Migrate off expensive stealth scraping — 19× cheaper per request

Many companies that run regular stealth scraping for product intelligence, price monitoring, ad verification, or to build training corpora for AI face soaring per-request costs and fragile reliability when trying to mimic real browsers at scale. The addressable market is roughly 300,000 teams spending about $4,000 per year on proxies, browser infrastructure and related tooling (≈ $1.2B), and incumbents often use opaque credit systems that make per-request economics unpredictable. You could build a developer-first stealth scraping platform that targets a 19× lower cost per successful request than current stealth providers by combining shared browser instances and optimized rendering, automated fingerprint management, ML-driven human-like behavior emulation, deduplication/caching, and dynamic routing, paired with transparent consumption-based pricing. The product would include SDKs, integrations, monitoring dashboards and enterprise SLAs so teams can migrate off brittle bespoke stacks with predictable unit economics. Market timing is favorable: growing AI/automation needs increase demand for large volumes of web data, stronger anti-bot defenses raise the value of true-stealth reliability, and customers prefer predictable per-request pricing — we rate this opportunity high (market score 94/100, revenue potential 88/100). Differentiation will come from maintaining a sustained unit-cost advantage, superior developer UX and transparent billing, but expect continuous engineering to stay ahead of bot defenses, non-trivial legal/compliance risk in some jurisdictions, and capital/ops intensity to run a global fleet. If you can sustain the 19× per-request cost advantage for 12+ months and onboard a few hundred customers (100 customers ≈ $400k ARR; 500 ≈ $2M ARR), the economics make this worth pursuing; otherwise the operational and regulatory burdens materially increase risk.