We decompose legacy monoliths into independently deployable microservices using domain-driven design and the strangler fig pattern — without a big-bang rewrite and without taking the business offline.
Decomposition Patterns and Technologies — Domain-Driven Design, Strangler Fig, Kafka, Kubernetes, Istio, gRPC, GitOps
Most monolith decomposition projects fail because they start with code, not with domain modelling. Teams begin extracting services based on technical layers — a user service, a product service — rather than business capabilities, and end up with distributed monoliths: services that are independently deployable on paper but so tightly coupled through synchronous REST calls and shared databases that they fail together and deploy together. The distributed monolith is harder to operate than the monolith it replaced.
Perimattic starts every decomposition engagement with domain-driven design. We map the bounded contexts inside your monolith — the business domains with clear ownership boundaries and defined data — before recommending a service extraction order. We use the strangler fig pattern rather than a parallel rewrite: new services are built alongside the running monolith, traffic is migrated incrementally, and the monolith code path is decommissioned only after the extracted service has been validated under real production traffic.
The value of independently deployable services is not architectural elegance — it is team velocity and system resilience. Teams that own their own service can deploy without coordinating with other teams. A failure in one service does not take down the platform. Services can be scaled to their actual load rather than the peak load of the entire application. These benefits are real, but they are only achievable if the decomposition is done correctly from the start.
Deployment risk
Full platform deployment required for every change — one team's release blocks all other teams
Deployment risk
Each service deployed independently on its own pipeline with no coordination required across teams
Scaling efficiency
Entire application scaled as one unit — over-provisioning required to handle partial load spikes
Scaling efficiency
Each service scaled to its actual load independently, reducing infrastructure cost significantly
Team autonomy
All teams working in a shared codebase — merge conflicts, release freezes, and inter-team blocking
Team autonomy
Each team owns one or more bounded contexts end to end, deploying independently without coordination
Fault isolation
A failure in one domain — a slow query, a memory leak — takes down the entire application
Fault isolation
Failures are isolated to the failing service; circuit breakers and retries prevent cascade failures
Release frequency
Weekly or monthly release cycles due to integration risk and the need to coordinate all teams
Release frequency
Multiple deployments per day per service, with each team controlling its own release cadence
The decisions made before the first service is extracted — bounded context boundaries, data ownership, API contracts — determine whether the decomposition produces autonomous microservices or a distributed monolith that is harder to operate than the original.
Seven specialist service lines covering every layer of your monolith-to-microservices engagement.
We map the business domains inside your monolith, define bounded context boundaries, and produce the domain model that governs the extraction sequence — before a single line of code is touched.
We stand up new microservices alongside the running monolith and migrate traffic incrementally using a façade layer, validating each service in production before decommissioning the corresponding monolith code path.
We replace synchronous monolith cross-domain calls with event-driven messaging using Apache Kafka or RabbitMQ, enabling loose coupling and resilient communication between extracted services.
We design and deploy the API gateway layer and service mesh — Istio or Envoy — for traffic management, mutual TLS, circuit breaking, and rate limiting across the microservices estate.
We containerise each extracted service and deploy to Kubernetes — EKS, AKS, GKE, or self-managed — with Helm charts, autoscaling policies, and health probes configured from the first deployment.
We establish logical data ownership boundaries within the shared monolith database and incrementally migrate each domain's data to its own dedicated schema or database as the corresponding service is extracted.
We instrument every extracted service with distributed tracing using Jaeger or Zipkin, metrics via Prometheus and Grafana, and structured logging — giving full end-to-end visibility across the microservices estate from day one.
A structured six-stage process from free domain analysis to production deployment and observability handover.
We map the business domains inside your monolith, identify bounded contexts, and produce a domain model with candidate service boundaries and data ownership — at no cost.
We sequence the extraction order using the strangler fig pattern, prioritising services that can be extracted with minimal data coupling and will deliver the greatest velocity benefit earliest.
We design the API contracts — REST or gRPC — and event schemas for each extracted service before writing extraction code, ensuring a stable interface for consumers from day one.
We extract services one bounded context at a time, routing traffic via a façade, validating each service in production, and decommissioning the monolith code path only after validation.
We containerise each extracted service, deploy to Kubernetes, configure the API gateway and service mesh, and migrate traffic incrementally with a tested rollback path at every step.
We instrument every service with distributed tracing, metrics, and structured logging, tune performance under load, and hand over with full runbook documentation.
