6 E-Commerce Sites Like eBay to Scrape in 2026

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. It is noisy, fast, and incredibly informative.

But treating eBay as your only source of marketplace data is a strategic blind spot. Pricing moves first on Amazon and Walmart. Cross-border supply often starts on AliExpress. Niche demand shows up on Etsy. Consumer-to-consumer dynamics unfold on Mercari. Regional marketplaces like Allegro can completely reshape what “competitive price” means in that country.

In other words: if you only scrape eBay, you are seeing one slice of the market, not the whole picture.

In this guide, we will look at six marketplaces that behave like eBay from a data perspective – multi-seller, competitive, dynamic – and are worth scraping alongside it: Amazon, Walmart, AliExpress, Rakuten, Etsy, and Mercari. All of them are already supported by ScrapeIt as fully managed scrapers, alongside eBay itself.

If you need a bigger view of what we do for online retail, you can always start on our Ecommerce Data Scraping Services page. If you are specifically interested in eBay, there is a dedicated eBay scraper. 

Who This List Is For

Marketplaces and Price Comparison Sites

If you operate a marketplace or comparison engine, your job is to show buyers the best offers – which means understanding pricing, discounts, and assortment on rival platforms:

• Benchmark your sellers’ offers against Amazon, Walmart, AliExpress and others.
• Identify high-performing third-party sellers on those platforms and recruit them.
• Spot categories where your marketplace is under-represented vs. competitors.

Scraping “eBay-style” marketplaces lets you build a cross-platform product feed that your BI tools, merchandising team, and sales team can all use.

Online Stores, Brands, and Manufacturers

For direct-to-consumer brands and online stores, marketplaces are both competitors and demand signals:

• Track competitor pricing and discount patterns across Amazon, eBay, Walmart and more.
• Detect assortment gaps – products rivals sell on marketplaces that you do not carry yet.
• Monitor MAP violations and unauthorized listings that undercut your brand.

Here, marketplace scraping is an input into pricing tools, PIM systems, and assortment planning – the kind of workflows that Ecommerce data scraping services are designed to power.

Consultancies, Market Research Firms, and AI / Data Teams

If you build models or reports for others, you need breadth:

• Multi-country product datasets for market sizing and competitor benchmarking.
• Historical price and availability data to train pricing and demand models.
• Rich text (descriptions, titles, reviews) for semantic search and recommendation systems.

Using managed scrapers for multiple marketplaces lets your team focus on analysis, not crawling. You define the schema and cadence; ScrapeIt maintains the extraction layer.

Resellers, Arbitrageurs, and Secondary Market Players

Finally, there is the arbitrage use case – buying low on one platform and selling higher on another:

• Compare used prices between Mercari, eBay, and local classifieds.
• Track clearance and liquidation deals on Walmart and Amazon.
• Spot mispriced assets in collectibles, fashion, and electronics.

Here, frequency and speed matter more than historical depth: you want fresh snapshots, often multiple times per day, something ScrapeIt supports across all the marketplaces listed in the Popular Sites We Scrape catalogue.

What Data You Can Extract From Marketplaces Like eBay

Each marketplace looks different on the surface, but the high-value data layers are similar. When we design an engagement via ecommerce data scraping services, we usually work across the following layers.

1. Product and Catalog Layer

• Product titles, long and short descriptions.
• Brand, model, and manufacturer identifiers (when available).
• Category and subcategory placement, including breadcrumbs.
• Attributes and variants: size, color, material, capacity, etc.
• Media: main image, gallery images, sometimes video.

2. Pricing and Promotion Layer

• Current price, original price, and strike-through values.
• Coupons, promo codes, and discount badges.
• Bundle pricing (multi-packs, accessories, “with warranty”).
• Dynamic offers such as lightning deals or “limited time” discounts.

3. Availability, Inventory, and Logistics Layer

• Stock status (in stock, low stock, out of stock, backorder).
• Estimated delivery dates and shipping speeds.
• Shipping fees, carriers, and pick-up options.
• Warehouse / ship-from locations where visible.

4. Seller, Review, and Trust Layer

• Seller or shop name, rating, and volume of feedback.
• Number of items sold, watchers, or favorites (where available).
• Buyer reviews: rating, text, date, optional media.
• Badges: “Top Rated Seller”, “Verified”, “Star Seller”, etc.

5. Recommendation and Behavior Layer

• “Similar items” and “People also bought” carousels.
• Bestseller rankings within categories or subcategories.
• Cross-listing between marketplaces (where the same SKU shows up under different sellers).

All these fields can be combined into a single schema and delivered in your preferred format (CSV, JSON, Excel) through ScrapeIt’s managed extraction, exactly as described on ecommerce data scraping services.

