Cloud-native software built for the platform it runs on, not just moved onto it

An application is cloud-native when it is designed to use the cloud's strengths: horizontal scaling, managed services, automated recovery and frequent, independent releases. In practice that means stateless services in containers or functions, configuration kept outside the code, managed databases and queues, health checks, infrastructure as code and full observability. Hosting an unchanged application on a cloud virtual machine is cloud-hosted, which is useful, but it is not cloud-native.

We place each workload where its data, users, dependencies and required managed services already point, and default to one primary provider unless there is a clear reason to split. If your staff sign in through Microsoft Entra ID, Azure can simplify access control. If your analytics already live in BigQuery, Google Cloud keeps data close to it. If a required managed service or region exists on only one provider, that settles it. Each placement decision is written down with its trade-offs.

Multi-cloud is worth it only when there is a concrete reason, such as data residency, a customer or regulator requirement, a managed service available on one provider only, or resilience against a provider-wide outage. Otherwise, running well on one cloud is usually cheaper and simpler. When it is justified, we reduce the overhead with shared Terraform modules, one identity approach, common pipelines and a single monitoring view.

Hybrid cloud combines public cloud services with infrastructure you run yourself, such as an on-premises data centre or private servers, connected so applications and data work across both. It makes sense when some systems cannot move yet because of licensing, latency to site equipment, data rules or the cost of re-platforming. We design the connectivity, identity and monitoring so both sides can be operated as one estate.

No. Many successful cloud-native systems are a modular application deployed as a few containers with managed data services behind them. Microservices help when separate teams need to release independently or when parts of the system scale very differently. They also add network calls, distributed data and more operational work, so we split services only where the boundary is clear and the benefit outweighs that cost.

We manage lock-in rather than trying to avoid it completely, because some managed services save real effort. Portable layers such as containers, PostgreSQL, Kafka-compatible messaging, Terraform and open observability standards keep the core of the system movable. Where a proprietary service is worth using, we isolate it behind an interface and record the exit cost, so a later move is a known piece of work rather than a surprise.

Use serverless functions for short, event-triggered work with uneven traffic, such as processing an upload, reacting to a queue message or running a nightly task, because you pay per invocation and nothing sits idle. Containers suit long-running processes, steady traffic, WebSocket connections, large dependencies and jobs that exceed function time limits. Many systems use both: a containerised API with functions handling the events around it. We compare cold-start latency, local testing effort and running cost for your workload before choosing.

We use one identity source for people and short-lived federated credentials for services, so no long-lived keys are copied between providers. Staff sign in through a single identity provider federated into AWS, Azure and Google Cloud. A service on one cloud that calls another uses workload identity federation to exchange its own token for temporary access. Cross-cloud traffic runs over private interconnects or encrypted tunnels with a named owner, and logs from every provider land in one store for investigation.

The biggest drivers are compute sized for peak load, managed database tiers and network charges that architecture diagrams tend to hide. Data leaving a provider, traffic between zones or regions, NAT gateways and high log ingestion can all grow faster than your user numbers. We estimate these for each design option before the build, match per-request or provisioned pricing to your traffic shape, tag resources by service and environment, and set budget alerts so unexpected growth is noticed within days.

Timelines depend on the number of services, integrations and data migrations involved, and on how much existing code is reused. A small, well-defined first release, such as one containerised service with its pipeline, managed database and monitoring, can often be live within a few weeks of discovery. Larger platforms are delivered in phases, with working software to review every one to two weeks throughout.

Cloud-native projects are priced per phase, with a fixed quote in a written proposal that follows a discovery call. For cloud-native work the main drivers are the number of services and integrations, any data that must be migrated, whether a second provider or an on-premises link is involved, and how many environments and regions you need. The proposal also estimates the monthly cloud bill at your expected traffic, so build cost and running cost can be compared together.

Yes. Application code, Terraform and pipelines live in your repositories, and every resource is created in AWS accounts, Azure subscriptions or Google Cloud projects that you own, with billing directly between you and each provider. Container registries, DNS zones and Terraform state sit in your accounts too. We work through access you grant and can revoke at any time, and at handover you receive architecture notes, runbooks and recorded walkthroughs.