The Best E-Commerce Dataset Providers of 2026
Companies like Bright Data, Oxylabs, Exellius, and Grepsr offer different ways to get e-commerce data. Some charge $50,000 for a single dataset, while others provide low-cost monthly plans or real-time APIs.
This guide compares the pricing structures, features, and delivery methods of these providers. It also examines the advantages of real-time APIs over purchasing large, static datasets.
Best e-commerce dataset providers
Bright Data
Bright Data is currently the market leader in dataset scale and coverage of e-commerce platforms. The dataset provider offers a comprehensive collection of e-commerce datasets, including Amazon, Walmart, Target, and Shein. The datasets are available in multiple formats, including JSON, CSV, and Parquet.
Bright Data delivers high levels of customization, enabling businesses to tailor and filter data to their evolving needs precisely. Whether choosing “off-the-shelf” datasets or commissioning custom-crawled data.
Offerings:
- Pre-built datasets: Access large-scale, ready-to-use snapshots of major retailers (Amazon, Walmart, Target, eBay, AliExpress).
- On-demand scraping: With their scraper APIs, users capture the data they need, when they need it, enabling total control and timely insights.
Pricing:
- Subscription: ($50,000 for the initial delivery of a dataset containing ~393M records). After the first payment, it drops to ~$6,364/month for ongoing updates.
- Frequency: Offers Monthly, Quarterly, or Bi-annual snapshots with “Smart Updates” to save on costs.
- Key advantage: Unmatched scale and data freshness (129M records updated monthly).
Oxylabs
Oxylabs offers e-commerce datasets for major marketplaces like Amazon and Walmart. Customers benefit from flexible data collection frequencies, including one-time, monthly, quarterly, and biannual deliveries to fit their unique needs.
The provider supports its dataset collection with high-quality proxy infrastructure, ensuring clients receive accurate, localized pricing data tailored to specific zip codes.
The datasets can be delivered in JSON and any other standard formats depending on customer needs (e.g., ndJSON, CSV).
Pricing:
Custom pricing based on specific data needs.
Exellius
Exellius offers Amazon seller data for the US, UK, India, and Germany to help you connect with the right retail partners. They customize the data to fit your business needs, such as identifying sellers to supply or new wholesale customers, and include verified contact details for each potential partner.
The dataset is updated every month. The Amazon FBA leads package gives you the business name, contact person, verified email address, and other useful details. You can receive the data in CSV or Excel formats, or via API integration.
Pricing:
- Credit-based: Ranging from $59/month (6,000 credits) to $199/month (25,000 credits).
- Free trial with 75 credits.
Grepsr
Grepsr’s ecommerce datasets cover product details, promotional discounts, out-of-stock trends, and past prices. You can receive the data straight into your analytics tools, cloud storage like S3, or through APIs. It’s available to download in JSON and CSV formats.
Grepsr also creates synthetic datasets. These AI-generated datasets mimic real patterns in product catalogs, reviews, employment data, and more. They are helpful for AI training, demos, and testing. E-commerce dataset types include product listings, price history, category pages, customer reviews, MAP, and promotional data.
Pricing:
- Starter package ($350): One-time extraction from simple websites.
Public vs. paid e-commerce datasets: Which is right for you?
Deciding between a public (free) dataset and a paid commercial source comes down to whether your goal is learning or competing.
- Public Datasets include sources such as Kaggle, the UCI Machine Learning Repository, and Google Dataset Search.
- The downside is that you cannot make business decisions, like dynamic pricing, using public data because prices and stock levels are outdated.
Paid Datasets come from providers like Bright Data, Grepsr, and Oxylabs.
- With paid datasets, you pay for up-to-date, well-organized information. For example, Bright Data charges $50,000 or more for fresh, actionable data that shows the current market.
- If your return on investment relies on accuracy and daily updates, public data is risky. But if you only need test data for your developers, public sources are a good place to start.
What to look for in an e-commerce dataset?
Price matters, but it’s just one part of the decision. Here are four technical factors that set enterprise-grade data apart from basic datasets.
- Schema depth: Does the dataset include product variants? For example, a T-shirt is the parent, while ‘Blue, Size Large’ is the SKU. If you only get the parent price, you miss important details.
- Fill rate and data errors: Ask for a sample and see how many ‘N/A’ values appear. Reliable providers like Grepsr use human checks to make sure the ‘Price’ column doesn’t get mixed up with ‘Shipping Cost’ or ‘Customer Rating’ by mistake.
- Update logic: Large datasets, such as Bright Data’s 393 million records, are too large to upload every day. Look for providers that offer ‘Smart Updates,’ so you receive only the rows that changed.
- Handling anti-bots: E-commerce sites use strong protections like DataDome and Akamai. Make sure your provider guarantees a ‘Success Rate.’ If they can’t get past Amazon’s latest security update, your data may have gaps or missing products.
Alternatives to e-commerce datasets
When you buy a dataset, such as the $50k Bright Data snapshot, it’s like getting a map of the market. If you use a real-time e-commerce scraper API, like those from Oxylabs or Bright Data, it’s more like having a live satellite feed.
In e-commerce, prices on sites like Amazon or Expedia can change several times an hour. By the time you download a 100GB dataset, about 10% of the prices may already be out of date.
Use a dataset if you need historical analysis, such as tracking how prices changed last year. Use a real-time API if you need up-to-date information for live operations.
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