On-Prem LLM Total Cost of Ownership (TCO) Calculator
This calculator estimates the full multi-year total cost of ownership for running large language models on your own infrastructure, built for IT and finance leaders at manufacturers evaluating on-prem AI. Cloud API bills are visible; on-prem costs hide in power, cooling, staffing, and support contracts. The model combines GPU and server capex with realistic operating assumptions - 700W average draw per GPU, a 1.5 PUE, and a 5% annual hardware support allowance - so you can compare an on-prem deployment against cloud spend on equal footing before committing budget.
Your numbers
Total inference GPUs across all servers in the deployment.
Street price per card; select the class closest to your quote.
Blended commercial rate; US industrial average is roughly $0.08-0.15.
Fraction of a full-time engineer dedicated to running the platform.
Salary plus benefits and overhead for the engineer maintaining the stack.
Most enterprises depreciate GPU hardware over 3-5 years.
Your results
Estimates only. Actual costs vary with vendor pricing, rack density, facility PUE, and support contracts. Use this as a planning baseline, not a quote.
Get your full on-prem LLM TCO report
We will email you a personalized cost breakdown with vendor-quote benchmarks for your GPU class and workload, and an on-prem AI specialist will follow up to validate the assumptions.
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How the TCO math works
Hardware capex is your GPU count times unit price, plus roughly $15,000 per 8-GPU node for chassis, CPUs, RAM, NVMe storage, and networking. Annual power assumes each GPU averages 700W under mixed inference load (peak TDP is higher, but utilization is rarely 100%), multiplied by 8,760 hours, your electricity rate, and a 1.5 PUE to capture cooling overhead. Opex adds a staffing fraction - most 4-8 GPU deployments need 0.3-0.7 FTE once stable - and a 5% of capex line for vendor support, warranty extensions, and spare parts. Total TCO is capex plus opex across your amortization horizon, and the monthly run rate spreads that evenly for budget comparisons.
Benchmarks behind the defaults
The defaults reflect what Netray sees in mid-market manufacturing deployments in 2025-2026. A 4x H100 server lands between $130K and $150K fully configured. Industrial electricity in the US averages $0.08-0.15/kWh, while colocation adds a premium. Enterprise PUE ranges from 1.3 in modern facilities to 1.8+ in legacy server rooms. Steady-state operations for a single-purpose inference cluster typically consume half an engineer, though the first six months run closer to a full FTE during setup and tuning.
- 4x H100 server, fully configured: $130K-$150K capex
- Average GPU power draw under inference load: 600-750W
- Typical enterprise PUE: 1.3 (modern) to 1.8 (legacy server room)
- Steady-state ops staffing for a small cluster: 0.3-0.7 FTE
How to interpret your results
Compare the monthly run rate against your current or projected LLM API bill at equivalent volume. If the on-prem run rate is lower and your workload is steady, on-prem usually wins - and the gap widens each year after hardware is paid off. If the numbers are close, weigh the non-financial factors: data sovereignty, air-gap requirements, latency, and vendor lock-in often justify a modest premium for regulated manufacturers. Also stress-test the staffing assumption; underestimating operations effort is the most common source of TCO surprise we see.
How Netray helps you act on these numbers
Netray designs, procures, and operates on-prem LLM stacks for aerospace, defense, and discrete manufacturers - including fully air-gapped deployments. We validate your TCO model against real vendor quotes, right-size the GPU footprint to your actual workload instead of a spec sheet, and can run the platform under a managed service so the ops FTE line shrinks. Most engagements start with a fixed-fee architecture and cost-validation sprint that turns this estimate into a board-ready business case.
Frequently Asked Questions
Is on-prem LLM hosting really cheaper than cloud APIs?
It depends on volume and utilization. Below roughly 500 million tokens per month, per-token APIs are usually cheaper because you avoid capex and staffing. Above that, steady workloads on owned hardware typically cost 40-70% less over three years. The crossover moves earlier if you have data sovereignty or air-gap requirements that force expensive cloud isolation tiers, and later if your workload is spiky and hardware would sit idle.
Why does the calculator add 50% to raw GPU power consumption?
That is the PUE (power usage effectiveness) multiplier. Every watt a GPU consumes generates heat that cooling systems must remove, and facilities add distribution losses on top. A PUE of 1.5 means the facility draws 1.5 watts for every watt of IT load - a realistic mid-point between modern purpose-built datacenters (1.2-1.3) and typical enterprise server rooms (1.6-1.9).
What costs does this calculator not include?
It excludes software licensing (most inference stacks like vLLM are open source), model fine-tuning compute, initial implementation services, and facility buildout if you need new racks, power circuits, or cooling capacity. It also assumes no GPU refresh within the horizon. For a 5-year horizon, budget a mid-cycle refresh or accept declining relative performance versus newer silicon.
Run your numbers, then let Netray's on-prem AI team pressure-test the business case with real vendor quotes.
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