Next-Generation Compute Solutions
Flexible, high-performance compute platforms supporting virtualisation, containers, analytics and AI with automation and lifecycle management.
Why it matters
Modern compute workloads don’t fit into a single mould. Your organisation probably runs traditional virtualised applications running on older operating systems. At the same time, teams are building containerised microservices and Kubernetes clusters. You’re likely running analytics workloads that spike unpredictably. And increasingly, AI and machine learning systems demand GPU acceleration and different operating assumptions altogether. Most organisations try to force all of these into a single legacy compute architecture, and the result is inefficiency – platforms over-provisioned for some workloads and under-powered for others. Bringing new workload types online takes months of planning. Capacity expansion is expensive and slow.
The complexity mounts quickly. Each workload type has different performance requirements, scaling behaviours and lifecycle needs. Supporting all of them on fragmented platforms means operating multiple independent systems, multiple teams, multiple skill sets. You’re managing infrastructure overhead that has nothing to do with business value.
SCC designs compute platforms that handle this diversity natively. We combine high-performance hardware with software-defined orchestration and automation to create systems that support traditional virtualisation, Kubernetes clusters, GPU-accelerated workloads and analytics, all within a single unified platform. You get flexibility – each workload gets infrastructure suited to its characteristics – plus operational simplicity. Your teams operate one system, not many. Deployment accelerates because infrastructure isn’t the limiting factor. Lifecycle services keep everything optimised as your workload mix evolves.
How it works
Step 1
Map your current compute environment
We understand what’s running where: virtualised applications (what operating systems, what versions), containerised workloads, analytics jobs, AI workloads. What’s working well? Where are the pain points – performance, scaling, operational overhead?
Step 2
Design the unified compute platform
Based on your analysis, we design a platform that supports your workload mix natively. This might emphasise traditional virtualisation, containerisation, GPU acceleration or a balanced mix. The design includes hardware selection, orchestration tooling and scaling policies.
Step 3
Build and test the platform
We deploy the physical infrastructure – servers with appropriate CPU, memory and accelerators. We configure orchestration platforms (vSphere, Kubernetes, or both) and automation tooling. Everything is tested against your workload profiles before handover.
Step 4
Migrate workloads to the unified platform
We help move workloads from fragmented environments into the new system. Some applications migrate unchanged. Others benefit from optimisation – refactoring to containers, or adjusting resource requirements based on actual measured demand rather than historical over-provisioning.
Step 5
Operate and optimise
Post-deployment, we monitor platform utilisation, help your teams optimise workload placement and scaling, and evolve the infrastructure as your application mix changes. Lifecycle services ensure continuous optimisation.
Partners
We partner with leading compute infrastructure and orchestration vendors to deliver platforms supporting the full range of modern workload types.
Market-leading hypervisor and virtualisation platform for traditional workloads. VMware remains the standard for enterprise virtualisation; we integrate it smoothly alongside containerisation and GPU workloads.
Standard container orchestration platform. Kubernetes is the de facto standard for containerised microservices; our platforms are designed for production-grade Kubernetes at scale.
High-performance compute servers and systems. HPE infrastructure provides the foundation for unified compute platforms; we design around HPE strengths to optimise across workload types.
Flexible server infrastructure supporting traditional virtualisation, containerisation and specialised workloads. Dell’s breadth of server options – from dense CPU platforms to GPU-accelerated systems – enables unified architecture.
GPU compute and acceleration. NVIDIA hardware powers specialised workloads (AI, analytics); we design unified platforms where GPUs sit alongside CPU-based infrastructure.
Enterprise cloud infrastructure and orchestration. OpenStack provides the management and automation layer for unified compute platforms serving cloud-native workloads.
Awards and accreditations
SCC holds cloud and infrastructure accreditations that validate expertise in cloud strategy, architecture and delivery.
VMware and Kubernetes expertise
Certification and partnership recognition for delivering enterprise-grade virtualisation and Kubernetes platforms at scale, including complex migrations and multi-workload environments.
Enterprise compute architecture
Our experience designing compute platforms for large enterprises – supporting multiple workload types, high availability and complex scaling requirements – informs our approach to flexibility and reliability.
Containerisation and microservices delivery
We’ve led major containerisation programmes, moving traditional applications to Kubernetes and designing cloud-native architectures. That experience shapes how we design platforms that support both traditional and containerised workloads.
GPU infrastructure and acceleration
Experience integrating GPU compute into broader platforms for analytics, AI and data processing informs our approach to hardware diversity in unified platforms.
Capacity planning and right-sizing
We help organisations avoid infrastructure bloat by designing systems with visibility and intelligent scaling. Our track record includes significant cost savings through better capacity planning.
Lifecycle and continuous optimisation
We don’t hand over infrastructure and disappear. Managed services including performance monitoring, capacity planning and evolutionary support keep platforms optimised as workload mix changes.
Stop managing multiple compute platforms. Start managing one
Supporting diverse workload types on fragmented infrastructure creates operational overhead and limits your agility. Let’s discuss what a unified compute platform could look like for your organisation and how it would change your ability to deploy and scale.
FAQs
Can a single platform really handle both traditional virtualisation and Kubernetes containers efficiently?
Yes. Modern compute platforms use software-defined orchestration to allocate resources based on workload characteristics, not infrastructure type. Traditional VMs and Kubernetes containers often run on the same physical servers, with orchestration managing placement and resource limits. The key is starting with platforms designed for that flexibility – not simply bolting containers onto virtualisation infrastructure or vice versa.
What’s the migration path if we currently run separate virtualisation and Kubernetes clusters?
We design a unified platform that runs both simultaneously. Usually, physical infrastructure is consolidated under a single orchestration layer. Workloads gradually migrate as teams become comfortable with the new environment. Some traditional applications may never containerise, and that’s fine, they keep running on virtual machines. The point is one platform supporting both, not two separate systems.
How do we avoid GPU infrastructure sitting idle when workloads aren’t running GPU-heavy analytics or AI?
Intelligent orchestration allocates GPU resources dynamically. When GPU workloads are idle, orchestration can allocate GPU capacity to batch processing or analytics that doesn’t strictly require GPUs. You define policies about GPU priority – some workloads get guaranteed GPU access; others use GPUs when available. This maximises utilisation without forcing compromises.
What happens when our workload mix changes? Do we need to redesign the whole platform?
No. Well-designed unified platforms are built for evolution. If you begin running more GPU workloads, you add GPU-accelerated nodes to the cluster. If you shift more workloads to containers, you add Kubernetes capacity. The underlying orchestration adapts. We help with capacity planning and evolution as part of ongoing lifecycle services.
How much will a unified compute platform cost compared to running separate infrastructure?
Usually less. You’re consolidating hardware rather than maintaining separate systems. Operational staffing drops because you’re managing one platform instead of many. And better utilisation – intelligent scaling eliminates over-provisioning. We help model costs specific to your current environment and proposed unified platform.