Research · AI Infrastructure & Operations

AI Compute & Cloud Platforms

AI runs on specialized compute, usually GPUs and other accelerators, delivered through cloud platforms, specialized providers or your own data centers. Where AI runs affects cost, performance, data control and resilience.

Options for running AI

• Model APIs: call models hosted by providers or cloud platforms. No infrastructure to manage.
• Managed AI platforms: cloud services for building, fine-tuning and deploying models with integrated security and monitoring.
• Rented accelerator capacity: GPU instances from hyperscalers or specialized GPU cloud providers.
• Own infrastructure: accelerators in your data center or colocation facility.

Decision factors

• Data sensitivity, residency and regulatory requirements.
• Workload type: training, fine-tuning or inference.
• Volume and predictability of demand.
• Latency requirements and proximity to users and data.
• Skills to operate specialized infrastructure.
• Power and cooling limits for on-premises accelerators.

Why it matters

AI compute can be expensive and, at times, hard to obtain. Inference usually becomes the larger ongoing cost as adoption grows. Dense accelerator racks need far more power and cooling than traditional servers, which limits what many existing facilities can host.

Common pitfalls

• Buying capacity ahead of proven demand.
• Committing to one platform with no path to move workloads.
• Underestimating inference costs at scale.
• Assuming existing facilities can support accelerator density.

How to get started

• Separate forecasts for training and inference demand.
• Compare API, managed platform, rented and owned options on total cost.
• Define which data classes may run on which platforms.
• Design for portability through model gateways and open formats.