CAPABILITY // SRV-05

Cloud Infrastructure & DevOps Engineering

Infrastructure that scales with you, costs less than you think, and never becomes the reason your product is down.

Bad infrastructure is invisible when it works and catastrophic when it doesn't. The goal is to make it invisible permanently — auto-scaling for traffic spikes, self-healing for container failures, automatically deploying your code while running tests against it, and alerting your team before users notice a problem. We design cloud environments that are secure, cost-efficient, observable, and operated by code rather than manual configuration — so no single engineer's knowledge is the only thing standing between you and an outage.

10× faster deployments · 24/7 server monitoring
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WHAT'S INCLUDED
✓AWS and GCP architecture design, security hardening, and monthly cost optimisation
✓Docker containerisation with multi-stage builds, minimal attack surfaces, and image scanning
✓Kubernetes (EKS / GKE) cluster design, RBAC, network policies, and Helm chart management
✓Terraform and Pulumi infrastructure-as-code for fully reproducible environments
✓GitHub Actions and GitLab CI pipelines with test gates, security scans, and staged deploys
✓Prometheus metrics, Grafana dashboards, Loki log aggregation, and PagerDuty alerting
✓Database backup automation, point-in-time recovery, and documented disaster recovery runbooks
✓VPC design, IAM least-privilege policies, secrets management, and SOC 2-aligned controls
WHO THIS IS FOR

Built for teams that need results, not experiments.

Startups Pre-Scale
Moving from a single server or Heroku to a proper cloud setup that can handle 10× their current traffic without a rewrite and without a full-time DevOps hire.
Engineering Teams
Spending developer hours managing infrastructure instead of shipping product — needing their pipeline, monitoring, and deployment process professionalised so they can focus on features.
Companies Post-Incident
That have experienced a significant outage or security incident and need an independent audit and architecture overhaul to prevent recurrence.
AI & ML Workloads
Running GPU training jobs, high-volume inference endpoints, or large data pipelines that require specialised compute scheduling, cost visibility, and autoscaling.
HOW IT WORKS

From first call to production in clear steps.

01
Infrastructure Audit
We review your current cloud setup: architecture diagrams, IAM policies, security group rules, compute sizing, storage costs, network topology, and CI/CD configuration. We produce a written findings report categorised by severity (critical, high, medium, low) and estimated remediation effort.
02
Target Architecture Design
Based on your current state, your traffic patterns, your team's operational capabilities, and your budget, we design a target architecture. This includes compute strategy (ECS vs EKS vs serverless), database topology, CDN setup, secrets management, monitoring stack, and disaster recovery design. You review and approve this before any changes are made to production.
03
Infrastructure-as-Code Implementation
We write every resource in Terraform or Pulumi — never clicking through consoles. This means every change is version-controlled, every environment (dev, staging, production) is reproducible, and your infrastructure can be rebuilt from scratch in under an hour if needed. We structure modules to be reusable and maintainable by your own engineers after handoff.
04
CI/CD Pipeline Construction
We build the deployment pipeline end to end: lint, unit test, build, security scan, integration test, staging deploy, smoke test, and production deploy. Gates at each stage prevent broken code from advancing. Blue-green or canary deployments eliminate downtime for production releases. Rollback to the previous version is one click or one command.
05
Observability & Handoff
We instrument your services with Prometheus metrics (request rate, error rate, latency, saturation), set up Grafana dashboards for each service and the overall system, configure Loki for log aggregation, and define alert rules with meaningful thresholds. We document the runbooks for every alert — what it means, what to check, and how to resolve it — so on-call engineers are not guessing at 2am.
IN DEPTH

The details that separate good from great.

Infrastructure-as-code: why clicking in the console is technical debt

When infrastructure is created manually through AWS or GCP consoles, three problems accumulate over time. First, no one fully knows what exists — resources get created for experiments and forgotten, permissions get granted for urgency and never revoked. Second, you cannot reproduce the environment — rebuilding from scratch after a disaster, or spinning up a staging environment that actually mirrors production, becomes weeks of archaeology. Third, you cannot review changes — there is no diff, no approval process, no history. Terraform solves all three: every resource is declared in code, reviewed in a pull request, applied with a plan that shows exactly what will change, and stored in version control. We treat infrastructure code with the same engineering standards as application code.

