The part that decides whether any of it stays up
Cloud architecture, delivery pipelines and operations are part of every engagement, not a separate line item. A system that cannot be deployed safely, observed honestly and paid for predictably is not finished, however good the model is.
Four clouds, chosen per workload
AWS is where most of our production work runs and where our infrastructure and CI/CD delivery has been done. Azure, Google Cloud and DigitalOcean are in use where the client is already there, the managed services fit better, or the budget makes a smaller platform the sensible answer.
AWS
Primary. VPC design, managed databases, object storage and CDN, secrets, autoscaling and cost control.
Microsoft Azure
Where the client is already there. Managed services, identity integration and enterprise networking.
Google Cloud
Where the managed AI and data services fit the workload better than the alternatives.
DigitalOcean
Where the budget makes a smaller, simpler platform the sensible answer for a first product.
Assess, architect, migrate, secure, automate, monitor
The same six stages whether the system is a first deployment or a platform that is already carrying load.
Cloud architecture
Network and VPC design, managed databases, object storage and CDN, secrets management, and the boundary between what stays private and what is exposed. Built on the platform that suits the workload rather than the one we know best.
CI/CD pipelines
Build, test and deploy on every merge. GitHub Actions, GitLab CI or Bitbucket Pipelines, with staged environments, migrations run in order, and a rollback that has been rehearsed.
Containers and runtime
Docker and Docker Compose for parity between a laptop and production, Kubernetes or a managed container service where the scale earns it, Nginx in front. The same image is promoted through each environment.
Observability
Centralised logs, metrics and alerting, with the AI layer instrumented like everything else: token spend, latency, retry counts, refusal rate and grounding failures on the same dashboard as CPU and error rate.
Infrastructure as code
Environments defined in version control rather than assembled by hand in a console, so a second region or a fresh staging stack is a pull request and not a fortnight of archaeology.
Scaling and cost
Autoscaling, caching and queue design sized to real traffic, and a running view of what the platform and the model providers actually cost. AI workloads make cost a design constraint, not an invoice you discover later.
Put your infrastructure in front of us
Sixty minutes on what runs, what it costs, and what would fail first. No charge, and the notes are yours either way.