler fig pattern migrates a monolith by routing specific requests away from the monolith to new microservices, one domain at a time, until the monolith handles no traffic and can be retired. The name comes from the strangler fig tree, which grows around an existing tree and eventually replaces it. In practice: an API gateway or reverse proxy sits in front of the monolith. When a new microservice is ready, the gateway routes requests for that domain to the microservice instead of the monolith. The monolith continues handling everything else. Over time, more routes are migrated. The monolith shrinks. Eventually it handles nothing and is decommissioned. The business continues operating throughout -- there is no big-bang cutover where the entire monolith is replaced on a single day. The strangler fig pattern is the standard approach for migrating production applications that cannot tolerate the risk of a full rewrite and simultaneous cutover.

The shared database problem is the hardest part of microservices migration. A monolith typically has a single database that every module writes to directly. Microservices need their own data stores -- if they share a database, you have not achieved service independence and the database becomes the source of coupling you were trying to remove. The migration approach: during the strangler fig extraction, each new service gets its own database. The service syncs data from the monolith database using event publishing or CDC replication during the transition period. Once the service owns its domain fully and the monolith is no longer the system of record for that data, the sync is removed. For data that must be queried across service boundaries, we design the cross-service query pattern -- API composition, materialised views, or an event-sourced read model -- based on the query requirements and acceptable latency.

Microservices migration cost depends primarily on monolith size -- number of distinct domains, lines of code, team count -- and the state of the existing codebase. A focused migration extracting 3 to 5 services from a well-structured monolith into a Kubernetes environment typically runs $40,000 to $80,000. A full decomposition of a large monolith covering 10 or more services, complex shared database resolution, and a multi-team rollout typically runs $100,000 to $150,000 or more. We scope migrations in phases: a paid assessment engagement produces the target architecture, service boundary definitions, and a phased extraction roadmap with effort estimates per phase. The full migration is scoped and priced after the assessment rather than before it.