The cloud is reversible
DigitalCare moves workloads in either direction (hyperscaler to dedicated hardware, one cloud to another, on-premise to cloud) and operates them afterwards. Kubernetes is the portability layer; the decision is made on your numbers, not on a trend.
Move to dedicated
- Steady, predictable compute running near-continuously
- Large egress volumes
- GPU inference and other sustained accelerator load
- Data-gravity workloads: the storage is where the cost lives
- Anything under residency requirements
Why: At high utilization you are paying cloud rates for capacity you never stop using.
Keep in the cloud
- Spiky, seasonal or unpredictable demand
- Deep managed-service dependencies
- Global edge delivery
- Low-volume services where the operational overhead exceeds the bill
Why: Elasticity and managed services are worth real money when you actually use them.
Make portable first
- Anything welded to proprietary services
- Provider-specific IaC and identity assumptions
- Queues, storage and databases with open-source equivalents
Why: The work is Kubernetes, IaC and open-source equivalents. Done once, the choice stays yours later, in either direction.
This is the whole method: triage first, then a plan for each bucket. Not a generic five-step migration process.
Full cloud exit is rare. The common answer is hybrid, and sometimes it’s “stay where you are.”
The steady, predictable, high-volume workloads are the ones that pay for dedicated hardware. The bursty ones and the ones built on managed services usually should not move at all. Anyone who tells you to repatriate everything is selling a project, not an answer.
We model your workloads and your bill, and we tell you which is which, including when the honest recommendation is to change nothing. That recommendation has happened, and it will happen again.
Cost at high utilization
Dedicated capacity has a fixed price; cloud compute is priced per unit consumed. Above a certain steady utilization the fixed price wins.
Counterpoint: Below that line it loses, and you now own capacity planning. The crossover is arithmetic on your numbers, not a percentage from a blog.
Egress and data gravity
Moving data out is metered, and the more data accumulates the more expensive every option becomes, including leaving.
Counterpoint: The exit itself has a one-off transfer cost. We model it as part of the case rather than discovering it mid-migration.
Performance and hardware control
Dedicated hardware means no noisy neighbours, chosen CPU generations, local NVMe, and specific GPUs for inference.
Counterpoint: You also inherit failure domains, capacity lead times and lifecycle planning. That is operational work someone has to do.
Sovereignty and residency
An EU-only footprint, national requirements, documented data flows, and resilience against depending on one vendor control plane.
Counterpoint: Sovereignty is a set of concrete technical and contractual facts to be documented, not a label to adopt.
The half of this market nobody serves: where your data actually sits, and who can reach the control plane.
EU-only footprint
Compute, storage and backups in named European locations, with the jurisdiction of each component written down rather than assumed.
Dedicated hardware, named location
You know which datacentre your workload runs in, which provider operates the facility, and what the contract says about access.
No single control-plane dependency
Nothing critical relies on one hyperscaler identity, DNS or orchestration plane staying available to you.
Documented data flows for regulators
Diagrams and records showing what moves where, on which path, retained for how long: the artifact an assessor asks for. Security & Compliance Automation.
Resilience and continuity planning
Cross-provider standby, tested restores, and a documented backup jurisdiction, so continuity does not depend on one facility or one contract remaining available. Relevant for organizations operating under elevated infrastructure risk, stated in neutral operational terms.
Where a private LLM belongs
If inference data cannot leave the perimeter, the model runs inside it. That is the same hardware conversation as this page. Private LLM Deployment.
Not a numbered process. Five guarantees about method.
Reproducible before anything moves
The destination exists as code first. If it cannot be rebuilt from the repository, it is not ready to receive traffic. The current environment stays live and authoritative throughout: nothing is decommissioned on the strength of a plan.
Parallel run and validation
New and old run side by side and are compared on behaviour and performance, not just on whether the thing starts.
Gradual traffic, retained rollback
Traffic shifts in increments and the rollback path stays available until the destination is demonstrably stable.
Data migration rehearsed with restores
We rehearse the data move and test the restore: a backup job that has never been restored is not a plan.
Infrastructure work since 2012, including an exit from a metered external service.
Achieved for that client after moving to dedicated nodes we operate.
Held since a 4-hour rescue of a production cluster that kept failing.
An automated release path with rollback tested, so deploys stopped being events.
Results belong to the named client engagements, not to every project.
FAQ10 questionsCloseOpen
Is leaving the cloud actually cheaper?
It depends on utilization. Dedicated capacity is paid for whether you use it or not, so steady high-utilization workloads tend to win and bursty ones tend not to. We model your workload profile and your bill against dedicated, hybrid and staying put, then tell you which one wins, including when the answer is to stay.
What can’t be repatriated?
Anything whose value is the managed service: global edge delivery, deeply integrated serverless ecosystems, proprietary data and ML services with no equivalent, and workloads with genuinely spiky demand. Those normally stay where they are.
What about managed databases?
Usually the hardest decision. Self-hosting a database is entirely feasible and we do it, but it converts a bill into an operational responsibility. That responsibility can sit with us, which is often what makes the move viable.
Do we lose elasticity?
You lose infinite elasticity and gain predictable capacity. In practice most teams keep a cloud footprint for burst and edge while the steady base runs on dedicated hardware: hybrid, not exit, is the common outcome.
What hardware, and who owns it?
Dedicated servers or colocation, in a named location you choose, contracted either by you directly or through us. The infrastructure definition is yours as code either way, so the hosting decision stays reversible.
Can you move us between clouds instead?
Yes. Cloud to cloud uses the same method: make it portable, run in parallel, validate, shift traffic gradually, keep the rollback path until stable.
What if we want to move into the cloud?
Also yes: on-premise to cloud is still a large part of this work and the process is identical. We lead with exit because it is the direction almost nobody serves, not because it is always the right one.
Who runs it after the move?
Your choice: we operate it, with Metatron and Verdict carrying the daily load, or we hand it over with runbooks and training. See DevOps as a Service.
How long does it take?
It depends on how portable your workloads already are: the work is usually in the coupling to proprietary services, not in the move itself. The workload and cost review produces the plan and the sequence.
What happens to our existing infrastructure as code?
We build on it where it is sound and refactor where it is provider-locked. The goal is one reproducible definition of the destination environment that you own and can run without us.
