Private AI Cloud Cost Test for Enterprise Inference

Residency and latency set the floor

You are choosing where to host LLM inference for a regulated, latency-sensitive, or cost-sensitive product. The trade-off is fixed GPU capacity versus public per-token billing: a private AI cloud costs hardware, power, and support whether the GPUs are busy or idle. Private cost per token is GPU-hours plus support divided by tokens served; public cost is tokens times the per-token price, plus latency and egress penalties. If steady tokens per day are high enough, required GPU-hours fall below the public price line; if not, public inference stays cheaper.

Broadcom cited research indicating that 56% of enterprises are already running, or plan to run, production AI on private cloud infrastructure.

Keep the test in these steps:

• Classify residency and latency constraints.
• Estimate steady-state tokens per day.
• Compute required GPU-hours.
• Amortize hardware and support over the holding period.
• Compare against public per-token pricing plus latency and egress penalties.

Classify data residency and latency constraints before pricing. Regulated data must stay in a named region, and p95 latency must stay under a named millisecond budget. If either constraint fails on public inference, stop; the private path is the only option.

Map those constraints to a private AI cloud. Broadcom introduced VMware Private AI Cloud and VMware AI Factory at the 2026 Las Vegas VMware Explore event as a single private-infrastructure platform for inference, agent workloads, and enterprise apps.

Steady tokens decide the GPU math

Choose the smallest model that meets quality and latency. Broadcom said VMware AI Factory can run more than 150 open-source and commercial models, naming Nemotron 3, Gemma 4, Qwen 3.7-Max, and GLM 5.2. A model card should carry tokens per GPU-hour, memory footprint, and license terms.

Estimate steady-state tokens per day, not peak. Use a month of input and output tokens by model, endpoint, and customer tier. Peak traffic sizes the burst; steady traffic sizes the capital.

Compute required GPU-hours from model, batch size, and target latency. GPU-hours equal tokens served divided by tokens per GPU-hour at target latency. A table of tokens per second, GPU-hours per day, and required utilization shows where cost per token stays flat.

Say you serve 100 million tokens per day and your serving stack produces 500,000 tokens per GPU-hour at target latency. That is 200 GPU-hours per day. If the GPUs are half idle, you need capacity for about 400 GPU-hours per day. The gap is the idle cost you are buying.

Amortize hardware, power, and support over the planned holding period. Monthly cost per token equals capital depreciation plus operating cost divided by tokens served. AMD executive Suresh Andani said that pairing the AMD Instinct MI350 series with the open ROCm stack on VMware Cloud Foundation provides customers a private AI route with predictable costs and no per-token billing.

Public pricing is the comparison, not the ceiling

Compare private cost per token against public per-token pricing plus latency and egress penalties. Let P be public cost per token, including egress and latency penalties. Let C be monthly cost per GPU-hour in US dollars, S be monthly support, T be monthly tokens, and R be tokens per GPU-hour. Private cost per token is (T/R × C + S) / T. Break-even is the T where private and public lines cross: below it, public inference is cheaper; above it, private capacity pays for itself.

Check the hardware path before signing. The AI Factory is compatible with GPU accelerators from Nvidia and AMD and has server certifications from Cisco, Dell Technologies, Supermicro, and Lenovo. Build a bill of materials for certified servers and accelerators that fit your data center.

Price support and maintenance as a monthly line, not a footnote. Contract terms should cover response times, spare parts, and upgrade windows. If support cost moves the break-even above your steady state, the private path is not cheaper.

After launch, watch tokens per GPU-hour, idle hours, and cost per token by week. If utilization drifts down, either cut capacity or raise the price. If the break-even volume is above your realistic steady state, keep inference public and revisit when traffic grows.