1) Amazon – The Default “eBay Alternative” for Global Volume

Amazon is the obvious counterweight to eBay. It is the world’s largest e-commerce platform by revenue, mixing first-party retail with a massive marketplace of third-party sellers. For many categories and regions, Amazon defines the reference price that everyone else reacts to.

ScrapeIt runs a dedicated Amazon scraper that is used by marketplaces, brands, and market research teams to monitor prices, availability, and product performance at scale.

What Makes Amazon Different

• Highly optimized product pages with rich specs and content.
• Complex Buy Box dynamics – multiple sellers competing on one listing.
• Strong merchandising signals (“Amazon’s Choice”, “Best Seller”).
• Multiple country domains (.com, .de, .co.uk, etc.) with different currencies and assortments.

High-Value Data to Scrape From Amazon

• Product title, brand, category, attributes, and ASIN.
• Prices, coupons, and deal badges for each offer.
• Seller information (for marketplace offers), including ratings and feedback counts.
• Availability and delivery promises (Prime vs non-Prime, same-day, next-day, etc.).
• Ratings and reviews, including review text, images, and dates.
• Bestseller rank and category rankings where visible.

Key Scraping Challenges and Nuances

• Dynamic layout and experiments. Amazon continuously tests page layouts; robust scraping needs to target underlying structured data rather than brittle CSS selectors.
• Multiple variations per product. Sizes, colors, and pack sizes are all modeled under one parent listing. You need logic to map each variant to its price, availability, and images.
• Country localization. Each regional site can differ in structure, language, and assortment; scraping must respect that while still mapping back to a common schema.
• Anti-bot measures. Rate limiting and blocking are common, so a managed solution is usually more cost-effective than building your own rotating proxy + browser stack.

If Amazon is a major input in your pricing or catalog decisions, it is worth reading the dedicated blog article linked from the Amazon scraper page, which covers its specific challenges in more depth.

2) Walmart – U.S. Retail & Marketplace Hybrid With Huge Pricing Signal

Walmart is both a giant retailer and a fast-growing online marketplace. For U.S. mass-market categories – groceries, household goods, general merchandise – Walmart’s prices and promotions strongly influence what consumers expect to pay.

ScrapeIt offers a tailored Walmart data scraper that collects product information, prices, reviews, and availability in a format suited to your systems.

What Makes Walmart Different

• Combination of first-party and third-party marketplace offers.
• Strong in groceries and everyday essentials, where eBay is less competitive.
• Store pickup and local stock availability integrated into online listings.
• Frequent “Rollback” and special promotions that matter for price tracking.

High-Value Data to Scrape From Walmart

• Product names, descriptions, package sizes, and UPCs where visible.
• Current price, rollback price, and promotional labels.
• Stock status, including local store availability and pickup options.
• Seller data for marketplace offers (ratings, number of ratings).
• Customer reviews and Q&A, which can be used to understand quality issues and expectations.

Key Scraping Challenges and Nuances

• Geo-sensitive content. Price and availability can change by ZIP code; scraping strategy must account for location parameters.
• Dynamic, JavaScript-heavy pages. Many details load asynchronously and require the right rendering approach.
• Multiple domains. Walmart operates different localized sites (like walmart.ca), each with its own quirks.
• Integration with other marketplaces. To compare Walmart vs Amazon vs eBay, you need consistent taxonomies and units – something ScrapeIt can normalize when setting up multi-site jobs.

For clients who want an integrated view of U.S. retail, we often combine Walmart data with Amazon and eBay feeds and deliver a unified dataset via the workflow described on ecommerce data scraping services.

3) AliExpress – China-to-World Marketplace With Unmatched Assortment

AliExpress is the world’s largest B2C marketplace for cross-border shopping, connecting mostly Chinese sellers with buyers around the globe. For many categories, it is where new products, designs, and suppliers appear before they are picked up by western brands and marketplaces.

Our AliExpress scraper is built for teams that want to monitor global supply, dropshipping trends, and early-stage demand signals.

What Makes AliExpress Different

• Huge catalog of low-priced products across almost every category.
• Strong emphasis on cross-border logistics – multiple shipping options, delivery times, and “local warehouse” flags.
• Promotional cycles (11.11, etc.) that drastically affect pricing.
• Seller-side metrics (orders count, store rating) that give early demand signals.

High-Value Data to Scrape From AliExpress

• Product names, SKUs, item codes, and variant structures (color, size, bundle).
• Multi-level pricing: original price, current price, discount percentage, and tiered prices.
• Number of orders and store rating as demand/quality indicators.
• Seller data: store name, rating, ship-from country.
• Shipping methods and transit times by destination country.
• Reviews, including images and sizing feedback, valuable for quality checks.

