Service
Cloud Engineering
We build and operate cloud platforms that teams can deploy to confidently — reproducible infrastructure, automated pipelines, real observability and spend that stays explainable.
What is cloud engineering?
Cloud engineering is the discipline of designing, automating and operating infrastructure on cloud platforms. It covers how environments are defined in code, how applications are deployed and rolled back, how systems are monitored, how identity and network boundaries are enforced, and how spend is attributed and controlled.
It is distinct from cloud migration, which is the one-time move of workloads onto a cloud platform. Cloud engineering is the ongoing capability that determines whether that platform stays reliable and affordable afterwards.
Cloud platforms make it easy to create resources and difficult to keep track of them. Infrastructure drifts from documentation, environments diverge, permissions accumulate and costs grow in ways nobody can attribute.
We treat infrastructure as software. Environments are defined in code and reproducible from scratch. Deployments are automated with a rollback path. Telemetry exists before incidents, not after them. Access follows least privilege by default. Cost is tagged and reported per environment and per workload.
Business problems
What this usually solves.
The situations clients describe when they start this conversation.
Environments built by hand
Staging does not match production, nobody knows which setting differs, and problems only appear after release.
Deployments nobody wants to run
Releases are manual, partly undocumented and effectively irreversible, so they happen rarely and carry more risk each time.
Incidents diagnosed by guesswork
Without distributed tracing, structured logs and meaningful alerts, diagnosis depends on whoever remembers the system best.
Cloud spend with no owner
Resources lack tags, idle capacity persists and nobody can attribute the bill to a product, environment or customer.
Capabilities
What we bring to it.
Infrastructure as code
Terraform or Bicep definitions covering networking, compute, data and identity, reviewed and versioned like application code.
CI/CD pipelines
Build, test, scan and deploy pipelines with environment promotion, approval gates and automated rollback.
Observability
Structured logging, distributed tracing, metrics and alerting tied to service-level objectives rather than raw thresholds.
Security baselines
Least-privilege identity, managed secrets, private networking, encryption in transit and at rest, and policy enforcement.
Resilience engineering
Health checks, graceful degradation, backup and restore that is actually tested, and documented recovery objectives.
Cost engineering
Tagging standards, right-sizing, autoscaling policies, and spend reporting attributable to teams and workloads.
Deliverables
What we build
- Greenfield cloud platforms for new products
- Landing zones with governance, identity and network baselines
- Containerised workloads on managed Kubernetes or serverless runtimes
- Multi-environment pipelines with preview environments per branch
- Observability stacks with SLOs, dashboards and actionable alerting
- Cost optimisation programmes with measurable before-and-after spend
Stack
Technology approach
We work primarily on Microsoft Azure, with AWS and Google Cloud where the workload or existing estate calls for it.
| Layer | What we use |
|---|---|
| Definition | Terraform and Bicep, modular and environment-parameterised |
| Compute | Azure App Service, Container Apps, AKS, AWS ECS and Lambda |
| Data | Managed PostgreSQL, Azure SQL, object storage with lifecycle policies |
| Identity | Microsoft Entra ID, managed identities, workload federation |
| Pipelines | GitHub Actions and Azure DevOps with environment promotion |
| Telemetry | Azure Monitor, Application Insights, OpenTelemetry instrumentation |
Process
How we deliver.
- 01
Discover
Understand business requirements and existing systems.
- 02
Architect
Design product, cloud, integration and data architecture.
- 03
Engineer
Build production-grade software.
- 04
Launch
Deploy, integrate and validate.
- 05
Scale
Optimize, monitor and evolve.
Case studies
Relevant work
FAQ
Cloud Engineering — questions we are asked
Azure, AWS or Google Cloud?
Where an organisation already runs Microsoft 365 and Entra ID, Azure usually wins on identity integration and commercial alignment. AWS has the broadest managed service catalogue. Google Cloud is strong for data and analytics workloads. The existing estate and the team’s skills matter more than feature comparisons.
Do we need Kubernetes?
Often not. Managed container platforms such as Azure Container Apps or AWS ECS cover most workloads with a fraction of the operational burden. Kubernetes earns its complexity when you need fine-grained scheduling, a large service estate or portability across clouds.
Can you work alongside our internal team?
Yes. A common arrangement is that we establish the platform, pipelines and standards, work alongside your engineers through the first delivery cycles, and hand over documented systems they own.
How do you reduce cloud cost without reducing reliability?
By measuring first. Tagging and attribution show where the money actually goes; usually a small number of over-provisioned resources, idle non-production environments and unmanaged storage growth account for most of it. Those are addressable without touching production capacity.
Planning cloud engineering work?
Tell us about the systems involved and the constraints. We will come back with an architecture and a delivery sequence.