Why generic scrapers fail at scale
Retailers run mature anti-bot stacks (DataDome, Akamai, custom Shape) and aggressively block IPs that show price-monitoring patterns: repeated visits to high-margin SKUs, no cart interactions, no checkout flow. A naive scraper gets blocked within hours. A good price-monitoring API rotates residential IPs at the country level, varies request patterns, and respects per-domain concurrency limits so any one identity stays under the radar.
Geo-targeting matters more than you think
Amazon prices, availability, and Prime eligibility differ by country and even by ZIP code. Walmart shows different inventory by store. Without geo-locked residential proxies you are scraping the wrong data and your pricing models are wrong by default. A good API exposes country and city-level proxy selection per request so you can monitor the markets you actually sell in.
Structured output vs raw HTML
Some scraping APIs return raw HTML and leave parsing to you. For price monitoring, prefer APIs that ship pre-built extractors for the major retailers — they handle layout changes for you, so a Walmart redesign does not break your pipeline at 3am. For long-tail Shopify stores, generic HTML + a small extraction layer (CSS selectors or LLM-based extraction) is usually the right answer.