Key Scraping Challenges and Nuances

• Dynamic interface. Listings rely heavily on JavaScript and are often updated on the fly, especially during big campaigns.
• Regionalization. Pricing and availability vary by country, language, and currency; robust projects must model this explicitly.
• Campaign overlays. Sales events introduce extra layers of UI and pricing logic that change how discounts are represented.
• Volume. Even narrow category slices can return huge numbers of listings; you need clear limits and sampling strategies.

Our experience building the AliExpress data scraper is often used as the template for other dynamic marketplaces listed under Popular Sites We Scrape, such as Shopee, Tokopedia, or Flipkart.

4) Rakuten – Japan & Asia’s Loyalty-Driven Marketplace

Rakuten is one of Japan’s leading marketplaces and a key player in several other Asian markets. It blends traditional marketplace structure with a strong loyalty program and coupon ecosystem, which makes it a powerful source of regional price and promotion data.

The Rakuten data scraper from ScrapeIt focuses on extracting those loyalty- and promotion-driven nuances that standard product feeds tend to miss.

What Makes Rakuten Different

• Multi-seller marketplace with deep penetration in Japan and selected Asian markets.
• Heavy use of coupons, points, and special campaigns.
• Category and merchandising structures that reflect local purchasing behavior, not western norms.

High-Value Data to Scrape From Rakuten

• Product titles, attributes, and seller identities.
• Current prices, campaign prices, and coupon indicators.
• Shipping options and installment/financing details.
• Ranking and bestseller indicators within categories.
• Reviews with localization (e.g. Japanese text, dates, and rating distributions).

Key Scraping Challenges and Nuances

• Localization. Japanese (and sometimes other Asian languages) require proper encoding and handling of non-Latin scripts.
• Multiple sites. Rakuten operates different regional domains; they may need to be scraped separately and then mapped together.
• Promotion logic. Points and coupon systems often appear as badges and banners, not just numeric fields – the scraper has to interpret this correctly.

For clients already collecting data from Amazon, Walmart, and eBay through our e-commerce scraping services, adding Rakuten scraping is a pragmatic way to extend coverage into Japan and adjacent markets without changing their internal analytics stack.

5) Etsy – A Niche “eBay Alternative” for Handmade, Vintage & Digital Goods

Etsy is where handmade, vintage, and creative products live. It is not a mass-market retailer; it is a marketplace of ideas and micro-brands. For categories like jewelry, home decor, print-on-demand, and digital templates, Etsy often provides better competitive and trend insight than eBay or Amazon.

Our Etsy scraper focuses on the fields that make or break a listing in Etsy’s search and discovery algorithms: titles, tags, materials, photos, and reviews.

What Makes Etsy Different

• Listings are often unique, limited-run, or made-to-order.
• Search and recommendation lean heavily on tags and long-tail keywords.
• Shops are small: the seller universe is fragmented and diverse.

High-Value Data to Scrape From Etsy

• Titles, long descriptions, and especially tags and keywords.
• Materials, customization options, production method (“handmade”, “vintage”, “supplies”).
• Prices, shipping fees, and delivery estimates.
• Shop information: seller name, location, rating, total sales.
• Badges such as “Bestseller” or “Star Seller”.
• Review text with focus on quality, expectations, and shipping experience.

Key Scraping Challenges and Nuances

• Non-standard attributes. Many attributes (e.g., style, occasion, theme) exist only as tags or free text – they require careful extraction for analytics and AI training.
• Strong seasonality. Demand spikes around events (Christmas, Valentine’s Day, etc.) – cadence should be adjusted to capture these dynamics.
• Creative assets. Listings rely heavily on images; projects that need image data must plan for larger payloads and storage.

Because Etsy sits closer to the “long tail” of demand, data collected via the Etsy scraper is frequently used as training material for recommendation and search models, often alongside more standardized data from Amazon and eBay fetched via ecommerce data scraping services.

6) Mercari – C2C “Mini-eBay” for Used & New Items

Mercari is one of the leading consumer-to-consumer marketplaces in Japan and the U.S. It is particularly important for second-hand fashion, electronics, collectibles, and everyday items. If eBay is your benchmark for secondary market prices, Mercari is the logical second source.

The Mercari scraper from ScrapeIt collects structured product, seller, and pricing data from Mercari’s web interface, giving you a second lens on resale and circular-economy behavior.

What Makes Mercari Different

• Strong focus on second-hand goods and casual sellers.
• Mobile-first experience with some behavioral signals that differ from desktop marketplaces.
• High listing churn – items get listed and sold (or removed) quickly.

