Services
Cloud & Platform Engineering
Cloud architecture, migration, and DevOps — the infrastructure your software and AI workloads actually run on.
Cloud costs and reliability problems are rarely a pricing issue — they're an architecture issue. Lift-and-shift migrations, no infrastructure-as-code, and cost decisions made after the fact instead of during design compound quietly until a bill or an outage forces the conversation.
The number attached to that quiet compounding is large: enterprises typically waste somewhere between a fifth and two-fifths of their cloud budget on idle resources, oversized instances and services nobody is actively monitoring. Without a formal cost-discipline practice in place, that waste doesn't self-correct — it just becomes next quarter's baseline.
We design, migrate and operate cloud platforms across AWS, Google Cloud and Azure with infrastructure as code from day one — every environment reproducible and version-controlled, not hand-configured. Cost isn’t a monthly review after the fact: it’s estimated at design time and checked in CI before resources are provisioned, so a scaling decision’s cost impact is visible before it ships, not discovered on next month’s invoice. The same CI/CD and containerisation discipline covers AI workloads specifically — GPU deployment and model-serving infrastructure, not just standard web hosting.
A mature cost-discipline practice consistently cuts that waste roughly in half, and the mechanism is upstream of the bill, not downstream of it — cost estimation checked in CI before a resource is ever provisioned catches an expensive decision before it ships, rather than a finance team discovering it a month later. The same infrastructure-as-code and CI/CD rigor extends to AI-specific infrastructure — GPU provisioning and model-serving endpoints are treated as first-class, cost-estimated, version-controlled infrastructure, not a special case bolted on outside the normal pipeline.
Frequently asked
Should we migrate everything to the cloud at once?
Rarely. A phased migration, sequenced by which workloads carry the least migration risk and the most benefit, consistently outperforms a single big-bang cutover — and most large organisations now run hybrid or multi-cloud rather than one all-or-nothing move.
How do you actually control cloud costs, not just monitor them?
Cost estimation happens at design time and gets checked in the CI/CD pipeline itself, so an engineer sees the projected cost impact of a change before it's provisioned — not a finance team discovering it a month later on the bill.
Do you support the infrastructure our AI features actually need?
Yes — GPU deployment and model-serving infrastructure specifically, not just standard web hosting. The same infrastructure-as-code and CI/CD discipline applies whether the workload is a web app or a production model endpoint.
How do you decide between containers, Kubernetes and serverless for a workload?
By traffic shape and operational overhead, not preference. Serverless suits spiky or infrequent workloads where you don't want to pay for idle capacity; containers on a managed service suit steady, predictable traffic with simpler operational needs; full Kubernetes earns its complexity only once you genuinely need fine-grained orchestration across many services. Most platforms end up using more than one of the three for different parts of the system, not a single choice applied everywhere.