Building robust AI data pipelines often starts with a critical decision: how to acquire the necessary data. A recent evaluation from the Chinese developer community breaks down three primary methods—proxies, scraping APIs, and pre-built datasets—offering a practical framework for engineering teams. Proxies are best for high-volume, real-time scraping where IP rotation is essential, but they require significant infrastructure management. Scraping APIs provide a more structured and reliable interface, ideal for teams that need clean data without building crawlers from scratch. Pre-built datasets offer the fastest time-to-value but may lack freshness or specificity. The evaluation highlights that the choice depends on project scale, budget, and data freshness requirements. For overseas developers and technical founders, this comparison is directly applicable when designing data pipelines for AI training or market analysis. The key takeaway is to avoid a one-size-fits-all approach and instead match the acquisition method to the specific data lifecycle needs of your project.
A practical comparison of proxies, scraping APIs, and datasets for AI data engineering, helping teams choose the right tool for their pipeline.