As AI moves from experiment to production, the infrastructure question gets sharper: should you buy GPUs, rent them as a service, or run in public cloud? There is no universal answer — the right choice depends on utilization, workload type, budget structure and where your data is allowed to live. This article breaks down the trade-offs for teams making that call.
CAPEX vs OPEX: the economics come first
Buying GPUs on-premises is a capital expense: a large upfront investment that amortizes over the hardware's life. It is economical only if you keep those GPUs busy. Renting — via GPUaaS or public cloud — is an operating expense: you pay per hour of use, trading a higher per-hour rate for zero upfront cost and no idle risk.
The pivot point is utilization. A GPU cluster running near-continuously (say 70%+ around the clock) usually favours ownership. Spiky or exploratory workloads favour consumption pricing, because idle owned GPUs are pure sunk cost.
Utilization and the hidden cost of idle silicon
GPU accelerators are expensive and depreciate fast. An owned cluster sitting at 15% utilization is one of the worst outcomes in AI infrastructure. Before buying, model realistic utilization across your team — including nights, weekends and between projects — not the peak you hope for.
VRAM: the constraint that decides feasibility
For modern models, GPU memory (VRAM) is often the binding constraint, not raw compute. Large language models and high-resolution vision models need enough VRAM to hold the model (and, for training, optimizer states and activations). If a model does not fit, you are forced into multi-GPU sharding, which adds complexity and interconnect requirements. Match VRAM per GPU (and interconnect like NVLink) to the models you actually intend to run.
Training vs inference: two different problems
Training is bursty, massively parallel and VRAM-hungry — a strong fit for on-demand, high-end GPUs you can spin up for a run and release. Inference is continuous, latency-sensitive and typically needs less memory per request — a strong fit for steady, cost-optimized capacity close to your users. Many teams train in a flexible environment and serve inference on dedicated, right-sized infrastructure.
Data residency and sovereignty
AI runs on data, and that data is often sensitive. If your training or inference data includes personal data of people in Turkey (KVKK) or the EU (GDPR), where the GPUs physically sit is a compliance decision, not just a performance one. Global public-cloud GPU regions may place data outside your permitted jurisdiction. GPUaaS from a provider with in-country data centers lets you access accelerated compute while keeping data resident locally.
The three models, side by side
- On-premises GPU — maximum control and, at high sustained utilization, the best unit economics; heavy CAPEX, procurement lead times and you own the operations.
- GPUaaS — dedicated or reserved GPU capacity from a provider; OPEX model, faster to start, and can keep data in-country — a strong middle ground for regulated or capacity-constrained teams.
- Public cloud — vast elasticity and the widest instance catalogue; excellent for spikes and global reach, but watch egress costs, long-run price and data-residency limits.
How Cloud Expert helps
Cloud Expert offers GPU-as-a-Service and hybrid AI infrastructure on Turkey-located, KVKK-aligned data centers — so you can run training and inference on accelerated hardware without sending regulated data abroad. We help you model utilization, size VRAM to your models and split training from inference cost-effectively. Contact us to design AI infrastructure around your workloads and budget.