Cloud-Native Platform Architecture
Migrating and modernising legacy systems — or building new platforms from the ground up — on cloud-native infrastructure designed for resilience, independent team deployability, and long-term operational simplicity.
The Problem
Legacy monoliths accumulate technical debt until deployments become risky, new features take months instead of weeks, and any outage can take down the entire system. Organisations need a path to modern infrastructure without stopping the business.
We apply the strangler-fig pattern to migrate incrementally — or design a greenfield microservices architecture from the outset — with containerised services, managed cloud infrastructure, and a CI/CD pipeline that allows teams to ship independently and safely.
How We Engineer It
Architecture Assessment
We map the existing system: service dependencies, data flows, traffic patterns, and pain points. We identify which bounded contexts should become independent services and which should remain unified.
Migration Strategy
We favour incremental migration over big-bang rewrites. We identify the highest-value service to extract first, build it alongside the monolith with a routing layer, validate it in production, then repeat — reducing risk at every step.
Infrastructure as Code
Every cloud resource is defined in Terraform. Environments are reproducible, auditable, and version-controlled. No manual console configuration — changes go through code review and CI before they reach production.
CI/CD Pipeline Design
We build pipelines that allow engineers to deploy individual services independently. Feature flags, canary releases, and automated rollbacks are standard — not optional.
Observability from Day One
Distributed tracing, structured logging, and metrics dashboards are set up before the first service goes live. We define SLOs and alert on error budgets, not on arbitrary thresholds.
The Stack We Use
Engineering Skills Applied
- Microservices decomposition and boundary design
- Zero-downtime migration strategy
- Kubernetes cluster design and operations
- Infrastructure as code (Terraform / Pulumi)
- Distributed system observability
- SLO definition and error budget tracking
- Multi-cloud and hybrid-cloud architecture
- Security hardening and IAM design
These outcomes describe what well-engineered solutions in this category tend to achieve. Specific results depend on the starting state and scope of each engagement.
- Deployment frequency typically increases when teams can ship services independently
- Mean time to recovery drops when rollback is automated and observability is in place
- Infrastructure costs become more predictable with autoscaling and right-sizing
- Developer experience improves when local and CI environments mirror production