We map the business domains inside your monolith, identify bounded contexts, and produce a domain model with candidate service boundaries and data ownership — at no cost.
We sequence the extraction order using the strangler fig pattern, prioritising services that can be extracted with minimal data coupling and will deliver the greatest velocity benefit earliest.
We design the API contracts — REST or gRPC — and event schemas for each extracted service before writing extraction code, ensuring a stable interface for consumers from day one.
We extract services one bounded context at a time, routing traffic via a façade, validating each service in production, and decommissioning the monolith code path only after validation.
We containerise each extracted service, deploy to Kubernetes, configure the API gateway and service mesh, and migrate traffic incrementally with a tested rollback path at every step.
We instrument every service with distributed tracing, metrics, and structured logging, tune performance under load, and hand over with full runbook documentation.
Select an industry to see how we decompose enterprise monoliths into independently deployable microservices.
E-commerce monoliths grow to a point where a single slow deployment blocks a hundred engineers and a failing promotions service takes down checkout. Decomposing the retail monolith unlocks per-service scaling, independent deployment, and the ability to rewrite the worst-performing domain without touching checkout or payments.
E-commerce monoliths grow to a point where a single slow deployment blocks a hundred engineers and a failing promotions service takes down checkout. Decomposing the retail monolith unlocks per-service scaling, independent deployment, and the ability to rewrite the worst-performing domain without touching checkout or payments.
Financial monoliths accumulate compliance risk as regulatory obligations change and legacy code becomes untestable. Domain-driven decomposition isolates high-compliance domains — payments, KYC, ledger — into independently auditable services with defined data boundaries and access controls.
Healthcare monoliths carry HIPAA and clinical governance risk when patient data, billing logic, and scheduling all share the same codebase and deployment boundary. Decomposition isolates PHI-bearing services into dedicated bounded contexts with encrypted data stores and auditable access.
SaaS companies outgrow their monolith when feature team velocity slows because every change requires a full deployment. Decomposition restores team autonomy — each product domain owned and deployed by a single team — and enables per-tenant scaling and independent SLA management.
Logistics systems require real-time visibility across multiple domains — shipment tracking, carrier connectivity, warehouse management, and customer notification — that are difficult to evolve independently inside a monolith. Decomposition enables event-driven integration and per-domain scaling during peak fulfilment periods.
Media platforms decompose monoliths to separate content delivery — which must scale to millions of concurrent readers — from content management and subscription logic, which is low-traffic but high-complexity. Decomposition enables CDN-backed read APIs and independent billing and entitlement services.
Perimattic's work resulted in stable production systems. The team was helpful, easily accessible, and communicative through email. Their professionalism was impressive.
Quality
4.5
Schedule
5.0
Cost
5.0
Willing to Refer
4.5
DevOps Managed Services · Oct 2023 – Aug 2024
24/7 monitoring and support for production environments plus Linux server administration for a leasing automation company.
The new architecture is scalable and highly efficient, saving a lot of money in fees. Perimattic provides high-quality IT consulting and cloud development work promptly and at great value. The team remains involved from the planning stage to providing support, showing diligence and proactiveness.
Quality
5.0
Schedule
5.0
Cost
4.5
Willing to Refer
5.0
AWS Migration (Legacy → Microservices) · Nov 2018 – Ongoing
Transitioned a travel systems company's legacy server system to an AWS-based microservices architecture with ongoing maintenance.
Four structural advantages that separate a successful decomposition from a distributed monolith.
We do not extract services arbitrarily — we define bounded contexts first. The domain model governs the extraction sequence and prevents the most common decomposition failure: drawing service boundaries that cross business domains.
We never recommend parallel rewrites. The strangler fig pattern keeps your monolith running in production throughout the decomposition, with each extracted service validated under real traffic before the monolith code path is removed.
We establish data ownership boundaries before service extraction begins. Each extracted service gets its own schema or database — no shared mutable state across service boundaries, no distributed join queries.
Distributed tracing, metrics, and structured logging are instrumented from the first service extraction, not retrofitted at the end. You have end-to-end visibility across the evolving architecture throughout the engagement.
“The difference between a successful decomposition and a distributed monolith is not the container orchestration platform — it is the bounded context design done before the first service is extracted.”
Monolith to microservices migration is the process of decomposing a large, single-deployable application into a set of independently deployable services, each owning a specific business capability and its own data store. Each microservice communicates with others via well-defined APIs — REST, gRPC, or event streams — and can be deployed, scaled, and updated independently without affecting the rest of the system. The goal is to restore team velocity, enable per-service scaling, and reduce the blast radius of failures to a single domain rather than the entire application.
