Ferox Data VS SerpApi

Ferox Data's answer

Three reasons. First, one connection instead of many: one API and one contract covers every marketplace we support, so you’re not managing a separate tool for each store. Second, you only pay for what works: failed requests are never charged, and every response shows exactly what it cost. Third, it’s built for how teams work now, with price and stock alerts, barcode matching across stores, bulk jobs, and access from AI assistants through MCP. And because Ferox does the collecting, there are no scrapers for your team to build or fix when a store changes its website.

SerpApi's answer:

We provide more search engines under one subscription.

Ferox Data's answer

Anyone whose product or work depends on knowing what’s for sale online, at what price, and from whom. That includes companies that build pricing and repricing tools, brand protection teams tracking unauthorised sellers, market research and seller intelligence platforms, price comparison and shopping apps, and developers building AI shopping assistants. Most users are developers and data teams who would otherwise have to build and maintain their own data collection.

SerpApi's answer:

Developers/Companies who need data from search engines.

Ferox Data's answer

Ferox is delivered as a REST API, with signed webhooks for alerts and an MCP server so AI assistants can use it directly.

SerpApi's answer:

Ruby on Rails and MongoDB

Ferox Data's answer

Most tools that collect online product data either cover one store, or leave you to stitch different stores together yourself. Ferox gives you one API that works the same way across 19+ marketplaces, plus any Shopify store and any public product page. Learn one request and you can use it everywhere. The data comes back in one consistent layout, whichever store it came from. On top of that, you can match the same product across stores by barcode, set alerts for price and stock changes, see the prices Ferox has recorded over time, and use all of it from inside AI assistants like Claude.

SerpApi's answer:

We're the first web scraping company that focus on scraping search engines.

Ferox Data's answer

Ferox Data started with a problem almost everyone who works with ecommerce data runs into.

The information is all public. Anyone can open a product page and see the price, the sellers, the stock and the reviews. But turning that into data a business can rely on is a different job. Every store lays out its pages differently and names the same things differently. Websites change without warning, requests get blocked, and the scripts that collect the data quietly break. Teams that wanted to build something useful, like a repricing tool, a brand protection service or a price comparison app, kept ending up spending their time maintaining scrapers instead.

We kept seeing the same pattern: talented teams rebuilding the same plumbing, store by store, and paying for it again every time a website changed. So we set out to build that layer once, properly, for everyone.

Ferox is the result. One API that reads product, price, seller and review data from marketplaces and online stores, and returns it in the same clean format every time. On top of that sit the tools teams kept asking for: matching the same product across stores by barcode, alerts when prices or stock change, a record of prices over time, and access from inside AI assistants.

Our goal is simple: be the data layer underneath digital commerce, so the people building on top of it can focus on their product, not on collecting the data.

SerpApi's answer:

Back in 2017, Julien Khaleghy, the founder of SerpApi, built an iOS app that can analyze data from a picture. iOS didn't have a proper machine learning framework back then. It was challenging: iPhones' RAM were limited, no GPU or no dedicated chip acceleration were available, using only CPU was painfully slow, and compiling/porting C code from machine learning framework like Tensorflow or Caffe to iOS wasn't straightforward. Oddly, all of this wasn't the most difficult part of this project. Collecting images from Google Images was.

In these projects, 80% of his time ended up being spent on scraping and parsing Google Images. And maybe only 20% on actual machine learning model training, UI design of the actual apps, and iOS programming. This is how SerpApi was born.

SerpApi's answer:

• Airbnb
• Nvidia
• Meta
• Shopify
• Grubhub
• and more!