AI Cluster Cost Calculator
End-to-end total cost of ownership for an AI training cluster — GPUs, storage, egress, and operations across neoclouds, hyperscalers, and owned colocation.
An AI cluster's all-in cost is dominated by GPU-hours (typically 80%), with storage, egress, and operations layered on top. A 1,024-GPU H100 program on reserved 1-year neocloud pricing runs roughly $13–18M per year; the same cluster on a hyperscaler runs $25–30M. Owned colocation is cheaper above 60% three-year utilization.
| Cost category | Monthly | % of total |
|---|---|---|
| Compute (GPU-hours) | $941,524 | 93.6% |
| Storage | $16,384 | 1.6% |
| Egress bandwidth | $1,434 | 0.1% |
| Operations & observability | $47,076 | 4.7% |
TCO estimate based on indicative public 2026 pricing: neocloud reserved 1y at ~62% of on-demand list; hyperscaler reserved 1y at ~55%; colocation ownership amortized to ~36% of cloud on-demand assuming 3-year GPU life. Storage $0.08/GB-mo, egress $0.07/GB, ops 5% of compute. Excludes power overages, support, and engineering headcount.
Frequently asked questions
- What does an AI training cluster cost?
- A 1,024-GPU H100 cluster on reserved 1-year neocloud pricing runs ~$13–18M/year all-in (compute, storage, networking, ops). The same cluster on AWS reserved is ~$25–30M/year. Adjust the inputs to model your scenario.
- What's the cost breakdown beyond GPUs?
- Typically: compute ~80%, storage ~5–10%, networking egress ~3–8%, ops + observability ~5%. Storage scales with model size and checkpoint cadence; egress matters for multi-region inference.
- Is it cheaper to buy GPUs and colocate?
- Yes for ≥3-year utilization above 60% — owning H100s in colo runs ~$0.85/hr fully loaded vs ~$1.40–$1.50/hr reserved on neocloud. But colo requires capex, supply-chain access, and operations team.
- What's the cost to train a frontier model?
- Llama-3 70B: ~6.4M H100-hours ≈ $9–15M. Llama-3 405B: ~30M H100-hours ≈ $42–70M. Frontier 1T-param models: ~100M+ H100-hours ≈ $150M+. Add ~30% for failed runs, evals, and hyperparameter sweeps.
Company → Datacenter → GPU → Customer → Industry
Every entity on this site is cross-linked. Follow the graph from operators down to specific facilities, GPU clusters, customers, and sectoral context.