Overview
Move it, automate it, then keep it up
Cloud migration on its own rarely delivers what was promised. Lifting servers into a hyperscaler without changing how they are built, deployed and watched usually produces the same operational problems at a higher monthly bill.
We treat the three together: get the workload onto the right platform, put the deployment pipeline and infrastructure code around it, and then run it to a reliability target you can actually measure. Each of those can be bought separately, but the value compounds when they are done as one programme.
Engagements run as a defined migration project, as embedded DevOps and SRE engineers on your team, or as an ongoing reliability retainer.
Typical scope
- Platforms
- AWS, Azure, GCP, private cloud
- Migration
- Assess, plan, cutover, verify
- Automation
- IaC + CI/CD pipelines
- Runtime
- VMs, containers, Kubernetes
- Reliability
- SLOs, on-call, incident review
- Cost
- Right-sizing and monthly review
Capabilities
What we deliver
Cloud migration
Workloads assessed, sequenced and moved with the data verified against source. Cutovers planned so the business keeps running through the transition.
- Discovery and dependency mapping
- Rehost, replatform or rebuild decisions
- Data migration with verification
- Cutover runbooks and rollback plans
Cloud foundations
The account structure, network, identity and guardrails that should exist before the first production workload lands — or retrofitted if it already has.
- Landing zone and account structure
- Network, VPC and connectivity design
- IAM roles and least-privilege access
- Tagging, budgets and policy guardrails
Infrastructure as code
Environments defined in version-controlled code, so staging genuinely matches production and a rebuild is a pipeline run rather than a recovery project.
- Terraform and cloud-native templates
- Configuration management
- Environment parity and reproducibility
- Drift detection
CI/CD pipelines
Build, test and deploy automated end to end, with the checks that stop a bad change reaching production — and the ability to roll it back in minutes if one does.
- GitHub Actions, GitLab CI, Jenkins, Azure DevOps
- Automated testing and quality gates
- Blue-green and canary releases
- Secrets management and artefact control
Containers & orchestration
Applications containerised and run on managed Kubernetes or simpler container platforms, sized to what your team can realistically operate.
- Docker packaging and registries
- EKS, AKS and managed Kubernetes
- Autoscaling and resource limits
- Helm charts and deployment manifests
Observability
Metrics, logs and traces in one place, with alerts that fire on user-visible symptoms rather than on every CPU spike at 3am.
- Prometheus, Grafana, ELK, Datadog
- Dashboards per service and per journey
- Alert tuning to cut noise
- Distributed tracing
Site reliability engineering
Reliability treated as an engineering target: agreed service levels, an error budget that governs release pace, and a blameless review after every incident.
- SLI and SLO definition
- Error budgets and release policy
- On-call rota and escalation design
- Incident response and postmortems
Resilience & disaster recovery
Recovery objectives agreed in writing, then tested — because a DR plan nobody has rehearsed is a document, not a capability.
- RTO and RPO definition
- Backup and replication design
- Failover testing and game days
- Runbook documentation
Cost optimisation
Monthly cloud spend attributed to the teams and services that generate it, with the obvious waste removed first.
- Right-sizing and idle resource cleanup
- Reserved and spot capacity planning
- Cost attribution by tag and team
- Monthly spend review
Migration
How a cloud move actually runs
Assess
Inventory of workloads, dependencies, data volumes, licences and current cost, with the constraints that will decide the sequence.
Design and plan
Target architecture, landing zone, migration waves and a cost model, agreed in writing before anything moves.
Pilot
A low-risk workload migrated first to prove the pattern, the pipeline and the cutover runbook on your real environment.
Migrate in waves
Production workloads moved in planned windows, with data verified against source and rollback available at every step.
Optimise and operate
Right-sizing once real load is visible, observability and alerting in place, and reliability targets handed to the team that will run it.
Toolchain
What we work with
Cloud
- AWS
- Microsoft Azure
- Google Cloud
- Private and hybrid cloud
Automation
- Terraform
- Ansible
- GitHub Actions, GitLab CI
- Jenkins, Azure DevOps
Runtime
- Docker
- Kubernetes, EKS, AKS
- Linux and Windows Server
- Managed databases
Observability
- Prometheus and Grafana
- ELK / OpenSearch
- Datadog
- Cloud-native monitoring
Platform and product names are the trademarks of their respective owners. Tooling is selected per engagement against your existing stack, team skills and budget.
Tell us what you need to get running.
Share the scope — a project, a team, or day-to-day support — and we will come back with people, timelines and commercials.