Skip to content

Cloud and DevOps

Migrate to Google Cloud or AWS, automate the deploys, and get monitoring that tells you something. On your server, ours, or both.

Built in Lahore. 6 tools in production.

What we run

Google Cloud and AWS

The two platforms we run in production, including AWS HealthLake for clinical data. We pick the cheaper one for your workload and say why.

Deployment you choose

On your own server, hosted by us, or split between the two. Polaris, our own product, runs both ways for the 19 businesses on it, and client systems get the same choice.

Security and monitoring

Encryption at rest and in transit, network isolation, scoped IAM, audit logging, and dashboards that alert a person instead of filling a log file.

A typical engagement

Four to eight weeks for most migrations. The assessment in the first phase is what sets the real dates, and we revise them out loud if the estate is larger than it looked.

  1. Week 1-2Assessment and planWe read your current setup, measure what it costs to run, and write the target architecture down before touching anything.
  2. Week 3-4MigrationWorkloads move behind a cutover plan with a tested rollback. Traffic switches when the new stack has already been serving a copy of it.
  3. Week 5-6Pipelines and monitoringDeploys become one merge. Dashboards, alert thresholds, and on-call routing go in at the same time, not afterwards.
  4. Week 7-8Tuning and handoverRight-sizing, reserved capacity where it pays, and enough runbook and training that your team can operate it without us.

Work we get asked for

Four shapes cover most of it. If yours is none of them, the call is still free.

Cloud migration

Move off on-premise boxes or legacy hosting. We handle assessment, planning, the move itself, and the cost work after it.

  • Workload assessment
  • Cutover with tested rollback
  • Cost optimisation
  • Performance tuning

CI/CD pipelines

Get from a merged commit to production without anyone SSHing into a server. Tests, staging, and a rollback path that has been used at least once.

  • GitHub Actions and GitLab CI
  • Automated test gates
  • Blue-green deploys
  • Automated rollback

Kubernetes and containers

Run containerised applications at a size that justifies the operational cost. If a single VM is the right answer, we will tell you that instead.

  • GKE, EKS and AKS
  • Helm charts
  • Service mesh with Istio
  • Autoscaling rules

AI and ML infrastructure

The infrastructure under model workloads: serving, GPU capacity, vector storage, and the pipelines that retrain and redeploy.

  • GPU instance management
  • Model serving on vLLM and TGI
  • Vector database hosting
  • Training pipeline setup

The stack we reach for

LayerTools
Cloud platformsGoogle Cloud, AWS, Azure, DigitalOcean, Hetzner
Infrastructure as codeTerraform, OpenTofu, Pulumi, AWS CDK, CloudFormation
ConfigurationAnsible, Packer
ContainersDocker, GKE, EKS, AKS, Helm, Istio
MonitoringPrometheus, Grafana, Datadog, cloud-native logging
AlertingPagerDuty, on-call rotation routing

What you get handed over

  • Terraform or CloudFormation for every resource, in your repository, under review
  • Dashboards and alert thresholds set for your traffic, not left on defaults
  • A hardening pass with the compliance evidence written down
  • A runbook your team can follow at 3am without calling us

What we will not claim

We do not publish an uptime number, because the number that matters is the one your cloud provider contracts for and the one your architecture actually earns. We will write both down for your setup during the assessment.

Autoscaling, alerting and cost controls are configured for your traffic and reviewed after the first month of real load.

Questions we get asked

Send us your current bill and architecture.

We will read it and come back with what we would change and what we would leave alone. Reply within same working day. If your setup is already fine, that is what we will say.

Book a call