DevOps & Cloud Automation
Reproducible infrastructure, safe deployments and the observability to know when something breaks.
Most infrastructure problems are not hardware problems — they are consistency problems. Environments that were clicked together, deployments that require a specific person, configuration drift between staging and production, a cloud bill that nobody can explain. Infrastructure-as-code, automated pipelines and a well-designed container layer eliminate each of these systematically.
We set up the platform layer that engineering teams usually skip when they are moving fast: Terraform-provisioned environments reproducible from a single command, CI/CD pipelines where a merged pull request triggers a tested, staged deployment, container orchestration that behaves identically in every environment, and observability that alerts on the things that matter before a user notices. Then we document it on your accounts and hand it to your team.
What this covers
Cloud architecture design and provisioning on AWS, GCP or Azure
Infrastructure-as-code with Terraform: modules, remote state and environment parity
CI/CD pipeline setup in GitHub Actions or GitLab CI: test, build, deploy, rollback
Containerisation with Docker and Kubernetes orchestration (EKS, GKE, AKS or self-managed)
Structured logging, metrics collection, distributed tracing and uptime monitoring
Alerting design: what to wake someone up for and what to log and review in the morning
IAM least-privilege, secrets management with Vault or AWS Secrets Manager, network segmentation
Deliverables
- Your application live on cloud infrastructure provisioned entirely with Terraform
- An automated CI/CD pipeline from pull request to production with a staging gate
- Infrastructure-as-code repository with full documentation and runbooks
- Observability stack: logs, metrics, traces and alerting configured from day one
What we build with
DevOps & Cloud Automation
DevOps & Cloud Automation at the sub-service level is the hands-on work of wiring together the platform layer: cloud accounts provisioned as immutable IaC, CI/CD pipelines that go from commit to production without human gatekeeping, and container orchestration that behaves identically in every environment. It is the boundary below product engineering and above SRE — it owns the delivery mechanism and the infrastructure topology, not the application code and not the SLO framework (that lives in the broader cloud-devops engagement).
The scope ends at a deployment that is reproducible, auditable and recoverable. Reproducible means any engineer can provision a fresh environment from source control in under thirty minutes. Auditable means every infrastructure change is a reviewed pull request with a Terraform plan attached. Recoverable means a blue-green or canary rollout can be halted and reversed in under five minutes without a war room.
Delivery Lifecycle
The five stages below follow the order in which changes flow from code to production. Each stage produces a concrete, version-controlled artefact — not a slide deck.
- Account / project layout: production, staging, and ephemeral preview accounts with separate state backends
- VPC and subnet design: CIDR allocation, NAT gateway placement, private subnet routing for workloads
- Terraform module boundaries: define the public contract (inputs, outputs) for the network, compute, and secrets modules before writing implementations
- Remote state configuration: S3 + DynamoDB lock or GCS backend with environment-specific workspaces
- Break-glass IAM role with MFA and session recording — the only path to direct console access
- Reusable GitHub Actions workflows per workload type: Node.js, Python, Docker-based service
- Build caching strategy: layer caches for Docker builds, dependency caches for package managers
- SBOM generation at build time (BuildKit --sbom flag) and container image signing with Cosign
- Static analysis, dependency vulnerability scanning (Trivy), and policy checks (Conftest) as blocking CI steps
- Artefact promotion policy: image tags are immutable SHA digests; mutable 'latest' is forbidden in production registries
- GitOps configuration: Argo CD ApplicationSets or Flux Kustomizations per environment, with auto-sync to staging and manual approval gate to production
- Canary rollout configuration via Argo Rollouts: step percentages (10 % / 40 % / 100 %), analysis interval, and the Prometheus metric that determines pass or fail
- Blue-green swap for stateful services or schema migrations where a canary population would cause split-brain on the data layer
- Rollback automation: analysis failure triggers an automatic abort and revert to the prior stable revision within the sync interval
