AI Build vs Buy Break-Even Calculator
Calculate the exact financial break-even point where in-house AI engineering beats commercial LLM API SaaS subscription costs over multi-year horizons.
Why I Built This: The Hidden Payroll of "Free" In-House Software
In enterprise architecture, teams often justify building custom in-house software or self-hosted AI pipelines by comparing server bills directly against vendor SaaS license quotes. They overlook the real expense: ongoing maintenance engineering payroll.
This calculator calculates the true break-even volume by factoring in fully loaded engineering salaries, maintenance headcount, schedule overrun risks, and serverless compute floors.
Architectural Assumptions & Mechanics
- Fully Loaded Payroll: Base engineering salaries are loaded at 1.4445x using U.S. Bureau of Labor Statistics (BLS) ECEC data to capture benefits, taxes, and overhead.
- Schedule Slippage Reality: Software projects frequently exceed initial timelines. The model defaults to 1.00x with 1.27x mean industry overrun and 3.00x tail risk options based on empirical research (Bent Flyvbjerg).
- Maintenance Floor: Maintenance is modeled in real engineering headcount (FTEs). Even a single dedicated maintenance engineer creates a significant ongoing payroll baseline.
- API Rate Cards: Vendor costs reflect current enterprise LLM token pricing (Anthropic Claude rate cards) with prompt caching and batch execution discounts.
- Serverless Unit Costs: Internal execution costs are modeled at an AWS serverless baseline floor of $0.00000128 per transaction.
- The Break-Even Threshold: At low or moderate transaction volume, maintenance payroll alone usually dwarfs third-party API invoices. Custom builds only yield positive ROI once transaction volume scales past the calculated break-even point.
Newsletter Companion
The Productivity Dividend
Read the complete article and analytical deep-dive on LinkedIn.
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