AI infrastructure cost and GPU sizing
What running AI on your own infrastructure costs, and the volume at which it pays off.
Overview
Lindstead quantifies the total cost of ownership of self-hosted AI against current API spend: hardware or cloud GPUs, energy, hosting, staffing and utilisation. The analysis shows whether to buy, rent or keep using an API, and at which monthly volume own infrastructure becomes cheaper.
Key questions
- What would it cost to run our AI workloads on our own GPUs?
- Should we buy servers, rent cloud GPUs in Europe or stay with an API?
- At what monthly volume does self-hosting become cheaper?
- How many GPUs, and of which type, do our models require?
Approach
- Workload and volume analysis Current and expected token volumes are measured per use case, including peaks, so capacity is sized on real demand rather than vendor defaults.
- Infrastructure sizing Models are matched to GPU types and counts, comparing owned servers, European cloud GPUs and colocation.
- Cost model and break-even A transparent model with every assumption visible: hardware, energy, hosting, staff and utilisation, compared with API pricing over three years.
Deliverables
- Infrastructure sizing GPU type, count and hosting option per workload.
- Cost model and break-even Three-year TCO with every assumption shown, and the break-even volume.
- Buy, rent or API advice A recommendation per workload, with sensitivity to price and volume changes.
Frequently asked questions
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Only at sustained, high volume or where data residency requires it. Lindstead's break-even analysis shows that against a mid-priced API an owned eight-GPU server pays off only at high, steady utilisation; below that an API is cheaper.
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On-demand prices differ by provider and change often. Lindstead's European cloud GPU price tracker lists current H100, H200 and B200 prices per GPU hour at European and US providers, with the date of each price.
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It depends on model size, precision and required throughput. The Model Index lists minimum hardware per model; the sizing in this engagement is based on the organisation's own volumes.
Related
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