- Ephemeral preview environments per pull request: created on open, promoted to named URL, destroyed on merge
- Node pool design for managed Kubernetes (EKS / GKE / AKS): spot instance node group with on-demand fallback via Karpenter disruption budget
- Horizontal Pod Autoscaler (HPA) configuration: CPU/memory triggers for baseline services; KEDA ScaledObject for queue-depth-driven workloads
- Pod Disruption Budget and topology spread constraints to maintain availability during node drain or zone failure
- Resource request and limit calibration from VPA recommendation — not ad-hoc guesses — to prevent OOM evictions and over-provisioning waste
- Cost guardrails: Infracost comment on every IaC pull request; namespace resource quotas to prevent a single team from monopolising cluster capacity
- Vault dynamic database credentials for Postgres and MySQL: lease duration matched to workload restart interval
- IRSA (AWS) or Workload Identity (GCP) for pod-level cloud API access — no static access keys in environment variables
- External Secrets Operator syncing Vault paths into Kubernetes Secrets, with automatic version sync on lease renewal
- Network policy baseline: deny-all default, explicit allow rules per service pair, enforced by Calico or the cloud-native NetworkPolicy controller
- CIS Benchmark check as a Terraform plan gate via tfsec or Checkov — non-compliant plans are a CI failure, not a review comment
Environment Baseline
Establish the cloud account structure, network topology and IaC module boundaries before a single pipeline is wired. Decisions made here determine every blast radius and every rollback path downstream.
CI Pipeline Engineering
Build the build-and-test pipeline as a versioned, testable artefact. The pipeline is not exempt from code review; its configuration lives in source control and is treated like application code.
CD & Progressive Delivery
Wire the deployment path from a merged pull request to a running production workload, with a progressive delivery gate that blocks promotion when the canary population is burning its error budget.
Compute & Autoscaling
Provision and configure the compute substrate that workloads run on, with autoscaling policies that eliminate both idle over-spend and cold-start latency spikes under bursty traffic.
Secrets & Access Hardening
Eliminate long-lived static credentials from the system before day-one hand-off. Every secret is dynamic, scoped, and rotated on a defined schedule.
How we'd choose
There's rarely one right answer — these are the trade-offs we weigh before recommending an approach.
Managed Kubernetes (EKS / GKE) vs Serverless Containers vs PaaS
Compute substrate determines operational overhead, cold-start behaviour, autoscaling granularity, and how much of the platform team's time goes to infrastructure versus product enablement. Use this guide before committing to a platform — migrating later is expensive.
| Criterion | Managed K8s (EKS / GKE / AKS) | Serverless Containers (Fargate / Cloud Run) | PaaS (Render / Railway / Heroku) |
|---|---|---|---|
| Operational overhead | Medium — control plane is managed; node pools, CNI, CSI, and ingress controller are team responsibility; requires K8s expertise on-call | Low — no node management; task or container runtime is fully managed; networking and scaling are abstracted away | Very low — platform manages runtime, networking, TLS and scaling; engineering effort is near-zero for standard workloads |
| Cold-start latency | Sub-second with pre-warmed pods; Karpenter node provisioning adds 60-90 s for new nodes, mitigated by overprovisioner daemonset | 1-10 s for Fargate task start; Cloud Run sub-second with minimum instances set > 0; provisioned concurrency eliminates cold starts at a cost premium | Near-instant for running dynos/services; initial deploy takes 30-120 s; no in-request cold start for persistent process models |
| Autoscaling granularity | Fine-grained: HPA on CPU/memory/custom metrics; KEDA on queue depth, Prometheus, or cron; Karpenter bins-packs workloads onto cheapest available instance shape | Concurrency-based scaling to zero; burst scaling is near-instant; no custom metric triggers without additional plumbing; minimum instance floor avoids cold starts | Scales on request volume or CPU via platform heuristics; limited tuning surface; minimum instance count is the primary cost lever |
| Multi-tenancy & namespace isolation | Namespace-level isolation with RBAC, NetworkPolicy, and resource quotas; strong with policy discipline; right substrate for a self-service IDP | Task-level isolation; no shared-state risk between containers; no concept of namespace or team-level quota without custom tooling | Application-level isolation only; no granular resource quotas; not suited for multi-team self-service without separate accounts per team |
