AI Infrastructure
TCO Calculator
Calculate the complete total cost of ownership for your AI infrastructure deployment. Compare on-premises vs. cloud with 3-7 year financial projections and break-even analysis.
Configuration
GPU Configuration
Power & Cooling
Analysis Period
TCO Results
Capital Expenditures (CapEx)
Annual Operating Expenses (Year 1)
Multi-Year Projection
Cloud Comparison (5 Years)
| Deployment Model | Total Cost | Per GPU | $/GPU/Hour |
|---|---|---|---|
| On-Premises (SLYD) | $842,050 | $105,256 | $2.40 |
| Cloud On-Demand (24/7) | $3.10M | $387,236 | $8.00 |
| Cloud Savings Plan (40% off) | $1.86M | $232,342 | $4.80 |
Cloud comparison assumes 24/7 utilization. Actual cloud costs may include additional data egress, storage, and network transfer fees not shown here.
What is Total Cost of Ownership?
Understanding the complete financial impact of AI infrastructure investments
TCO Definition
Total Cost of Ownership represents the complete financial impact of an IT investment over its useful life. For AI infrastructure, TCO extends far beyond hardware purchase price to include every cost associated with deploying, operating, and maintaining GPU systems.
A thorough TCO analysis captures costs that are often overlooked or underestimated, including power consumption that can exceed the hardware cost over a 3-year period, cooling requirements that scale with compute density, and personnel costs for managing increasingly complex AI systems.
Why TCO Matters for AI
AI workloads present unique TCO challenges compared to traditional IT:
- Power density: A single B200 server can draw 10kW, more than an entire rack of traditional servers
- Cooling complexity: High-density deployments often require specialized cooling solutions
- Utilization patterns: Training and inference have different profiles affecting cost efficiency
- Rapid depreciation: GPU generations advance quickly, affecting residual value
Break-Even Analysis: Cloud vs. On-Premises
For most sustained AI workloads, on-premises infrastructure becomes more cost-effective than cloud beyond a certain utilization threshold and time horizon.
Understanding Cost Categories
Deep dive into the components of AI infrastructure TCO
Hardware Costs
GPU hardware represents the most visible component of AI infrastructure. A production-ready system includes:
- Base system: CPU(s), memory, NVMe storage, chassis: $5,000-$45,000 per server
- Interconnects: NVLink bridges, InfiniBand NICs: $3,000-$10,000 per GPU for multi-node
- Networking: Switches, cabling, top-of-rack equipment
Power & Cooling
For high-utilization AI workloads, electricity can become the largest single cost category over a 5-year period.
*At $0.12/kWh, 24/7 operation, before PUE multiplier
Personnel Costs
AI infrastructure requires specialized skills that command premium compensation:
- System administration (Linux, containers, GPU drivers)
- Networking (InfiniBand, high-performance fabrics)
- ML Operations (training pipelines, model deployment)
Maintenance & Support
Hardware failures are inevitable over multi-year deployments. Budget for:
- Hardware support: 10% of hardware cost annually
- Software licensing: ~$500/GPU/year for enterprise tools
- Failure replacement: 1-3% GPU failure rate annually
Frequently Asked Questions
Common questions about AI infrastructure TCO
What does this calculator actually produce?
An estimate of the total cash cost of owning a GPU deployment over a period you choose, next to the cost of renting equivalent capacity continuously over the same period. It is built from the inputs you set plus a set of built-in assumptions that are listed in full on this page. It is planning analysis rather than a quote, and it is not accounting, tax, credit, or investment advice.
What is the biggest limitation of the comparison?
It charges for continuous utilization on both sides. Owned capacity does cost the same whether or not it is busy, so that side is realistic. Rented capacity is only billed when used, so if your accelerators will sit idle a meaningful share of the time, this model overstates the cloud cost and the owned case looks better than it is. Sustained utilization is the input that moves the conclusion most, and it is the one the tool cannot know.
Where do the built-in prices come from?
They are indicative planning defaults in US dollars. There is no governed public price record behind them and no as-of date attached to them, because accelerator and server pricing depends on configuration, quantity, channel, geography, and timing. Treat them as placeholders that get you a shape, and substitute your own quoted prices before relying on any figure.
Does the model account for residual value, financing, or tax?
No. Hardware is assumed worth nothing at the end of the period, capital is treated as spent on day one with no interest or discount rate, and no tax effect is modelled. Excluding residual value is conservative against the owned case. Excluding financing means this is a cash-cost model rather than a net-present-value one, so it will not match a model your finance team builds.
What is not in the cloud figure?
Storage, data transfer and egress, networking, support tiers, and committed-use discounts are all excluded. The cloud number here is accelerator hours only, escalated at a fixed rate. A real cloud bill for a training or serving workload includes several of those items, and for data-heavy workloads they are not a rounding error.
What is PUE and how does it affect the result?
Power usage effectiveness is total facility power divided by IT power. A PUE of 1.50 means half a watt of cooling and facility overhead for every watt of IT load. It multiplies straight into the electricity line, so it changes operating cost proportionally. PUE is an outcome of a whole facility rather than a property of a cooling technology, so the defaults here are starting points. Override it with your own measured figure if you have one.
How should I use the output?
As a range. Move accelerator price, sustained utilization, electricity rate, and the cloud rate you are comparing against to the top and bottom of what you think is plausible, and see whether the conclusion holds. If it flips, the difference between owning and renting is smaller than the uncertainty in your inputs, and the decision should turn on something else such as data residency, lead time, or flexibility.
Need Expert TCO Analysis?
