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Cloud Infrastructure & DevOps

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The analytics and AI work we do has to run somewhere reliable. We design, migrate and operate cloud infrastructure on GCP, AWS and Azure — including the server-side tagging, data platform and AI workloads we build — with infrastructure as code, CI/CD, observability, security controls and cost management treated as part of the build rather than an afterthought.

Challenges we solve

Infrastructure that grew organically has a recognisable shape: resources created by hand in a console, no record of why, environments that differ in ways nobody documented, secrets in places they should not be, a bill that goes up every month for reasons nobody can fully explain, and one engineer who is the single point of failure for all of it. None of that is unusual and none of it is unfixable. Codify what exists, separate environments properly, put deployment behind a pipeline, add observability so failures are visible before users report them, and attach cost to teams so the bill becomes a decision rather than a surprise.

What we deliver

Cloud architecture and landing zones

Account and project structure, network design, identity and access management, organisation policies, logging and billing structure — the foundations that are painful to retrofit and straightforward to get right early.

Migration and modernisation

On-premises to cloud, cloud to cloud, and lift-and-shift to managed services, with dependency mapping, wave planning, parallel running, cutover runbooks and rollback plans. Migrations fail on planning far more often than on technology.

Infrastructure as code

Terraform or the platform-native equivalent, with modules, remote state, environment separation, plan review in pull requests and drift detection — so infrastructure changes go through the same review as application changes.

CI/CD and release engineering

Pipelines in GitHub Actions, GitLab CI, Cloud Build or Azure DevOps: automated testing, artefact management, staged environments, approval gates, blue-green or canary release and reliable rollback.

Containers and serverless

Kubernetes (GKE, EKS, AKS) or serverless (Cloud Run, Lambda, Container Apps) depending on what the workload actually needs — including the server-side GTM, pipeline and AI workloads we build for you.

Data hosting and warehouse operations

Hosting and operating warehouses, lakehouses and streaming infrastructure: capacity, backup and restore, disaster recovery, data residency and the operational runbooks that make on-call survivable.

Observability and reliability

Metrics, logs, traces and dashboards; alert design that avoids fatigue; SLOs and error budgets; and an incident process with runbooks and post-incident reviews.

Security, compliance and cost management

Least-privilege IAM, secret management, network policy, vulnerability scanning, encryption and audit logging — plus FinOps: tagging, showback, rightsizing, committed-use planning and anomaly alerts on spend.

Platforms & Tooling

  • Google Cloud
  • AWS
  • Azure
  • Terraform
  • Kubernetes
  • Cloud Run
  • Lambda
  • GitHub Actions
  • GitLab CI
  • Tealium
  • Cloud Build
  • Datadog
  • Grafana

Process

Week 1–2

Assess

Inventory, architecture review, cost and risk baseline.

Week 3–4

Design

Target architecture, migration waves, security model.

Week 5–12

Build

IaC, pipelines, platform services, observability.

Week 13–14

Migrate

Wave cutover with parallel running and rollback ready.

Ongoing

Operate

Monitoring, optimisation and reliability improvements.

FAQs

Probably not. Most analytics and marketing workloads run perfectly well on Cloud Run, Lambda or Container Apps with a fraction of the operational burden. Kubernetes earns its complexity when you have many services, real scaling requirements and a team to run it. We will push back if it is being chosen for the CV rather than the workload.

Yes, and it is one of the most common reasons clients come to us for infrastructure. Server-side tagging is a real production service with real traffic, scaling and cost characteristics — it deserves proper infrastructure rather than a container spun up by hand and forgotten.

Wave planning with dependency mapping, parallel running so both environments are live and reconciled, traffic shifted gradually, and a rollback plan tested before cutover rather than written during it.

Tagging and attribution first — you cannot control what you cannot attribute. Then the usual suspects: idle and oversized resources, storage class and lifecycle policies, egress patterns, and unoptimised warehouse queries. Baseline first so the saving is provable.

Usually, yes. We tend to take a specific workstream — the data platform, the tagging infrastructure, the AI workloads — and work to your existing standards and pipelines rather than introducing a parallel way of doing things.

Your team runs it. Everything is in code and version control, with runbooks, on-call guidance and enablement sessions. If you want us to keep operating it, that is a managed arrangement we agree explicitly rather than a dependency we leave behind.

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