| Progressive delivery support | First-class with Argo Rollouts: canary weights, blue-green swap, analysis gates against Prometheus or Datadog; rollback is automatic on gate failure | Traffic splitting via weighted ingress (Cloud Run revisions, ALB weighted target groups); no built-in analysis gate; rollback is manual traffic shift | Blue-green between deployments via platform toggle; no canary weight control; rollback is one-click but without automated analysis |
| Cost model at scale | Economical above ~10 pods with Spot/Preemptible nodes; fixed cluster baseline cost; Karpenter reduces waste by right-sizing nodes to workload shape | Pay-per-vCPU-second with no cluster baseline; cheaper at low-to-medium load; expensive above the density threshold where Kubernetes idle cost amortises | Fixed per-service tier pricing; predictable; lacks the density advantages of container scheduling; costs scale linearly with service count |
Failure Modes to Avoid
Mutable image tags in production
Tagging images as 'latest' or with a branch name and deploying by tag means the image a deployment references can change without a deployment event. A rollback points to the same tag but the registry may serve a different digest than the one that was live before the incident. Reproducing the exact production state for debugging becomes impossible, and the audit trail — which commit is actually running — breaks entirely.
Mandate immutable SHA-digest tags for all production images and enforce this at the registry level by disabling overwrites on the production repository. The GitOps manifest references a digest, not a tag. Mutable tags are allowed in development registries only, and the promotion step between registries requires a tag-to-digest resolution that is recorded in the pull request.
Configuration drift between staging and production
Staging environments that differ from production in resource limits, environment variables, or infrastructure topology consistently produce deployment surprises: a canary that passed staging analysis fails in production because the request rate, the instance type, or a third-party endpoint differs. The staging gate exists to give confidence — but a drifted staging environment gives false confidence, which is worse than no gate at all.
Declare both environments from the same Terraform module with environment-specific variable overrides and nothing else. Ephemeral preview environments use the same Helm values file as staging. Infracost diffs are posted on every pull request so size differences between environments are a deliberate, documented choice rather than accumulated divergence. Terraform plan output for both environments is posted as a CI comment before merge.
Manual rollback under pressure
When a bad deployment reaches production and the rollback path is a kubectl set image command issued by whoever is on-call, the outcome depends on that person knowing the previous image tag, having cluster credentials on their machine, and executing correctly under pressure at 2 AM. Even when it works, nothing about the incident is automatically recorded — the cluster state after recovery differs from what the GitOps repository declares, which means the next deployment re-introduces the bad version.
Rollback must be a GitOps operation: a git revert of the offending commit, merged via an expedited pull request, which the reconciler applies within the sync interval. For time-critical incidents, Argo Rollouts abort-and-revert runs automatically when an analysis gate fails, without a human touching kubectl. The post-incident state of the cluster is always reconcilable from the repository — no out-of-band kubectl commands leave undeclared state behind.
Common questions
Our deployment process is a manual SSH session — how painful is it to modernise?
Less painful than most teams expect. We audit what you have, containerise the application, set up the pipeline incrementally alongside the existing process, and cut over once the automated path is proven. The team keeps shipping throughout; there is no freeze.
We are on a single cloud provider today — will you lock us in further?
No. We use portable tooling — Terraform, Docker, Kubernetes — that runs on any cloud. We will discuss whether multi-cloud is genuinely valuable for your workload or just added complexity; most early-stage products are better served by going deep on one provider rather than thin across three.
Related capabilities
Have something in mind?
Tell us what you're building or stuck on. The first consultation is free — no obligation, no hard sell.