Cloud-native
development.

Cloud-native means operable, not just containerised

Putting an application in a container and running it on Kubernetes does not make it cloud-native. What matters is whether the system can be deployed, scaled, observed, and recovered without heroics — and those are properties of how the application is written, not of where it runs.

Concretely: configuration comes from the environment rather than from files baked into images, so the same artefact promotes from staging to production. Processes are stateless so instances can be added and removed freely, with state pushed to managed data services. Startup and shutdown are fast and handle signals correctly, because autoscaling and rolling deployments depend on it. Health checks distinguish liveness from readiness, so traffic is not routed to an instance that is running but not yet able to serve.

These are unglamorous properties and they are what determine whether scaling is a configuration change or a project. We would rather deliver a well-behaved application on modest infrastructure than an elaborate platform hosting one that cannot be restarted safely.

Choosing the right compute, honestly

Kubernetes is the default answer to more questions than it should be. It is genuinely the right choice for heterogeneous workloads at scale, complex networking requirements, or multi-cloud portability — and it brings a real operational burden that a small team will feel.

For a straightforward web application or API, managed container platforms deliver most of the benefit with a fraction of the operational surface. For event-driven and bursty workloads, serverless functions are often better economics and less to run — with the caveat that cold starts matter, which is a live concern when the workload is model inference and a multi-gigabyte model must load into memory.

For AI workloads specifically the calculus differs from ordinary web services. GPU capacity is expensive and scales poorly to zero, so the architectural question is usually whether predictions genuinely need real-time serving or whether batch scoring serves the same decision — see batch vs real-time inference, where the cost difference is commonly an order of magnitude. We size that before choosing a platform, not after.

What we build in, by default

Infrastructure as code from the first commit, so environments are reproducible and reviewable rather than assembled by hand and undocumented. CI/CD with automated tests as a gate, and deployments that roll forward without downtime and roll back in one action.

Observability that answers questions rather than producing dashboards: structured logs with correlation IDs so a single request can be traced across services, metrics on the things that indicate user-visible health, and alerting on symptoms rather than causes. For AI systems this extends to prediction logging and distribution monitoring, because a model can degrade while every infrastructure metric stays green.

Security as configuration rather than convention — least-privilege service identities, secrets in a managed store and never in images or repositories, network policy that denies by default, and dependency scanning in the pipeline. And cost visibility per environment and per service, because cloud spend that nobody owns grows steadily and invisibly. We would rather show you the cost model during design than explain the bill afterwards.