The strangler fig pattern is an incremental decomposition approach where new microservices are built alongside the existing monolith and traffic is gradually redirected to them, service by service, until the monolith has been fully replaced. The name comes from the strangler fig tree, which grows around a host tree until it eventually replaces it. We use this pattern because it avoids the high-risk 'big-bang rewrite' — the most common cause of failed modernisation projects — and allows the business to continue operating on the monolith while new services are validated in production.
Decomposition is not always the right answer. If your monolith is small, your team is a single squad, and your deployment frequency is already satisfactory, the operational overhead of microservices — service discovery, distributed tracing, network latency, and eventual consistency — will outweigh the benefits. We also recommend against decomposition when the monolith has no automated test coverage: extracting services from untested code produces independently deployable, but equally untestable, microservices. In these cases, we recommend a bounded context analysis and test coverage improvement programme before any service extraction begins.
A typical enterprise monolith decomposition engagement runs 16 to 40 weeks, depending on the size of the codebase, the number of bounded contexts identified, the quality of existing test coverage, and the complexity of the shared database. We sequence decomposition to deliver value incrementally — the first extracted service is typically in production within 6 to 8 weeks of the engagement starting. We do not advocate for parallel-rewrite approaches that aim to replace the monolith in a single big-bang release; incremental extraction with production validation at each step is the only approach we recommend.
Database-per-service is one of the hardest problems in monolith decomposition because most monoliths share a single database across all domains, with cross-domain joins embedded throughout the application. We address this in stages: first, we identify the data that belongs to each bounded context and introduce logical ownership boundaries within the shared schema; second, we introduce an anti-corruption layer that prevents cross-domain direct database access; third, we migrate each domain's data to its own dedicated schema or database as the corresponding service is extracted. We use the saga pattern or event-driven eventual consistency to replace synchronous cross-domain database joins.
Distributed transactions — operations that must succeed or fail atomically across multiple services — are a fundamental challenge in microservices architecture. We design around distributed transactions wherever possible by modelling business processes as sagas: a sequence of local service transactions, each with a compensating transaction that can be executed if a later step fails. For use cases where strong consistency is genuinely required, we use the outbox pattern combined with a reliable message broker — Kafka or RabbitMQ — to ensure at-least-once delivery with idempotent consumers, giving eventual consistency guarantees without distributed locking.
Conway's Law states that organisations design systems that mirror their communication structures — and this applies directly to microservices. We recommend a team-per-service or team-per-domain structure where a single, cross-functional team owns one or more bounded contexts end to end: design, development, testing, deployment, and on-call operations. Teams should not share ownership of a service. The primary organisational change required for microservices is not technical — it is the shift from functional teams (a separate ops team, a separate QA team) to product teams that own their service through its full lifecycle.
The cost of a monolith decomposition engagement depends on the complexity of the existing codebase, the number of bounded contexts identified, the state of existing test coverage, and the scope of infrastructure work required. A focused engagement covering two to three bounded contexts with a well-tested monolith typically runs in the range of USD 40,000 to 120,000. A full enterprise decomposition programme covering a large codebase with multiple domain teams typically runs from USD 120,000 to 400,000 or more. We produce a detailed scope and cost estimate as part of our free decomposition discovery call — no commitment required.
We establish baseline metrics before decomposition begins and measure the same metrics after each service extraction: deployment frequency per service, lead time from commit to production, mean time to recovery when a service fails, and infrastructure cost per transaction. Successful decompositions typically show: a 3x to 10x increase in deployment frequency as teams can deploy their service without coordinating with others, a reduction in mean time to recovery from hours to minutes because failures are isolated to a single service, and a reduction in cloud infrastructure cost because each service can be scaled independently to its actual load.
We begin with a free decomposition discovery call where you describe your monolith — its size, age, technology stack, team structure, and the specific pain points slowing delivery. From that conversation, we produce a bounded context map and a preliminary decomposition roadmap identifying two to three candidate services for the first extraction sprint, along with the data ownership boundaries that need to be established before extraction can begin. If the engagement proceeds, we spend the first two weeks in a paid domain analysis phase before any code is written — this phase produces the authoritative bounded context map and the strangler fig sequencing plan that governs the full engagement.
Tell us about your monolith — its size, team structure, and the pain points slowing delivery — and we will produce a decomposition plan and bounded context map in a free discovery call.