05 · Cloud Engineering
Cloud infrastructure that is boring in the best way.
We design and run cloud environments for data-heavy and spatial workloads on AWS, Azure and Google Cloud. Serverless where it simplifies, containers where it doesn't and infrastructure as code throughout.
- AWS
- Azure
- GCP
- Cloud-native architecture
- CI/CD
- Infrastructure automation
- Observability
- Scalable deployments
What we build
Cloud Engineering, in practice.
Cloud-native architecture
Serverless APIs, managed databases and container platforms chosen per workload, with clear boundaries between services.
Infrastructure as code
Reproducible environments for development, staging and production; no hand-configured servers.
CI/CD
Build, test and deployment pipelines with environment promotion, secrets management and rollback.
Observability
Logs, metrics, traces and alerts that tell you what broke and why, before users do.
Spatial & data workloads
Tile servers, raster processing and pipeline compute sized for bursty geospatial and analytics jobs.
Cost & security posture
Least-privilege access, network boundaries and cost monitoring built into the environment from the start.
How we work
Engineering principles.
- AWS Lambda
- API Gateway
- Amazon RDS
- Azure
- Google Cloud
- Docker
- GitHub Actions
Managed where possible
We prefer managed services that remove operational burden over self-hosted components.
Everything in code
Infrastructure, pipelines and policies are versioned and reviewed like application code.
Measure before scaling
Capacity decisions are based on observed load, not guesses.
Start a conversation
Bring us the hard problem.
Tell us what you are trying to build and the constraints you are working within. We will come back with how we would approach it.