White Paper
The Economics of AI Infrastructure: TCO Analysis
Total cost of ownership comparison between traditional GPU infrastructure and next-gen ASIC solutions.
Executive Summary
This analysis compares the total cost of ownership (TCO) for AI inference infrastructure across three deployment models over a 5-year horizon:
- Public Cloud GPU: On-demand instances from major cloud providers
- Self-Hosted GPU: Owned GPU clusters in colocation facilities
- ASIC-Optimised Cloud: Purpose-built inference infrastructure
Key finding: For sustained inference workloads exceeding 1M tokens/day, ASIC-optimised infrastructure delivers 40-60% lower TCO compared to alternatives.
Cost Components
Capital Expenditure (CapEx)
| Component | Public Cloud | Self-Hosted GPU | ASIC Cloud |
|---|---|---|---|
| Hardware | $0 (OpEx) | $2.5M | $0 (OpEx) |
| Datacentre build-out | $0 | $500K | $0 |
| Network infrastructure | $0 | $150K | $0 |
| Initial integration | $50K | $200K | $30K |
Operating Expenditure (OpEx) - Annual
| Component | Public Cloud | Self-Hosted GPU | ASIC Cloud |
|---|---|---|---|
| Compute | $1.8M | $0 | $720K |
| Power | Included | $480K | Included |
| Cooling | Included | $120K | Included |
| Colocation | Included | $300K | Included |
| Personnel | $200K | $600K | $150K |
| Maintenance | Included | $250K | Included |
| Software/licensing | $100K | $150K | $50K |
Workload Assumptions
Analysis based on:
- Daily token volume: 10M tokens (input + output)
- Peak concurrency: 100 simultaneous requests
- Latency SLA: P95 < 200ms
- Availability target: 99.9%
- Growth rate: 30% annually
5-Year TCO Comparison
Year 1
| Model | CapEx | OpEx | Total |
|---|---|---|---|
| Public Cloud | $50K | $2.1M | $2.15M |
| Self-Hosted GPU | $3.35M | $1.9M | $5.25M |
| ASIC Cloud | $30K | $920K | $950K |
Year 5 (Cumulative)
| Model | Total 5-Year Cost | Cost per Million Tokens |
|---|---|---|
| Public Cloud | $14.2M | $0.78 |
| Self-Hosted GPU | $15.8M | $0.87 |
| ASIC Cloud | $6.1M | $0.33 |
Hidden Cost Factors
Public Cloud
Advantages:
- Zero upfront investment
- Elastic scaling
- Managed operations
Hidden costs:
- Egress charges (data transfer out)
- Reserved instance commitment risk
- Vendor lock-in switching costs
- GPU availability constraints during demand spikes
Self-Hosted GPU
Advantages:
- Full control
- No per-token costs at scale
- Hardware asset ownership
Hidden costs:
- GPU refresh cycles (3-4 years)
- Specialised talent requirements
- Underutilisation during low-demand periods
- Obsolescence risk from rapid AI hardware evolution
ASIC Cloud
Advantages:
- Optimised for inference workloads
- Predictable per-token pricing
- No hardware obsolescence risk
- Integrated optimisations
Considerations:
- Limited flexibility for training workloads
- Vendor relationship dependency
Sensitivity Analysis
Workload Volume Impact
| Daily Tokens | Public Cloud | Self-Hosted | ASIC Cloud |
|---|---|---|---|
| 1M | Most economical | Highest cost | Competitive |
| 10M | High cost | Break-even | Most economical |
| 100M | Very high | Economical | Most economical |
Insight: ASIC infrastructure becomes increasingly advantageous as volume grows.
Utilisation Sensitivity
Self-hosted economics depend heavily on utilisation:
- Less than 50% utilisation: Public cloud often cheaper
- 50-70% utilisation: Break-even zone
- Above 70% utilisation: Self-hosted becomes competitive
ASIC cloud provides consistent economics regardless of client-side utilisation patterns.
Non-Financial Considerations
Time to Production
| Model | Typical Timeline |
|---|---|
| Public Cloud | 1-2 weeks |
| Self-Hosted GPU | 6-12 months |
| ASIC Cloud | 2-4 weeks |
Operational Complexity
| Model | Required Expertise |
|---|---|
| Public Cloud | Cloud operations, ML engineering |
| Self-Hosted GPU | Hardware, datacentre, ML ops, security |
| ASIC Cloud | API integration, ML engineering |
Risk Profile
| Model | Primary Risks |
|---|---|
| Public Cloud | Cost volatility, availability, vendor dependence |
| Self-Hosted GPU | Technology obsolescence, talent retention |
| ASIC Cloud | Vendor relationship, capacity constraints |
Recommendations
Choose Public Cloud When:
- Workloads are unpredictable or experimental
- Time-to-market is critical
- Internal infrastructure expertise is limited
- Volume is below 1M tokens/day
Choose Self-Hosted When:
- Regulatory requirements mandate on-premises
- Existing datacentre capacity is available
- Long-term volume justifies investment
- Organisation has infrastructure expertise
Choose ASIC Cloud When:
- Inference is the primary workload
- Cost-per-token is a key metric
- Predictable, high-volume demand
- Operational simplicity is valued
Conclusion
AI infrastructure economics vary significantly by workload profile. For sustained inference at scale, purpose-built ASIC infrastructure delivers compelling TCO advantages—often 40-60% savings over alternatives.
The optimal choice depends on workload characteristics, organisational capabilities, and strategic priorities. Many enterprises benefit from hybrid approaches that match infrastructure to workload requirements.
For a customised TCO analysis, contact info@scx.ai.