Cloud & DevOps Services

Infrastructure that matches the software

A great application running on amateur infrastructure gets paged incidents, surprise bills, and slow, manual deployments. We build the infrastructure layer from the same engineering discipline we apply to the application — typed, tested, version-controlled, and observable.

This matters more the moment something goes wrong. Infrastructure that's been hand-configured through a cloud console has no audit trail, no easy rollback, and no way to reproduce the exact environment in staging before you push a fix to production. Infrastructure as code gives you all three, and it's the difference between a five-minute rollback and a multi-hour incident.

The infrastructure decisions that are expensive to reverse later

Some infrastructure choices are cheap to change later; others quietly lock you in for years. Which cloud provider and region, whether the database is genuinely portable or tied to a proprietary managed service, how tightly the deployment pipeline is coupled to one vendor's tooling — these get harder to unwind the longer a system runs on top of them. We flag which decisions are reversible and which aren't at the point they're made, so you're choosing convenience with your eyes open rather than discovering the lock-in during a renewal negotiation three years later.

What we deliver

• Cloud architecture & migration — AWS, Azure, GCP right-sizing and landing-zone setup
• Infrastructure as Code — Terraform or Pulumi; every resource version-controlled, auditable, reproducible
• CI/CD pipelines — GitHub Actions, GitLab CI, or Bitbucket Pipelines with test gates, security scanning, and zero-downtime deploys
• Kubernetes & container orchestration — EKS, AKS, GKE; HPA, pod budgets, multi-region failover
• Observability stack — metrics, logs, traces (Grafana, Prometheus, Loki, Tempo or Datadog) with meaningful alerts, not noise
• Security hardening — VPC design, IAM least-privilege, secrets management (Vault, AWS SSM), WAF, DDoS mitigation
• MLOps pipelines — model training pipelines, feature stores, model registry, A/B serving infrastructure
• Cost optimisation — reserved instance planning, spot fleet management, right-sizing audits

Our DevOps engagement types

Greenfield infrastructure (new project)

We architect the cloud environment in parallel with application development — landing zone, CI/CD, monitoring, and deployment pipeline all production-ready on day one of go-live.

Infrastructure audit & remediation

We review your existing cloud setup for security gaps, cost waste, and reliability risks. Deliverable: a prioritised remediation plan with estimated savings and implementation timeline.

DevOps retainer

Ongoing platform engineering — incident response, dependency patching, capacity planning, and shipping infrastructure improvements alongside your application team.

What "production-ready" actually means to us

A lot of infrastructure work looks finished the moment the application is reachable at a URL. We hold it to a higher bar before calling it done: automated rollback if a deploy fails health checks, alerting that pages a human only for issues that need one, backups that have actually been restored in a drill rather than just configured, and a runbook your on-call engineer can follow at 3am without needing to have built the system themselves. Infrastructure that only the original engineer understands is a liability the moment that engineer is unavailable.

Reliability engineering, not just uptime monitoring

Uptime dashboards tell you a service is responding; they don't tell you it's healthy. We build observability around the metrics that actually predict incidents — error rate trends, saturation on the resources most likely to become a bottleneck, and latency percentiles rather than just averages, since an average can look fine while a meaningful share of your users are having a slow experience. When something does break, the goal is a fast, boring recovery guided by a runbook, not a scramble through logs at 2am trying to remember how the system was wired together.

Cloud cost is an engineering problem, not just a finance one

Most cloud overspend isn't a pricing problem — it's an architecture problem. Instances sized for peak load that run at peak all month, storage that never moves to a cheaper tier, dev environments left running over the weekend. We treat cost the same way we treat performance: something with a target, a dashboard, and an owner, not a line item finance flags once a quarter.

• Reserved instance planning — multi-year commits sized to your actual baseline load, not a guess
• Spot / preemptible workloads — batch jobs, dev environments, and ML training moved to discounted spot capacity where interruption is tolerable
• Right-sizing — automated weekly reports on underutilised resources so oversized instances get caught before they've cost you a year
• Storage tiering — S3/Blob lifecycle policies moving infrequent data to cheaper tiers automatically

Where infrastructure connects to the rest of your build

Infrastructure decisions are easiest to get right when they're made alongside the application, not retrofitted after launch — which is why we scope custom software and cloud work together whenever the timelines allow. Security hardening here overlaps directly with our cybersecurity practice, and if you're deploying AI workloads, the MLOps pipelines described above are usually scoped alongside our AI development service rather than as a separate engagement. If you're earlier in the process and comparing cloud providers or planning a migration, our cloud migration checklist and DevOps practices that actually reduce incidents posts are a good starting point.

Questions buyers actually ask