Compare Gaffa vs. apiJuice

Gaffa is a REST API built for web scraping and browser automation, allowing developers to run real, full browsers at scale with a single API call. It removes the difficulty of managing headless browser frameworks, rotating proxies, CAPTCHA solving, and scaling infrastructure, all of which are handled automatically. JavaScript-heavy and dynamic websites render exactly as they would for a human visitor by default. Beyond standard scraping, Gaffa supports AI-driven structured data extraction (extract data into a defined schema without writing CSS selectors), screenshot and PDF capture, infinite-scroll and form-filling automation, and clean Markdown conversion for feeding webpages directly into LLM and RAG pipelines. A rotating residential proxy network keeps access reliable across regions, and a credit-based pricing model means teams pay only for the browser time and bandwidth they actually use. Gaffa is designed for AI engineers, data teams, and developers who want production-grade web data extraction without having to build and maintain their own infrastructure.

apiJuice is a revolutionary platform powered by AI that transforms any webpage into a personalized, hosted API, providing clean and structured JSON responses without the need for coding or manual scraping. Users can effortlessly input a URL and specify their data requirements in straightforward language; the AI then generates a customized API endpoint or n8n node that supplies precisely the needed information. This functionality allows both developers and those lacking technical skills to swiftly obtain structured data for integration into applications or workflows. The entire experience is quick and user-friendly, taking mere seconds to set up while removing the challenges associated with building web scrapers or developing extraction logic from the ground up. Designed to simplify the process of data extraction and implementation, apiJuice enhances accessibility and efficiency across diverse applications. Additionally, it empowers users to streamline their operations, ultimately leading to more productive data management practices.