DevOps Infrastructure Build for a
Series A SaaS Startup
Manual deployments, no CI/CD, and a cloud bill that was out of control
A Series A SaaS startup with a growing engineering team was operating without a formal DevOps function. Deployments were manual, taking 3–4 hours per release and requiring a senior engineer to be available. Infrastructure was provisioned manually in the cloud with no IaC, no environment parity, and a monthly cloud bill that had tripled in six months with no explanation. The CTO needed to fix the foundation before the team scaled further.
- Manual deployments taking 3–4 hours, requiring senior engineer involvement every time
- No staging environment — features tested directly in production before release
- Cloud infrastructure provisioned manually with no Terraform or IaC — no reproducibility
- Cloud costs had grown 3× in 6 months; no tagging, no cost allocation, no visibility
- No container orchestration — Docker containers run manually on single VMs with no restart policy
Infrastructure as code, CI/CD pipeline, and container orchestration from the ground up
Infrastructure audit and cost analysis
Full audit of existing cloud resources. Identified £8,000/month in unused or oversized resources. Tagging strategy implemented for cost allocation by service and environment.
Terraform IaC migration
All cloud infrastructure codified in Terraform. Existing resources imported into state. Separate workspaces for dev, staging, and production environments — identical infrastructure, environment-specific variables.
CI/CD pipeline build
GitHub Actions pipeline built for automated testing, Docker image build, push to registry, and deployment to Kubernetes. Full pipeline run takes 8 minutes. Rollback to previous image available in under 2 minutes.
Kubernetes cluster deployment
Managed Kubernetes cluster deployed. All workloads containerised and migrated. Horizontal pod autoscaling configured — infrastructure scales with load rather than running at peak capacity 24/7.
Observability stack
Prometheus and Grafana deployed for metrics, Loki for log aggregation. Alerting configured for error rate, latency, and resource thresholds. On-call runbooks documented for the engineering team.
Stack
(Name withheld at client request)
Outcomes at 90 days post-implementation
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