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An AI assistant is planned. Until it is genuinely useful we would rather point you at the page that actually answers your question.
Cloud work covers three distinct jobs that often get bundled together: designing infrastructure for a new system, moving an existing system off owned hardware or another provider, and reducing the cost of infrastructure already running.
They need different approaches. New design is mostly about choosing appropriate managed services. Migration is mostly about sequencing and risk. Cost work is mostly measurement — finding where spend does not correspond to value.
Cloud bills grow quietly. Instances provisioned for a launch peak stay running, environments created for a test are never deleted, and data transfer between zones accumulates. It is common to find a meaningful share of spend attached to nothing anyone uses.
The architectural argument is elasticity. Workloads with variable demand — seasonal retail, batch processing, anything with a daily peak — are expensive on fixed capacity, because you buy for the peak and idle the rest of the time.
The honest counter-argument: steady, predictable workloads are sometimes cheaper on dedicated hardware. We will say so when the numbers support it.
Our approach
We size infrastructure against measured load, not a reference architecture. Most systems need far less than they are given. Starting smaller with autoscaling headroom is cheaper and produces better information than provisioning for an imagined peak.
We define everything in code. Manually configured infrastructure cannot be reliably reproduced, reviewed or rolled back, and the knowledge lives in one person's memory. Terraform means environments are identical and changes go through review like application code.
We migrate in stages with a rollback that stays viable. Moving a whole estate in one weekend is how migrations become incidents. Component by component, with the ability to revert each step, takes longer on paper and is faster in practice because nothing has to be undone under pressure.
Capabilities
Compute, storage, networking and data services chosen for the workload and the team who will operate them.
Staged movement from on-premise or another provider, with rollback preserved at each step.
Terraform definitions so environments are reproducible and changes are reviewable.
Finding unused resources, right-sizing instances and applying commitment discounts where usage justifies them.
Redundancy and backup designed against a stated recovery objective, with restores actually rehearsed.
Network boundaries, least-privilege access, encryption and secrets management as part of the build.
Stack
Process
Measuring current load, spend and failure points before proposing anything.
A design with the cost implications stated, so trade-offs are visible before commitment.
Defining the target in Terraform and standing up a non-production environment from it.
Moving component by component, each with a tested rollback and a validation step.
Right-sizing against real usage once the workload is running in its new home.
Runbooks, alerting and training so your team can operate and change it confidently.
Use cases
Where a refresh cycle is due and the capital cost no longer makes sense against elastic capacity.
Where spend has grown without a corresponding increase in usage and nobody owns the total.
Where current infrastructure will not absorb expected load and the failure point is known.
Where a contractual uptime requirement exceeds what the current setup can support.
Outcomes
FAQ
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