High-Value Data to Scrape From Mercari

• Item titles, categories, brand, size, and condition (new/used).
• Listing price, shipping inclusion, and historical price changes where visible.
• Seller ratings, number of sales, and any status badges.
• Location (where exposed) and shipping methods.
• Images and descriptions, often useful for recognizing real-world wear and authenticity clues.

Key Scraping Challenges and Nuances

• Mobile-centric design. Layouts and endpoints optimized for mobile can differ from typical desktop patterns.
• Fast-moving inventory. To catch price and availability before a listing disappears, you may need a more frequent scraping schedule than on Amazon or Walmart.
• Region differences. Mercari Japan and Mercari US behave differently in language, categories, and price ranges – sometimes they need separate handling.

When combined with eBay data via the eBay scraper, Mercari data gives traders, marketplaces, and analytics teams a far more robust picture of what second-hand products are really worth in different regions.

Beyond the Big Six: Regional eBay-Style Marketplaces You Should Know

Global platforms are only half the story. In many countries, regional marketplaces dominate everyday consumer behavior and pricing. ScrapeIt already supports dozens of these platforms; a few important examples:

Allegro – Poland and Central Europe’s Marketplace Leader

Allegro is effectively “eBay + Amazon” for Poland and parts of Central Europe. For categories like electronics, household goods, and automotive, it is the primary reference marketplace.

ScrapeIt not only runs an Allegro scraper, but also maintains a full production pipeline documented in the case study The Lowest Allegro Prices from 150K EANs Collected, where 150,000 EANs are searched daily to deliver the lowest marketplace price per SKU.

Other Regional Marketplaces

In addition to the global “eBay-style” platforms covered above, ScrapeIt supports a wide range of regional marketplaces, including (among others):

• eMAG in Eastern Europe.
• Wildberries in Eastern Europe and Russia-adjacent markets.
• Shopee, Tokopedia, Lazada in Southeast Asia.
• Flipkart in India.

In practice, most sophisticated clients mix global and regional sources into a custom basket that matches their footprint and goals.

Conclusion

If you rely on marketplace data to make decisions, eBay is essential – but incomplete. Serious e-commerce and retail teams treat it as one signal among many. Amazon, Walmart, AliExpress, Rakuten, Etsy, and Mercari provide their own slices of reality: mass-market pricing, cross-border supply, regional tastes, creative trends, and second-hand liquidity.

Scraping these “sites like eBay” in a structured, continuous way is what turns them from noisy websites into reliable market intelligence. With ScrapeIt, you do not have to own the crawling infrastructure – you just define the marketplaces, the fields, and the rhythm, and our team delivers ready-to-use datasets.

If you want to explore how this could work for your marketplace, brand, or client portfolio, start with ecommerce data scraping services or browse the individual scrapers for eBay, Amazon, Walmart, AliExpress, Rakuten, Etsy, and Mercari. From there, it is a short step to a pilot dataset that shows you what multi-marketplace visibility really looks like.

FAQ

1. Why shouldn't I rely on eBay as my only source for marketplace data?

eBay is informative, but it only shows one slice of the market. Key pricing signals often move first on platforms like Amazon and Walmart, cross-border supply originates on AliExpress, niche demand appears on Etsy, and C2C dynamics unfold on Mercari. Scraping these "eBay-style" marketplaces provides a complete, multi-seller, competitive view of the market.

2. Which six specific marketplaces are recommended to scrape alongside eBay?

The six marketplaces recommended for their competitive, dynamic, and multi-seller nature are Amazon, Walmart, AliExpress, Rakuten, Etsy, and Mercari.

3. What types of businesses benefit most from scraping multiple marketplaces?

Four main groups benefit: Marketplaces and Price Comparison Sites (for benchmarking and recruitment); Online Stores, Brands, and Manufacturers (for price tracking, assortment gaps, and MAP monitoring); Consultancies and Market Research Firms (for breadth, historical data, and AI training); and Resellers/Arbitrageurs (for speed and finding mispriced assets).

4. What are the five main layers of high-value data that can be extracted from these marketplaces?

The data can be broken down into five layers: 

1) Product and Catalog Layer (titles, attributes, media); 

2) Pricing and Promotion Layer (current/original price, coupons, bundles); 

3) Availability, Inventory, and Logistics Layer (stock status, delivery dates, shipping fees); 

4) Seller, Review, and Trust Layer (seller ratings, reviews, badges);

5) Recommendation and Behavior Layer (similar items, bestseller rankings).

5. Besides the "Big Six," what other types of marketplaces should businesses consider scraping?

Businesses should also consider Regional "eBay-Style" Marketplaces that dominate specific areas, such as Allegro (Poland/Central Europe), eMAG, Wildberries (Eastern Europe), Shopee, Tokopedia, Lazada (Southeast Asia), and Flipkart (India), to ensure their data matches their specific geographical footprint.