Lift-and-shift gave you a cloud bill. Modernisation gives you cloud agility.

Moving a virtual machine to cloud infrastructure reduces capital expenditure and transfers operational responsibility. It does not make your application more scalable, more resilient, easier to deploy or faster to iterate on. The agility that cloud promised requires a different approach: breaking monolithic applications into composable services, containerising workloads so they run consistently anywhere, building the platform abstractions that let developers focus on product work rather than infrastructure management. Node takes you from where you are to a cloud-native architecture that actually delivers what cloud was supposed to.

What cloud-native modernisation means in practice

Cloud-native is not a technology choice - it is an architectural approach. Applications designed for cloud-native operation are built from small, independently deployable services. Each service is containerised, runs on shared infrastructure managed by an orchestration layer, communicates via well-defined APIs, is deployed through automated pipelines, and operates with built-in observability.

The journey to cloud-native is not a big-bang migration. Large monolithic applications cannot be rewritten overnight without destroying business continuity. We use proven patterns - the strangler fig, decomposition by bounded context, progressive extraction - to move applications incrementally from legacy architectures to cloud-native designs without requiring a complete rewrite or an extended period of parallel operation.

Containerisation strategy and implementation

Containers are the foundational unit of cloud-native deployment. They provide consistent runtime environments, efficient resource utilisation, fast startup times and the portability that makes deployments repeatable across development, staging and production.

Application containerisation - we work through your application portfolio, assessing each application for containerisation readiness, identifying dependencies and configuration requirements, and building Docker images that follow best practices: minimal base images, non-root execution, immutable build artifacts, and secret injection at runtime rather than bake-time.

Container image security - every image is scanned for vulnerabilities using automated tooling integrated into the build pipeline. Base images are maintained and updated on a regular schedule. Image signing and verification ensures that only validated images are deployed to production.

Kubernetes platform setup - containers need orchestration to run reliably at scale. We deploy and manage Kubernetes clusters (EKS, AKS, GKE or upstream Kubernetes) configured for production operation: high availability control planes, auto-scaling node groups, network policies, pod security standards, resource quotas and limit ranges.

Helm and GitOps deployment - application deployments are managed through Helm charts and GitOps tooling (ArgoCD or Flux). The desired state of every deployment is defined in Git. Changes are made by updating the Git repository, not by running manual commands against the cluster. Deployment history is complete and every change is auditable.

API strategy and microservices migration

Microservices architectures require a coherent API strategy. Services that communicate through ad hoc internal APIs create coupling that defeats the purpose of decomposition.

API design and governance - we establish API design standards (OpenAPI specification, versioning policy, breaking change management) and implement API governance tooling that validates new APIs against standards before they are deployed.

Service decomposition - we work with your architecture and engineering teams to identify bounded contexts within existing monolithic applications and define the service boundaries that will emerge from decomposition. This is the hardest part of microservices migration and the part where experience matters most - poor service boundaries create distributed monoliths that are worse than what they replaced.

Apache APISIX for internal and external API management - we integrate APISIX as the API gateway layer for both internal service-to-service communication and external-facing APIs. Authentication, rate limiting, circuit breaking, observability and routing are handled at the gateway layer rather than reimplemented in each service.

Event-driven integration - not all service communication is request-response. We implement event-driven patterns using Apache Kafka for workloads that benefit from asynchronous processing, loose coupling and reliable event delivery. Services publish events to topics and consume events from topics without direct dependencies on each other.

Platform engineering and internal developer platforms

The productivity of engineering teams is determined more by their internal tooling than by the technology stack they use. Platform engineering builds the golden paths that make doing the right thing the easy thing.

Internal developer platform (IDP) - we design and build internal developer platforms that abstract infrastructure complexity away from application teams. Developers provision environments, deploy applications, view logs, manage secrets and trigger pipelines through a unified interface - without needing deep Kubernetes or cloud expertise.

Golden paths and templates - standardised project templates that give developers a fully configured starting point: CI/CD pipeline, containerisation, observability, secret management, and cloud resource provisioning all configured correctly from day one. Teams spend their time building features, not configuring infrastructure.

Self-service infrastructure - through platforms like Backstage or Humanitec, developers provision the cloud resources their applications need - databases, caches, queues, storage - through approved, policy-compliant templates without filing tickets or waiting for operations teams to provision manually.

Developer experience measurement - we implement DORA metrics (deployment frequency, lead time for changes, change failure rate, time to restore service) as operational measurements that give you objective visibility into how platform improvements translate into engineering productivity.

GitOps and operational maturity

Cloud-native operations require operational practices that match the pace of cloud-native deployment.

GitOps for infrastructure and applications - all infrastructure and application configuration lives in Git. Changes are proposed as pull requests, reviewed, approved and merged. The platform reconciles the desired state in Git with the actual state of the environment automatically. Manual changes to running systems are eliminated.

Progressive delivery - we implement deployment patterns (blue-green, canary, feature flags) that allow new versions to be released to a subset of users before full rollout. Risk is managed through observability and automated rollback rather than by slowing deployment frequency.

Observability stack - production cloud-native systems require comprehensive observability: metrics, logs and traces collected, correlated and made accessible to engineering teams. We implement observability stacks (Prometheus, Grafana, OpenTelemetry, Loki) that give engineers the information they need to understand system behaviour and diagnose problems quickly.

Incident response automation - runbooks for common incident scenarios are codified and executable. On-call engineers have access to automated remediation actions rather than manual playbooks, reducing mean time to restore and reducing the cognitive load of out-of-hours response.

The platform engineering shift - the most impactful change in cloud-native engineering over the last five years is not a technology - it is an organisational model. Platform engineering teams build and operate the internal platforms that product engineering teams build on. The platform team owns Kubernetes, CI/CD, observability and golden paths. Product teams own their services. Both move faster because responsibility is clear and the abstractions are right. We have built and operated platform engineering functions across multiple organisations and know what works at different scales and maturity levels. We help you design the right model for your team size, engineering culture and business velocity.