We design, build, and operate the cloud foundations that production software runs on. AstraCloud Labs is a UK project-delivery consultancy, not a staffing agency: we take ownership of an architecture or a platform problem and deliver it. The work spans AWS and GCP architecture, Kubernetes, infrastructure as code, CI/CD pipelines, observability, and cloud cost optimisation (FinOps). Clients are usually teams that have outgrown a hand-managed setup, are migrating workloads, or need a senior engineer to take their infrastructure from proof-of-concept to something they can run with confidence.
What we deliver
Each engagement is scoped around a concrete outcome rather than a rented seat. The capabilities below are the ones we are most often brought in for, and they tend to overlap: a Kubernetes migration usually arrives with Terraform, pipeline, and observability work attached.
AWS and GCP architecture
Cloud architecture design and review on AWS and GCP, including serverless designs on AWS Lambda, networking, account and project structure, and the tradeoffs between managed services and self-hosted components.
Kubernetes and EKS
Container platforms on Kubernetes and EKS with Docker and Helm: cluster topology, workload packaging, autoscaling, and the operational guardrails a team needs to run them day to day.
Infrastructure as code
Reproducible infrastructure with Terraform, CloudFormation, and AWS SAM. Environments are defined in code, reviewed like application changes, and applied through pipelines rather than the console.
CI/CD pipelines
Build, test, and deploy pipelines with GitHub Actions, GitLab CI, or Jenkins, wired to the deployment model the platform actually uses so releases are repeatable and low-drama.
FinOps and cost optimisation
Cloud cost work using right-sizing, autoscaling, caching, request batching, and (for AI workloads) model-tier optimisation, so spend tracks usage instead of drifting upward.
Observability
Metrics, traces, and logs with OpenTelemetry and Prometheus, plus event monitoring, so the people running the system can see what it is doing before users tell them.
How we work
The approach is the same whether the engagement is a greenfield platform or a rescue. We optimise for what the system does in production, not for what looks neat in a slide.
Principles we hold to
- Production-first: we design for the failure modes, on-call burden, and operational cost of running the system, not just the happy path of a demo.
- Cost and reliability together: right-sizing and autoscaling are decided alongside redundancy and recovery, because cutting spend at the expense of reliability is a false saving.
- Infrastructure as code by default: every environment is defined in Terraform, CloudFormation, or SAM and changed through review and pipelines, so there is no undocumented state living in someone's console session.
- Observability from the start: instrumentation with OpenTelemetry and Prometheus is part of the build, not a thing bolted on after the first incident.
What you are left with
We hand back infrastructure your own team can understand and operate. That means readable IaC, pipelines that document the deployment process, dashboards that mean something, and a clear record of the decisions behind the design. The goal is to make ourselves unnecessary, not to create a dependency.
Selected work
For Convex Insurance, we built a serverless admin platform on AWS Lambda and Python, with role-based access control and Terraform managing the infrastructure as code. Real-time event monitoring and automated Slack channel provisioning were part of the same delivery, so the operational tools shipped with the platform rather than trailing behind it.
For VisitBritain, the work was a MACH-based e-commerce architecture with NestJS backend-for-frontend layers in front of serverless AWS Lambda microservices. The cloud and DevOps decisions there were about keeping a distributed serverless estate deployable and observable as it grew.
What these engagements have in common
- Serverless AWS architectures designed around the workload rather than a default template
- Terraform-managed infrastructure as code, reviewed and applied through pipelines
- Operational concerns (access control, monitoring, provisioning) treated as part of delivery, not an afterthought
Where this connects
Cloud infrastructure rarely arrives on its own. If the system you are building is an agent or LLM product, the infrastructure and the cost model are tightly coupled, which is the territory of our AI agent systems & LLM products work. If the problem is the services running on top of the platform, that sits with platform & backend engineering. The two often run as one engagement.
On the cost side, the same FinOps techniques we apply to general cloud spend (caching, request batching, right-sizing, model-tier choices) apply directly to inference-heavy workloads. We wrote up the specifics of that in our notes on LLM cost optimisation, which is a good starting point if cloud bills are what brought you here.
