Unified Multi-Cloud Management
One control plane across every cloud, regulated data kept in country.
Multi-Cloud Platform is a single control plane over everything you run: your own datacentres, private clusters, public cloud regions and edge sites. It federates them into one fleet behind Kubernetes-native APIs, so a workload is described once and placed wherever policy allows it to run. Nothing has to be re-architected to join the fleet, and nothing proprietary is needed to leave it.
Running several clouds without a single policy is a compliance exposure before it is an operational one: no unified view of where data sits, what it costs, or what happens if a provider becomes unavailable. Multi-Cloud Platform turns placement into a governed decision, holding regulated workloads on domestic infrastructure while elastic demand bursts to cheaper capacity elsewhere. That gives you a defensible answer to concentration-risk questions and a cost picture your finance team can act on.
Architecture
Residency rules, jurisdiction constraints, approval gates and an audit trail that records where every workload was allowed to run and why.
A single operational view across every registered cluster, public and private, driven through standard Kubernetes APIs rather than a proprietary console.
A propagation and override engine that decides which cluster runs what, adapting registries, storage classes and resource limits per site automatically.
GitOps pipelines with phased and canary rollout across clusters at once, and rollback that needs no manual cross-cloud coordination.
Active-active sites, cross-region disaster recovery and automatic cross-cluster failover, built into the platform rather than added afterwards.
The member clusters themselves, spanning sovereign datacentres, public cloud regions and edge nodes on x86 and ARM.
Register every cluster you own or rent and operate them as one fleet, with consistent identity, policy and observability across all of them.
Declare where a workload may and may not run, and let the scheduler enforce it rather than depending on a runbook being followed correctly.
Pin regulated data and the services that touch it to domestic infrastructure, with the constraint enforced at scheduling time and recorded for audit.
Scale out to additional clusters and providers when real demand arrives, then release the capacity when it passes.
Live spend broken down by cluster, cloud and workload, with cost-aware scheduling that steers eligible work to the most economical compliant capacity.
Active-active across two cities and cross-region recovery, so the availability you commit to in a contract rests on architecture rather than a procedure.
Phased and canary releases roll out to every cluster from one pipeline, with fast rollback and change records suitable for regulated change management.
Standard Kubernetes APIs and open components mean moving a workload between providers is a scheduling decision, not a rebuild project.
What you end up with
You end up with a governed fleet: one control plane over your own datacentres and any public cloud, placement rules that hold regulated data in country, spend you can see per workload, and the practical ability to move any workload elsewhere without rebuilding it.
Infrastructure as a Service
Virtual machines and containers on one control plane.
Platform as a Service
The cloud-native operating system for your estate.
AI Platform as a Service
Your complete AI factory, inside your own boundary.
Kubernetes as a Service
Production Kubernetes, operated by us, owned by you.
GPU Platform as a Service
Turn the accelerators you already own into a metered service.
High-Performance Computing
Simulation and AI on one cluster, inside your own borders.
Continuity and Resilience
Recovery you have proven, not recovery you have promised.
Cloud to Edge
Run every site from one console, even when the link drops.
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