Recognisable signals
- Configuration differs per VM and is hard to reproduce
- Upgrades and deployments require manual server steps
- Local storage, databases or Elasticsearch obscure dependencies
- A new environment requires cloning and manual correction
Application modernisation
Move from VMs to containers without a big-bang migration
For estates where divergent VM configuration, manual upgrades and environments that are hard to reproduce block growth. Not every VM has to go and Kubernetes is not automatically the destination.
Containerisation adds value when it improves delivery, isolation and repeatability. Assessment identifies stateless workloads, local state, licences and a migration order that limits risk.
What does not need replacing by default
- VMs for workloads that are stable or hard to containerise
- Managed databases and storage with a mature recovery path
- Working delivery components that can extend into a container flow
- Existing network and security controls that are reusable
Application modernisation
Possible directions
Containerisation adds value when it improves delivery, isolation and repeatability. Assessment identifies stateless workloads, local state, licences and a migration order that limits risk.
Workload inventory covering state, storage, network and runtime
Containerise one representative low-risk application first
Choose managed containers or Kubernetes based on the whole estate
Treat databases, Elasticsearch and operators as separate migration decisions
Approach
From first picture to working improvement
- 01
First conversation
We establish the situation, fit and whether discovery is the right next step.
- 02
Paid discovery
We assess workloads, cloud resources, Terraform, delivery, security, state and operations.
- 03
Phased implementation
Deliverables, effort estimate and budget guardrails stay visible per phase.
- 04
Stabilisation and support
Go-live is followed by validation, handover and, where useful, bounded support.
Concrete output
- Workload and dependency matrix
- Containerisation and migration order
- Reference container and delivery path
- Runtime, state and recovery decision
- Phased estimate, runbooks and handover
Good fit when
- VM drift and manual work become structural risks
- multiple environments must be repeatable
- the team wants to learn and migrate in phases
Less suitable when
- the VM setup remains simple, stable and cheap to operate
- applications cannot be changed or tested
- a big bang without discovery is the only acceptable route
Relevant case
Bettr Group
Designing, building and improving secure cloud environments across multiple companies with different levels of maturity and platform needs.
FAQ
Frequently asked questions
Which workload goes first?
A representative stateless workload with clear dependencies and manageable risk, not necessarily the largest application.
Do databases move too?
Not automatically. Managed databases can stay; stateful containers need separate choices around storage, backups, recovery and upgrades.
Is Kubernetes required?
Only if multiple workloads, isolation, scale and automation justify the operational investment.
First conversation
Discuss your platform situation
Share the broad situation and trigger. The first conversation establishes fit and next step; detailed analysis follows as paid discovery.
Useful context
Do not share sensitive infrastructure details in this form.
- Current cloud and application landscape
- Main operational or growth bottleneck
- Relevant deadline, audit or customer requirement