On-Premise to Cloud Migration
Moving off your own hardware with the scope modelled before you commit, workloads moved in stages, and a rollback that stays available throughout.
Lift and shift is a step, not a destination
Most cloud migrations that disappoint on scope were lift and shift treated as the end state. Moving a virtual machine sized for a five-year hardware purchase onto an on-demand instance of the same size, running continuously, produces a bill larger than the datacentre it replaced. That is not a cloud problem.
Lift and shift is still often the right first move, because it decouples leaving the datacentre from re-architecting. The mistake is stopping there and then being surprised.
So we model target-state scope before anything moves, including reserved capacity, autoscaling and storage tiering, and we say plainly which workloads will genuinely be cheaper and which are moving for resilience or compliance instead.
Teams that start here often pair it with monolith to serverless migration, MySQL to PostgreSQL migration and ML model deployment.
Cloud migrations rarely fail technically. They disappoint commercially, because nobody modelled the steady-state bill before the hardware was sold.
Where cloud migrations go over scope
Four patterns behind most disappointing outcomes.
Instances provisioned to match hardware bought for a five-year peak, running on demand around the clock.
Data transfer effort are excluded from the business case and then become one of the largest recurring lines.
Development and test environments run continuously because there is no schedule, quietly scopeing as much as production.
Backups are configured and never rehearsed, so the first real restore attempt happens during an incident.
What a cloud migration covers
A modelled business case, then a staged move you can reverse.
Every workload inventoried with utilisation data, and a target-state scope model covering compute, storage, egress and licensing.
Accounts, networking, identity, guardrails and logging built as infrastructure as code before any workload arrives.
VPN or Direct Connect so on-premise and cloud run as one network during the transition rather than as two islands.
Workloads moved in dependency order, lowest risk first, each with a tested rollback to on-premise.
Instances sized against observed utilisation after migration, with reserved capacity applied once patterns are established.
Monitoring, alerting, backup and rehearsed restores, plus runbooks handed to your team.
From inventory to datacentre exit
Twelve to twenty-four weeks, with the business case settled first.
Workload inventory with real utilisation data, dependency mapping and a target-state scope model.
Accounts, networking, identity and guardrails as code, reviewed by your security team before anything migrates.
One low-risk workload moved end to end to prove connectivity, deployment, monitoring and rollback.
Workloads moved in dependency order with verification and rollback at each wave.
Right-sizing and reserved capacity applied against observed usage, with the bill compared to the model.
The stack behind our cloud migrations
Everything as code, in your accounts.
AWS or GCP chosen against your workloads, licensing and existing skills.
Declared as code from the first account so environments are reproducible.
Hybrid networking so both sides operate as one network during transition.
Monitoring, backup and restore rehearsed before the datacentre is switched off.
What this looks like in production
A business case that changed shape once it was modelled
A manufacturer with two ageing datacentres faced a hardware refresh and assumed cloud would be cheaper. The initial like-for-like model showed it would scope roughly forty percent more, which was not the answer anyone wanted.
Utilisation data explained why: production servers averaged eleven percent CPU, sized for a peak that occurred twice a year. Right-sized instances with autoscaling and reserved capacity for the steady base changed the model to a modest saving, and the resilience gain was the stronger argument anyway.
Migration ran in six waves over five months. The disaster recovery test that followed took ninety minutes, against a documented on-premise objective of two days that had never actually been tested.
Cloud vs on-premise: what the comparison should actually cover
We produce a target-state scope model before anything moves, and we tell you which workloads will not be cheaper. Resilience is often the better argument anyway.
We move one slice at a time behind a router, with both systems live, so every step is small and every step is reversible.
A working demo and a written note every Friday covering what shipped, what slipped and what it means for the date. No status theatre.
Nothing goes live in one jump. We run in parallel or behind a flag until the numbers say it is safe to switch over.
