How AI and Cloud Computing work together

AI workloads place demands on infrastructure that traditional cloud environments were not originally designed to meet. As UK organisations move from AI experimentation into production, the relationship between AI and cloud computing, where workloads run, how data moves, and how performance and compliance are maintained, becomes a practical infrastructure question.

This FAQ covers how AI and cloud computing work together, and what that means for how infrastructure needs to be designed and placed.

Q: How do AI and cloud computing work together?

A: Cloud computing provides the compute, storage and network infrastructure that AI workloads need to function at scale. AI models require significant processing power to train and run, and cloud environments make that capacity available without requiring organisations to own and manage the underlying hardware. In practice, AI and cloud computing work together at every stage of an AI lifecycle: storing and preparing training data, running model training, deploying models for inference, and monitoring performance over time. The specific cloud environment chosen, whether public, private or hybrid, shapes how that process performs and how much control the organisation retains.

Q: Why does AI depend on cloud infrastructure?

A: AI workloads are computationally intensive and generate large volumes of data. Running them on standard on-premise hardware is often impractical due to the processing power required, the cost of GPU-accelerated servers, and the difficulty of scaling capacity up and down as workload demands change. Cloud infrastructure provides on-demand access to the compute resources AI needs, alongside the connectivity to move data between systems at speed. For inference workloads specifically, where a trained model analyses and responds to data in real time, the location and performance of the cloud infrastructure directly affects how quickly the AI can respond.

Q: What types of AI workloads run in the cloud?

A: The two main categories are training and inference. Training involves processing large datasets to build or refine a model, which requires substantial compute capacity over a defined period. Inference involves running a trained model against new data to generate outputs, which happens continuously and is highly latency-sensitive. Large-scale training workloads often suit hyperscale public cloud environments due to their raw compute capacity. Inference workloads, which need to respond quickly and operate close to end users or data sources, are well suited to regional edge cloud infrastructure. Pulsant's AI inferencing platform is designed specifically for inference at the regional edge, supporting low-latency AI deployment across the UK.

Q: What infrastructure does AI need from a cloud environment?

A: GPU-accelerated compute is the most frequently cited requirement, as AI model training and inference benefit significantly from parallel processing. Beyond raw compute, AI workloads need high-speed, low-latency connectivity to move data quickly between storage and processing layers. Storage capacity and throughput matter for training datasets. For production inference deployments, reliability and uptime are critical since AI services are often customer-facing. Security controls, access management and audit capabilities are increasingly important as AI handles sensitive data. Pulsant's infrastructure combines GPU-accelerated IaaS with Edge Fabric connectivity to support the full set of requirements for production AI workloads.

Q: Why does latency matter for AI workloads in the cloud?

A: Latency, the time it takes for data to travel between systems, directly affects the responsiveness of AI applications. For inference workloads that process requests in real time, such as customer-facing AI services, fraud detection or operational analytics, even a 10-millisecond delay can affect performance and user experience. The further data has to travel between the point of generation and the cloud infrastructure processing it, the higher the latency. This is one of the key reasons regional edge cloud infrastructure is better suited to inference than centralised data centres located far from the end user or data source.

Q: How does edge computing support AI in the cloud?

A: Edge computing brings cloud infrastructure closer to where data is generated and where AI outputs are consumed. Rather than routing all data to a centralised facility, edge data centres process AI workloads in regional locations, reducing latency and keeping data within a defined geography. For inference AI, this proximity is operationally significant. Pulsant's platformEDGE network of 14 UK data centres is designed to support this model, placing AI inferencing infrastructure close to regional user bases and connecting sites via a private 100Gbps network. This enables organisations to deploy AI at the edge without sacrificing connectivity or resilience.

Q: What are the security considerations for running AI in the cloud?

A: AI workloads often process sensitive or proprietary data, making security a central infrastructure concern. Key considerations include where data is stored and processed, who can access it, how it is transmitted between systems, and what audit controls exist. In cloud environments, shared infrastructure introduces additional questions around data isolation and access boundaries. Private or sovereign cloud infrastructure reduces these risks by providing dedicated environments with clearly defined security controls. For more detail on cloud security specifically, see Pulsant's cloud security FAQ.

Q: How does data sovereignty affect AI cloud deployments in the UK?

A: Data sovereignty requires that data is processed and stored within a defined jurisdiction, subject to its laws. For AI workloads, this matters because model training and inference both involve processing data, often at scale and in real time. If that processing happens on infrastructure outside the UK, sovereignty obligations may be breached. UK organisations in regulated sectors, or those handling sensitive customer or operational data, need to confirm that the cloud infrastructure running their AI workloads is physically located in the UK and governed under UK law. Pulsant's IaaS platform and colocation facilities are fully UK-owned and operated, making them well suited to AI deployments where sovereignty is a requirement.

Q: When does AI in the cloud make more sense than on-premise AI infrastructure?

A: On-premise AI infrastructure gives organisations maximum control but requires significant upfront investment in GPU hardware, power, cooling and ongoing maintenance. Cloud-based AI infrastructure shifts those costs to an operational model and allows capacity to scale with workload demand. Cloud deployments also benefit from managed connectivity, security and support, reducing the internal skills burden. On-premise may still make sense for organisations with very specific security requirements, highly predictable workloads that justify capital investment, or existing hardware that can be extended. For most UK organisations, a hybrid model, using regional cloud or colocation for inference and potentially hyperscale for large training runs, provides the best balance of performance, cost and control.

Q: How should UK organisations choose a cloud environment for AI workloads?

A: Start with the workload type. Training workloads with large compute requirements may suit hyperscale environments for burst capacity. Inference workloads running continuously and serving regional users benefit from low-latency regional infrastructure. Then consider sovereignty: if data must remain in the UK, confirm the provider's infrastructure is UK-based and independently verified. Assess the connectivity between the cloud environment and where your data originates, since poor connectivity undermines AI performance. Finally, evaluate support, transparency on costs, and whether the provider has experience with AI-specific workloads.

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