Cloud-native application development on AWS, Azure and GCP, and staged migration for the systems you already run - architecture, deployment, scaling, and the operational work that keeps it reliable after cutover.
Most cloud work we take on is one of two things: building an application that is cloud-native from the start, or moving a system that was never designed for the cloud onto it without breaking the business in the process. Both need the same things afterwards. Infrastructure defined in code, spend attributable to the workload creating it, and someone who understands the architecture when something goes wrong at 3am.
Cloud engineering from architecture through migration to the operational work that follows. Most estates are not moving all at once, so two things usually run alongside it: connecting the cloud side to the systems that stay on-premise, and moving the older estate across in stages. Our monolith-to-microservices migration on AWS walks through how that sequencing works in practice.
Day-to-day operation of AWS, GCP, and Azure environments: provisioning, patching, account structure, and the governance rules that decide who can create what. Useful when a team has cloud infrastructure but nobody whose job it is to own it.
Applications written for the cloud rather than moved onto it. Event-driven where the work is asynchronous, serverless where the load is uneven, and containers where it is steady. The point is that a traffic spike changes the bill rather than taking the service down.
We move your assets to the cloud using staged cutovers, parallel running, and tested rollback paths, so migration windows are planned rather than discovered. Data integrity is verified against the source before the old system is retired, and the cutover plan is agreed with you in advance.
Lambda, Cloud Functions, and managed queues for workloads that are bursty or infrequent, where paying for an idle server makes no sense. Serverless is not always cheaper at steady high volume, and we will say so when that is the situation you are in.
Workloads split across more than one provider, or across cloud and on-premise, usually because of data residency rules or an existing investment you are not ready to abandon. Multi-cloud costs more to operate than a single provider, so it should be a requirement rather than a preference.
Zero-trust networking, IAM policies scoped to what each service actually needs, and infrastructure controls mapped to frameworks like SOC 2 or HIPAA where your organization has to satisfy them. Mapping controls is engineering work. It is not the same as certification, and we do not offer certification.
We provision and manage all cloud infrastructure using Terraform, Pulumi, and AWS CDK automation techniques. This eliminates manual “Configuration drift,” making your environments audit-ready and enabling rapid deployments in minutes.
Containerized applications and production Kubernetes clusters on EKS, GKE, or AKS, deployed through GitOps with health checks and autoscaling configured. Kubernetes is operational overhead, so we introduce it when the workload justifies it rather than by default.
Cloud spending should not be a “black box” mystery. Our dependable FinOps practice identifies the right-sized resources, manages reserved instance strategies, and establishes proactive cost governance, so spend is attributable to the workloads creating it and reviewed against what each one actually needs.
What cloud-native architecture changes about running the system
Infrastructure that adds capacity when load arrives, instead of being sized for a peak that happens twice a year.
Hardware capital expenditure becomes an operating cost you can attribute to the workloads creating it.
Applications deployed in the regions your users are actually in, rather than the one the account was opened in.
Governance and access control built into the architecture rather than added once the environment is already running.
Automated CI/CD pipelines replace manual deployment steps, so shipping is a routine event rather than an evening.
Redundancy and automated backups, with restores tested rather than assumed to work.
A defined methodology for secure, cost-efficient cloud transformation.
Experienced engineers across all major cloud providers and DevOps toolchains
Deep technical analysis, architectural case studies, and strategic perspectives from our senior development teams.
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Related work that often sits alongside this one in the same engagement.