Our infrastructure economists will provide a custom TCO model with accurate costs, ROI projections, and complete financial recommendations for your specific deployment.
How this calculator works
Every figure it produces is an estimate built from the inputs you set plus the built-in assumptions listed below. It is planning analysis, not accounting, tax, credit, or investment advice, and it is not a quote. All amounts are in US dollars and exclude tax.
What you control
Accelerator model, accelerator count, accelerators per server, cooling method, electricity rate, PUE, analysis period, and annual operating-cost escalation. Changing any of these recalculates the result immediately.
Built-in assumptions
These are fixed in the tool. They are the reason two people can put the same inputs in and still need to sanity-check the output against their own quotes.
| Assumption | Value used |
|---|---|
| Accelerator unit price | NVIDIA B300 $60,000, NVIDIA B200 $45,000, NVIDIA H200 $27,000, NVIDIA H100 SXM $22,500, NVIDIA H100 PCIe $22,500, NVIDIA RTX PRO 6000 Blackwell $9,500, NVIDIA A100 80GB $12,000, AMD Instinct MI325X $15,000, AMD Instinct MI300X $12,000 |
| Server chassis price by density | 1-GPU $8,000, 2-GPU $12,000, 4-GPU $18,000, 8-GPU $35,000, 10-GPU $45,000 |
| Server overhead beyond the accelerators | 500 W per server, covering CPUs, memory, storage, network, and supply losses |
| Networking | $5,000 up to 8 accelerators; $25,000 plus $3,000 per server from 9 to 64; $75,000 plus $4,500 per server above 64 |
| Storage | $10,000 base plus $2,500 per accelerator |
| Power infrastructure capital | $500 per kW of IT load |
| Cooling infrastructure capital | Air with containment $300/kW, Rear-door heat exchangers $450/kW, Direct-to-chip liquid $600/kW, Immersion $800/kW |
| Default PUE per cooling method | Air with containment 1.50, Rear-door heat exchangers 1.35, Direct-to-chip liquid 1.20, Immersion 1.10 |
| Facility or colocation | $150 per facility kW per month |
| Staffing | $50,000 per year up to 16 accelerators; $100,000 up to 64; $200,000 above 64 |
| Support and licensing | 10% of hardware capital per year, plus $500 per accelerator per year |
| Utilization | Continuous. Electricity is charged for all 8,760 hours in a year. |
| Cloud comparison rate | NVIDIA B300 $12.00/hr, NVIDIA B200 $12.00/hr, NVIDIA H200 $8.00/hr, NVIDIA H100 SXM $4.50/hr, NVIDIA H100 PCIe $4.50/hr, NVIDIA RTX PRO 6000 Blackwell $1.80/hr, NVIDIA A100 80GB $3.50/hr, AMD Instinct MI325X $3.50/hr, AMD Instinct MI300X $3.00/hr |
| Cloud rate escalation | 5% per year, applied independently of the operating-cost escalation you set |
| Cloud reserved pricing | 40% below the on-demand figure |
What the model does not include
- Residual value. The owned case assumes the hardware is worth nothing at the end of the period. Any resale or trade-in value would improve the owned case, so this assumption is conservative in that direction.
- Financing. Capital is treated as spent on day one. There is no interest, lease structure, or discount rate, so this is a cash-cost model rather than a net-present-value one.
- Depreciation and tax treatment. No tax effect of any kind is modelled, and tax is excluded from every figure.
- Utilization below continuous. Electricity is charged for the full year. If your accelerators will be idle a meaningful share of the time, the owned case here understates cost per delivered hour, and the cloud case materially overstates cloud cost because rented capacity is only billed when used. This is the single largest limitation of the comparison.
- Cloud costs beyond the accelerator hour. Storage, data transfer and egress, networking, support tiers, and committed-use terms are all excluded from the cloud figure.
- Ramp, migration, and downtime. Procurement lead time, installation, commissioning, and the cost of capacity being unavailable are not modelled.
How to read the result
Treat it as a range, not a number. The output is most sensitive to accelerator price, sustained utilization, electricity rate, and the cloud rate you are comparing against. Move each of those to the top and bottom of what you think is plausible and see whether the conclusion changes. If it flips, the comparison is not yet decided and the difference between the two options is smaller than the uncertainty in the inputs.
Facility power and cooling load can be worked through in more detail with the power and cooling calculator. Current published on-demand cloud rates, with provider, shape, and date attached, are in the training guide.
Sources and basis
What the figures on this page rest on.
Every currency figure the calculator outputs: total cost of ownership, the capital and operating breakdowns, the per-accelerator and hourly derived figures, and the cloud comparison.
Calculated from your inputs and the built-in assumptions listed above. Changes when any of them change. Not a quote and not sourced market data.
The built-in accelerator prices, server prices, networking and storage costs, capital cost per kW, facility rate, staffing bands, support percentage, cloud hourly rates, cloud escalation, and reserved discount.
Indicative planning defaults with no governed public record and no as-of date. Published here in full so they can be judged and replaced rather than left implicit in the code.
Accelerator board power used to derive IT load.
Several parts publish a configurable board-power range rather than a single figure, and the design value comes from the OEM system. The single value used here is a planning simplification.
That PUE multiplies into facility load, and the relationship between IT load, facility load, and annual energy.
Definitional arithmetic. The PUE value itself is an input you set, not a sourced fact.
The absence of break-even month ranges, cost-category percentage splits, per-technology PUE benchmarks, and industry-average or hyperscaler PUE figures.
The previous version published all of them without a source. Break-even is an output of your own inputs, and PUE is a site outcome, so neither is publishable as a general figure.