Workload demand is uncertain
Training, tuning, retrieval, and inference have different utilization patterns. Early estimates can lead to idle capacity or constrained production services.
Cloud and Compute
Cloud, Infrastructure & AI Compute
Infrastructure for the Intelligent Enterprise
AI workloads need cloud architecture, data platforms, compute planning, observability, and automation that can support both experimentation and production demand.
Service Overview
What this service enables
Enterprise AI workloads place different demands on compute, storage, networking, latency, resilience, security, and cost. Cloud and AI infrastructure should be designed around those workloads and the data they use.
Business and Technical Challenges
What prevents useful AI from reaching operation
Data and compute are separated
Moving large or sensitive data can affect latency, cost, privacy, and architecture choices across cloud and on-premise environments.
Operations lack observability
Teams need a consistent view of performance, reliability, capacity, security, and spend across platforms and model services.
The Opportunity
Where technology creates practical value
Workload-led infrastructure planning helps organizations place AI services where performance, control, resilience, and economics make sense. Automation and observability then make those environments repeatable and operable as demand changes.
Architecture
How the system connects
AI Infrastructure Architecture
Data
Compute
Models
AI Services
Enterprise Applications
Infrastructure for AI-Driven Operations
We design cloud and platform foundations for enterprise AI: resilient, observable, automated, and aligned to the workloads they need to run.
Cloud architectureHybrid cloudMulti-cloudCloud migrationCloud optimisationInfrastructure automationKubernetesInfrastructure as CodePlatform engineeringObservabilityPerformance optimisationAI compute architecture
AI Compute
Infrastructure matched to model workloads
Training, tuning, retrieval, and inference place different demands on compute, storage, networking, latency, and cost. The right architecture starts with the workload and the data it needs.
01
Accelerated compute
Plan GPU and other accelerator capacity around model size, utilization, scheduling, and the economics of each workload.
02
Data and compute placement
Keep data movement, locality, privacy, and network throughput visible when choosing cloud, hybrid, or on-premise deployment.
03
Reliable deployment
Use repeatable infrastructure, monitoring, access controls, and recovery practices so services remain observable in production.
04
Scale with evidence
Measure latency, throughput, resilience, and cost before expanding capacity or changing deployment patterns.
Practical Use Cases
Apply the capability to a defined workflow
AI compute planning
Estimate accelerator, storage, network, and scheduling needs for a defined training or inference workload before committing capacity.
Hybrid AI platform
Place data and workloads across cloud and on-premise environments according to locality, latency, control, and support needs.
Platform engineering
Create repeatable infrastructure, deployment paths, identity, monitoring, and service ownership for application teams.
Infrastructure observability
Connect performance, capacity, incidents, and cost signals so teams can investigate and plan with one operating context.
Delivery Approach
From operating problem to maintained capability
- 01
Profile workloads
Define data, model, latency, throughput, availability, security, and growth assumptions.
- 02
Evaluate placement
Compare cloud, hybrid, on-premise, and edge options against operational and commercial constraints.
- 03
Engineer the platform
Implement repeatable infrastructure, identity, networking, storage, orchestration, observability, and recovery.
- 04
Measure and optimize
Review utilization, latency, reliability, incidents, and cost before scaling or changing architecture.
Business Outcomes
Value the organization can operate
- Infrastructure matched to actual AI workloads
- More repeatable deployment and recovery
- Visible capacity, performance, and cost
- A controlled path to scale compute
Why Turbo AI
Strategy connected to production engineering
Workload-first planning
Architecture begins with the model, data, user, and service requirements rather than a preferred platform.
Infrastructure and application context
We connect compute decisions to data engineering, AI delivery, security, and operational ownership.
Evidence before scale
Capacity and optimization decisions use measured utilization, performance, resilience, and cost.
Technology Ecosystem
Selective, practical technology alignment
These names identify relevant technologies and frameworks. They do not imply formal partnerships or certifications.
AWS
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Microsoft Azure
Google Cloud
NVIDIA
KubernetesConnected Work
Explore related capabilities and applications
Frequently Asked Questions
Cloud, Infrastructure & AI Compute questions
Answers to common questions about scope, implementation, controls, and operational fit.
How do you choose between cloud and on-premise AI compute?
Compare data locality, privacy, latency, utilization, scalability, available skills, resilience, and total operating cost for the specific workload.
Does every AI workload need GPUs?
No. Compute requirements depend on model size, training or inference, throughput, latency, and available optimized services. Profiling should precede procurement.
What is included in AI infrastructure observability?
Relevant measures can include service availability, latency, throughput, accelerator utilization, storage and network behaviour, capacity, cost, and security events.
How does DCI 360 relate to this service?
DCI 360 focuses on data-centre infrastructure visibility. Cloud and AI compute services address the wider architecture and operation of workload platforms.
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Prepare your infrastructure for enterprise AI.
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