Infrastructure you can reproduce, and pipelines you can trust.
Everything defined as code, every environment reproducible, and cost attributable to an owner from the first deployment. Console-clicked infrastructure is how estates become unmaintainable and bills become unexplainable.
- First question
- What can your team operate?
- Second
- What can you hire for locally?
- Third
- What does the existing estate already run?
- Rarely decisive
- Benchmarks and framework fashion
The right technology is the one you can still run in five years.
We are deliberately multi-cloud in capability and single-cloud in advice: pick the platform your organisation can operate and go deep on it. The cost of running three clouds badly far exceeds the benefit of avoiding one vendor.
Where portability matters we keep workloads in containers and infrastructure in Terraform, so a future move is an engineering project instead of a rewrite.
Every engagement is delivered with your engineers in the repository from the first week, so the knowledge accrues internally, not leaving when we do. You own the code, the pipelines and the documentation regardless of how the engagement ends.
If you are weighing a decision and want a second opinion instead of a proposal, we are happy to give one.
AWS
The broadest service catalogue and the deepest operational maturity. Our default recommendation where there is no existing platform commitment.
- Landing zones, multi-account structure and guardrails
- ECS, EKS and Lambda workload hosting
- RDS, Aurora and DynamoDB data platforms
- Cost optimisation, tagging and budget alerting
Microsoft Azure
Usually the correct answer for Microsoft estates, where Entra ID, Enterprise Agreement licensing and Power Platform integration make the total cost materially lower.
- Azure landing zones and policy-driven governance
- App Service, AKS and Azure Functions
- Entra ID identity and managed identity throughout
- Integration with Power Platform and Microsoft 365
Google Cloud
Strong for data-intensive and analytics workloads, and for teams already committed to BigQuery or Google's machine learning tooling.
- GKE and Cloud Run workload hosting
- BigQuery data platforms and analytics pipelines
- Vertex AI model training and serving
- Organisation policy and project structure
Docker & Kubernetes
Containers for consistency between environments; Kubernetes only where the operational complexity is justified. Plenty of estates are better served by managed container services.
- Containerising existing applications
- EKS, AKS and GKE cluster design and operation
- Helm charts, GitOps and progressive delivery
- Honest advice on when Kubernetes is overkill
Terraform & Ansible
Infrastructure as code without exception. Every environment reproducible from a repository, every change reviewable in a pull request.
- Terraform modules and remote state management
- Importing existing hand-built infrastructure into code
- Ansible configuration management and patching
- Policy as code and pre-merge plan review
CI/CD pipelines
Build, test, security scan and deploy on every merge. A release that requires a runbook and a weekend is a design problem, not a discipline problem.
- GitHub Actions, Azure DevOps, GitLab CI and Jenkins
- Automated testing and security scanning in the pipeline
- Blue-green and canary deployment patterns
- Environment promotion with approval gates
Need engineers on your own team instead?
Named individuals you interview, on monthly rolling terms. Each of these has a page explaining what separates a good hire from an average one in that stack.