Architecture and platform engineering

Built for the cloud it runs on

Most systems described as cloud-native are hosted in a cloud and built like they are not — one deployable, one database, scaled by making the machine bigger. The difference shows up under load, and again on the invoice.

Discuss your architecture

Where the architecture earns its keep

Cloud-native is not a stack, and it is not a hosting decision. It is a set of assumptions: that capacity changes, that any component can fail without taking the system with it, and that the platform underneath provides more than virtual machines.

Those assumptions pay off unevenly. A steady internal tool gains very little from being decomposed. A product with spiky load, tenants that must stay separated, or a workload that needs to scale on a different axis from the rest gains a great deal.

So the question we start with is not containers or serverless. It is which parts of the system have genuinely different operational shapes — because those are the only ones worth separating.

Scope

What you get

  1. Containerized services

    Kubernetes where a system needs long-running services, fine-grained control and predictable behavior under sustained load.

  2. Serverless workloads

    Managed functions and event pipelines where load is spiky or infrequent, and running a cluster would be cost without benefit.

  3. Multi-tenant platforms

    Separation designed into the data layer from the first schema, so one platform can serve many customers without one leaking into another.

  4. Event-driven integration

    Queues, streams and events between services, so a slow or failed component degrades the system rather than stopping it.

  5. Infrastructure as code

    Environments defined in version control and reproducible, because an architecture nobody can rebuild is a single point of failure.

  6. Observability and operations

    The logging, tracing and alerting that make a distributed system debuggable — built in, not added after the first incident.

Approach

How the decision gets made

  1. 01

    Find the different shapes

    Which workloads scale differently, fail differently or need isolating. Those are the seams worth cutting along.

  2. 02

    Choose per workload

    Containers, managed functions or a plain service — decided by the shape of the load rather than by a house standard.

  3. 03

    Make it reproducible

    Infrastructure in code and environments that can be rebuilt, before the system gets complicated enough to need it.

  4. 04

    Operate it

    The architecture is a hypothesis until it has run under real traffic. We stay to find out where it was wrong.

Proof

Where it has run before

Start with the architecture question

A readiness sprint maps the workloads, the constraints and the integration surface before anyone commits to a shape.

Explore a readiness sprint

Related

Frequently asked

What makes an application cloud-native rather than cloud-hosted?
Cloud-hosted means the same application running on someone else's servers. Cloud-native means the architecture assumes the platform underneath it — managed services instead of self-run ones, horizontal scale instead of bigger instances, and failure treated as routine rather than exceptional.
Do you use containers or serverless?
Both, decided per workload rather than per company. We have shipped Kubernetes where a system needed long-running services and fine-grained control, and serverless where the load was spiky and operating a cluster would have been overhead without a return.
Can you move an existing application to a cloud-native architecture?
Yes, and incrementally. The usual path is to extract the parts that benefit most — the spiky workload, the component that needs to scale separately — and leave the rest until there is a reason to move it.
Are we locked into one cloud provider?
Using managed services does create coupling, and pretending otherwise is how teams end up with an abstraction layer that costs more than the lock-in would have. We are explicit about which choices are portable and which are not, so the trade is made deliberately.

Start with one measurable use case.

A Readiness Sprint is a fixed-scope engagement that maps your integration and AI readiness and produces a production-oriented plan — before anything is built.

Book a Readiness Sprint Schedule a Call