Commodity-Hardware Bug Bounty Economics: How a $6,000 Workstation Replaces $100k+/Year Security AI

We present a comprehensive economic analysis of running a full-spectrum bug bounty operation powered entirely by consumer-grade hardware—specifically, an Apple Mac Studio with M2 Ultra (192GB unified memory) and a suite of nine locally-deployed specialized cybersecurity language models (the Saif ecosystem, totaling 36GB in 4-bit quantization). We compare this hardware-bound inference pipeline against three alternatives: (1) cloud GPU inference at prevailing rates ($3–$5/hr for 8× A10G), (2) commercial security AI products (CrowdStrike Charlotte AI at $100k+/year), and (3) the traditional scout-and-scan approach without AI augmentation. Over a two-week engagement across 23 bug bounty programs, 160 pipeline cycles, 2,500 enumerated subdomains, and approximately 150 live hosts, the system produced five findings with an estimated total value of $53,500–$198,000—achieving zero cloud compute expenditure. We model the total cost of ownership (TCO) over 12, 24, and 36 months, compute risk-adjusted ROI, identify scalability ceilings, and provide a replication guide enabling other researchers to reproduce the setup for under $7,000. Our findings demonstrate that consumer hardware running quantized open-weight security models achieves a per-dollar analytical capability ratio exceeding cloud-based and commercial alternatives by 8–35×, fundamentally challenging the assumption that effective security AI requires enterprise infrastructure.