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 is Total Cost of Ownership (TCO) for AI infrastructure?
Total Cost of Ownership represents the complete financial impact of an AI infrastructure investment over its useful life, including hardware acquisition, power and cooling, facilities, personnel, maintenance, and software licensing costs. For AI workloads, TCO extends far beyond hardware purchase price—power consumption can exceed hardware cost over a 3-year period.
When is on-premises AI infrastructure more cost-effective than cloud?
On-premises AI infrastructure typically becomes more cost-effective than cloud for sustained workloads with 60%+ utilization. Break-even typically occurs at:
- 7-14 months for 90%+ utilization (24/7 production)
- 14-24 months for 60-80% utilization (active development)
- 24-36+ months for 40-60% utilization (periodic workloads)
Below 40% utilization, cloud is often more economical.
What are the major cost categories in AI infrastructure TCO?
The major cost categories are:
- Hardware CapEx (30-50%): GPUs, servers, networking, and storage
- Energy OpEx (15-25%): Electricity and cooling
- Personnel costs (20-30%): System administration and MLOps
- Facility costs: Colocation or data center space
- Maintenance & Support (10-15%): Hardware support and software licensing
How much electricity does an AI GPU server consume?
Modern AI accelerators have significant power requirements: NVIDIA H100/H200 SXM draws 700W per GPU, B200 draws 1,000W, B300 draws 1,400W, and AMD MI355X draws 1,400W. An 8-GPU H200 server consumes approximately 6.1kW including CPU overhead. With typical data center PUE of 1.2-1.5, annual electricity costs range from $8,000-15,000 per 8-GPU server at $0.12/kWh.
What cooling is required for high-density AI GPU deployments?
Cooling requirements depend on rack density:
- Air cooling with containment supports up to 35 kW/rack (PUE ~1.50)
- RDHx supports 50-75 kW/rack (PUE ~1.35)
- Direct-to-chip liquid required for 50-100+ kW/rack (PUE ~1.20)
- Immersion cooling achieves best efficiency for 100+ kW/rack (PUE ~1.10)
What is PUE and why does it matter for AI infrastructure costs?
Power Usage Effectiveness (PUE) measures data center efficiency as the ratio of total facility power to IT equipment power. A PUE of 1.50 means for every 1 watt of IT load, 0.50 additional watts are consumed for cooling and facility overhead.
For high-power AI deployments, improving PUE from 1.50 to 1.20 can reduce energy costs by 20%. Industry average is 1.56; best-in-class hyperscalers achieve 1.08-1.10.
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.
Calculator Disclaimer
This calculator provides estimates based on typical costs and industry averages. Actual costs will vary based on specific configurations, vendor negotiations, geographic location, and individual circumstances. GPU pricing reflects current market conditions as of January 2026 and may change without notice. Cloud pricing is based on published rates and does not include potential discounts, data egress, storage, or network transfer costs. For detailed infrastructure planning and precise cost analysis, contact SLYD for a customized assessment.