AI investment & economics
Inference run-rate forecaster
Current decision
No monthly crossover in 24 months
Same instrument · 3 industries choosing a scenario replaces its inputs; save or share your current changes first
Monthly run rate · 24 months
All 24 monthly values
| Month | API (USD/month) | Self-host (USD/month) | Lower monthly cost |
|---|---|---|---|
| 1 | 13,500 | 71,000 | API |
| 2 | 14,310 | 109,000 | API |
| 3 | 15,169 | 109,000 | API |
| 4 | 16,079 | 109,000 | API |
| 5 | 17,043 | 109,000 | API |
| 6 | 18,066 | 109,000 | API |
| 7 | 19,150 | 109,000 | API |
| 8 | 20,299 | 109,000 | API |
| 9 | 21,517 | 109,000 | API |
| 10 | 22,808 | 109,000 | API |
| 11 | 24,176 | 109,000 | API |
| 12 | 25,627 | 109,000 | API |
| 13 | 27,165 | 147,000 | API |
| 14 | 28,795 | 147,000 | API |
| 15 | 30,522 | 147,000 | API |
| 16 | 32,354 | 147,000 | API |
| 17 | 34,295 | 147,000 | API |
| 18 | 36,352 | 147,000 | API |
| 19 | 38,534 | 147,000 | API |
| 20 | 40,846 | 185,000 | API |
| 21 | 43,296 | 185,000 | API |
| 22 | 45,894 | 185,000 | API |
| 23 | 48,648 | 185,000 | API |
| 24 | 51,567 | 185,000 | API |
Beyond 24 mo
Cumulative
Cumulative
Crossover sensitivity · crossover under each single move
- Growth → 12%/mono cliff
- Frontier share → 20%no cliff
- Tokens/call → 4,500no cliff
- Utilization → 85%no cliff
Each row recomputes the crossover with one assumption changed, earlier (green) means self-host monthly cost first undercuts API sooner; cumulative payback may differ.
No cliff inside 24 months
If you act on this · the call → expected lift → how you'd measure it
The call
Revisit inference strategy when volume and utilization make the run rate materially different from the pilot economics.
Expected lift · illustrative
Avoids both premature self hosting and uncontrolled API spend.
How you'd measure it
Monthly run rate, cumulative 24 month cost, utilization, cost per task, crossover month.
Steering committee takeaway: The crossover is not decided by vendor claims. It is decided by utilization, growth, and the share of workloads that truly need higher cost models.
How this is built & assumptions
API/mo = volume × tokens/call × blended price ($3 to $18/1M tokens by frontier share). Volume compounds at the monthly growth rate.
Self host/mo = ⌈tokens ÷ (cluster capacity 2.5B × utilization)⌉ × $38k amortized + ops FTE × $22k. The cliff is the first month self-host < API.
Stack: Next.js (static) + shared design system; deterministic client side.
Limitations: this model uses simplified cost assumptions. Production forecasting would require current pricing, workload profiles, infrastructure benchmarks, reliability requirements, and finance approved cost allocation.
Method, evidence and limitations
Prefer to read? The two minute case study · problem → approach → metric → outcome
Problem
API usage offers flexibility, but cost scales with volume. Self hosting can reduce marginal cost after utilization reaches the right level, but it introduces fixed capacity, operations, infrastructure, and talent requirements, and the decision depends on utilization more than sticker price.
Approach
The forecaster projects API and self hosted costs across 24 months. It marks the crossover point and shows how growth, token volume, frontier model share, utilization, and staffing assumptions move the decision.
Why this way
This connects AI operating strategy to budget planning, unit economics, platform investment, infrastructure commitments, and cost governance.
The metric
The crossover month; 24-month cumulative cost each way.
The trade-off
API is flexible pay per use; self host is fixed capacity that only amortizes past the crossover.
Outcome
The crossover month with the assumption that moves it most made explicit.