Kubernetes: when you need it, when you don't

Kubernetes is powerful and genuinely right for certain workloads — multi-service architectures with independent scaling requirements, ML inference clusters that need GPU scheduling, teams deploying dozens of services with complex dependency graphs. It is also frequently over-applied. For most startups and mid-sized businesses with fewer than 10 services, AWS ECS (Fargate) or Cloud Run on GCP delivers 90% of the operational benefits of Kubernetes with a fraction of the operational complexity. We recommend Kubernetes only when the use case genuinely demands it, and we scope the additional operational overhead honestly in the proposal.

Cloud cost optimisation: the hidden leverage point

Cloud bills are almost universally 20 to 40% larger than they need to be. The most common sources of waste are: over-provisioned compute (instances sized for peak load running at 15% average utilisation), forgotten development resources (databases and instances from old projects still running), on-demand pricing where Reserved Instances or Savings Plans would cost 40% less, and inefficient data transfer patterns that generate unexpected egress charges. We do a cost audit as part of every infrastructure engagement and typically identify enough savings to cover a significant portion of our fee. We also implement tagging policies and budget alerts so cost anomalies surface within hours rather than at the end of the month.

FAQ

Questions we get asked before every project.

We already have infrastructure that works — why would we change it?
Sometimes you shouldn't. If your current setup meets your performance and reliability requirements, the engineering cost of migration may not be justified by the operational improvement. We always start with an honest audit. The cases where improvement is clearly justified are: infrastructure that is entirely manual (no IaC), with no monitoring or alerting beyond basic uptime checks, with no documented disaster recovery process, running significantly over-provisioned compute at 2× the necessary cost, or with security configurations that a basic audit would flag as critical. If none of these apply, we'll tell you.
Do you provide ongoing monitoring and incident response after setup?
Yes. We offer a managed DevOps retainer that covers ongoing monitoring, incident response with defined SLAs (typically 30-minute response for P1 incidents), monthly cost reviews, dependency and security updates, and proactive performance tuning. Retainer clients also get access to our infrastructure runbooks and direct Slack access to the engineers who built their systems.
How do you handle secrets and API credentials?
All secrets are stored in AWS Secrets Manager or GCP Secret Manager — never in environment variables, never in code, never in configuration files committed to source control. Secrets are accessed by services at runtime with IAM roles that follow least-privilege principles. We rotate secrets automatically where possible and document manual rotation procedures where not. For applications with CI/CD pipelines, secrets are injected at deploy time from the secrets manager and never stored in the pipeline's environment variables.
Can you help with GDPR compliance or SOC 2 preparation in our cloud environment?
Yes. We have worked with companies preparing for SOC 2 Type II audits and GDPR compliance programmes. For SOC 2, we implement the technical controls required by the Common Criteria — access control, encryption at rest and in transit, audit logging, vulnerability management, and change management via IaC. For GDPR, we implement data residency controls, access logging, encryption, and deletion workflows. We produce the documentation artefacts that auditors and assessors require.
What cloud providers do you work with?
Primarily AWS and GCP. We have deep experience with AWS (ECS, EKS, RDS, S3, CloudFront, Lambda, IAM, VPC, Route 53, Secrets Manager, CloudWatch) and GCP (Cloud Run, GKE, Cloud SQL, Cloud Storage, Cloud CDN, IAM, Secret Manager, Cloud Monitoring). We also work with Vercel and Fly.io for specific use cases. We are cloud-agnostic in our Terraform modules — when a client has existing infrastructure on a provider, we work with it rather than forcing migration.
How long does a cloud infrastructure project take?
An infrastructure audit with a written findings report typically takes 1 to 2 weeks. A full infrastructure build — VPC, ECS or EKS cluster, RDS, CI/CD pipeline, monitoring stack, IaC — typically takes 3 to 6 weeks depending on the number of services and the complexity of the network design. A migration from an existing setup to a new architecture is typically scoped separately after the audit, since the timeline depends heavily on what needs to move and what can be rebuilt.
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