AI infrastructure cost and GPU sizing
What in-house AI costs, and the volume at which it pays off.
Overview
Lindstead sets the total cost of ownership of self-hosted AI against current API spend, and finds the monthly volume at which own infrastructure becomes cheaper.
- ResultA cost model comparing APIs, European cloud and owned infrastructure.
- InputCurrent bills, expected volume, performance needs and operational assumptions.
Key questions
- What would our AI workloads cost on our own GPUs?
- Buy servers, rent European cloud GPUs or stay with an API?
- At what monthly volume does self-hosting become cheaper?
- How many GPUs, of which type, do our models need?
Approach
- Workload and volume analysis Token volumes per use case, peaks included, so capacity follows real demand.
- Infrastructure sizing GPU type and count per model, for owned servers, European cloud and colocation.
- Cost model and break-even Hardware, energy, hosting, staff and utilisation against three years of API pricing, every assumption visible.
Deliverables
- Infrastructure sizing GPU type, count and hosting option per workload.
- Cost model and break-even Three-year TCO and the break-even volume.
- Buy, rent or API advice Advice per workload, tested against price and volume changes.
Frequently asked questions
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Only at high, steady utilisation or where data residency requires it. Below that, a mid-priced API beats an owned eight-GPU server, per Lindstead's break-even analysis.
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Prices vary by provider and change often. Lindstead's European cloud GPU price tracker lists dated on-demand H100, H200 and B200 prices at European and US providers.
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Model size, precision and throughput decide. The Model Index lists minimum hardware per model.
Related
Discuss infrastructure and cost with Lindstead.
A 30-minute meeting covers the use case, the requirements and a first plan.