We scrape product data from online stores and marketplaces - prices, discounts, stock, sellers, ratings and reviews - and deliver one clean feed on your schedule, from Amazon and Walmart to Allegro and eMAG.
Plans from €169/month · Free project assessment · Reply within 1 business day
Marketplaces
Online Stores & Retailers
Dropshippers
Price & Product Aggregators
Marketing & Advertising Agencies
Brands
Suppliers
Manufacturers
Data Analytics Companies
Research and Consulting Firms
Developers
Customers worldwide
Pages extracted
Hours saved for our clients
Ecommerce data scraping collects public product data from online stores and marketplaces - prices and discounts, stock, sellers, ratings and reviews - and turns it into one clean, comparable feed. Retailers use it for competitor price monitoring and repricing, brands to watch their minimum advertised price and unauthorised sellers, marketplace sellers to see who wins the offer, and category teams to find assortment gaps and enrich their own catalogue. Our ecommerce scraper runs as a managed service: we build and maintain the crawlers, match products across stores and deliver the data where your team works.
Find the right products, categories, and prices to build a strong marketplace catalog.
The ecommerce price scraper helps track competitors’ pricing and make smart comparisons to stay competitive.
Identify identical or similar products across different stores to clean and unify your catalog.
See what products are gaining popularity and predict market shifts.
Add missing attributes, specs, and descriptions to improve your listings.
Analyze customer feedback to understand opinions and improve products.
Use an ecommerce scraper to surface top-rated and trending products for curated “best” lists.
Compare prices of alternative items to refine your pricing strategy.
Automatically detect when sellers undercut your Minimum Advertised Price.
Every product becomes one record with the store's own product ID and, where the page shows it, the EAN or GTIN, so the same item can be matched across stores. Repeat runs add price history, stock changes and new or delisted items. The common core is below; each store adds its own fields.
Expertly customized web scraping services at a fraction of the cost of developing your own software.
€199 / one-time
setup fee - included
€169 / mo
setup fee €499
€229 / mo
setup fee €499
€349 / mo
setup fee €499
€549 / mo
setup fee €799
Delivery: CSV, JSON or XLSX files, our API or a JSON feed. MCP server on request.
Full plan details →
About 1.9 million auto parts listings from eBay.de with brand, OEM, EAN and fitment, plus titles and specs machine-translated for the client's team.
Learn More
A one-off Takealot product list for an entrepreneur choosing niches: only products rated 4 stars or higher with at least 50 reviews.
Learn More
Trendyol product data for an e-commerce startup reselling Turkish fashion: 27 categories in JSON on FTP, with in-stock status for every size.
Learn More
A cosmetics company got monthly prices and promotions from 40+ beauty stores in 15+ countries, translated to English and fed into its existing import.
Learn More
A consultancy working for a fragrance brand owner got sellers, official-store flags and USD prices for 25 fragrances on about 30 marketplaces in 12 countries.
Learn More
Three Amazon US lighting categories collected in full for a lighting manufacturer, with description text and images from brand content blocks.
Learn More
We go from idea to live scraper in days - no dev team required on your side.
As a full-service data provider, we handle scraper maintenance, monitoring, and delivery - no tech effort needed from your team.
Get large volumes of product info from web pages at a fraction of the cost of building your own extraction services - without compromising on quality.
Get the data you need, when you need it - always clean, structured, and ready to feed your database or internal systems.
From hundreds to millions of products - our automated pipelines handle any volume of data across global internet ecommerce platforms.
We guarantee structured, high-quality datasets ready for analysis, machine learning, or direct import into your systems.
Learn how to use web scraping to solve data problems for your organization
Six Polish ecommerce websites worth scraping in 2026, from Allegro and Ceneo to Castorama and Biedronka: the fields each site shows, what only it adds, how to merge them into one dataset and what our Allegro and Castorama projects needed.
If you sell online, run a marketplace, or advise e-commerce clients, you already know why eBay matters: it’s one of the few places where big retailers compete side by side with thousands of small merchants and private sellers.
E-commerce teams do not just need “some” competitor data anymore. They need a continuous stream of real prices, discounts, stock levels, reviews, and seller behavior from the platforms that actually shape their markets.
It is the automated collection of public product data from online stores and marketplaces: prices, discounts, stock, sellers, ratings, reviews and product attributes. A scraper visits the pages on a schedule and turns every product into a structured record, so you can compare prices and assortment across stores without copying anything by hand.
Order it as a managed service: we build, run and maintain the scrapers, and you receive clean product data on your schedule without writing code. We cover 191 ecommerce sites; one project delivered over 225,000 electronics products a day from two store catalogs, another about 10.8 million marketplace rows a month for a seller. A one-time dataset of up to 100,000 rows costs EUR 199 and comes as CSV, Excel or JSON. Monthly plans cost from EUR 169 a month plus a one-off EUR 499 setup fee that includes the sample dataset and the first month, and can also deliver through our API or as a JSON feed. Project assessment is free.
We discover products through category pages, search where the site allows it and product sitemaps, collect each product page, and normalise the fields to one schema. Products are matched across stores by EAN, GTIN or model number. Collection runs at a measured pace, and parsers are updated whenever a store changes its pages.
We work with 150+ stores and marketplaces, including Amazon, Walmart, eBay, Etsy, Allegro, eMAG, Zalando, MediaMarkt, Lazada, Shopee and Mercado Libre. If your store is not on the list, we assess it before quoting.
Yes. Daily or several-times-a-day runs record each product's price, discount and stock, so you get price history, price-change alerts and minimum advertised price breaches per seller. Most price monitoring projects run daily on a fixed list of products or categories.
Yes. Send a list of EANs or your own catalogue, and we return the matching offers in each store with price, seller and stock. One of our projects collected the lowest Allegro prices for 150,000 EANs; another tracked one brand across eight Amazon marketplaces in a single dataset.
Yes. We collect only publicly available data - everything a store shows to visitors: products, prices, stock, sellers, reviews and ratings - and we collect it legally, without logging in. Personal data in reviews, such as reviewer names, is handled under GDPR.
As CSV, Excel, JSON, JSON Lines or XML, delivered by SFTP, Amazon S3, Google Drive, Dropbox or email, as a JSON feed, or through our API straight into your pricing tool, PIM or data warehouse. An MCP server is available on request.
Hourly, daily, weekly or monthly. Fast-moving categories such as electronics and marketplaces usually run daily or several times a day; slower catalogues weekly.
Plans start at 169 EUR per month and depend on the number of stores, the volume of products and the refresh frequency. Project assessment is free, and a sample dataset lets you check the fields and format first.
Yes. We sign NDAs and data processing agreements before a project starts.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
KRS: 0000996672
NIP: 5423457175
REGON: 523384582
