The Best E-Commerce Dataset Providers of 2026

Paid dataset providers offer up-to-date, large-scale e-commerce data with defined coverage and regular updates, supporting applications like competitor price and stock-level tracking.

In contrast, free e-commerce datasets are usually static and outdated, limiting their value for real-time decision-making, including dynamic repricing.

Price comparison table of e-commerce datasets

Best e-commerce dataset providers

Bright Data is one of the largest providers by publicly stated dataset volume. It provides both prebuilt marketplace snapshots and on-demand scraping via its scraper APIs.

• Sources: Amazon, Walmart, Target, eBay, AliExpress, Shein.
• Formats: JSON, CSV, Parquet.
• Updates: daily to monthly; changed-rows updates avoid re-downloading the full set.

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.

• Sources: Amazon and Walmart.
• Formats: JSON, ndJSON, CSV.
• Delivery frequencies: one-time, monthly, quarterly, biannual.

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.

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.

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.
• If your return on investment depends on accuracy and daily updates, relying on public data is risky.

What to check before buying a paid dataset

Price is one factor. Four technical factors separate enterprise-grade data from a basic export.

• Schema depth: Does the data include product variants? A T-shirt is the parent; “Blue, Large” is the SKU. Parent-only pricing hides the detail you need.
  
• Fill rate and errors: Request a sample and count the “N/A” values. Check that fields are not mixed up, for example, price columns holding shipping cost or rating.
  
• Update logic: Large sets are too big to re-upload daily. Prefer “changed-rows” updates that deliver only what changed.

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Alternatives to e-commerce datasets

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 be out of date.

Use a dataset if you need historical analysis, such as tracking price changes from last year. Use a real-time API if you need up-to-date information for live operations.

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@misc{karatas2026,
  author = {Karatas, Gulbahar},
  title  = {{The Best E-Commerce Dataset Providers of 2026}},
  year   = {2026},
  month  = jun,
  howpublished    = {\url{https://aimultiple.com/ecommerce-datasets}},
  note   = {AIMultiple. Retrieved June 5, 2026}
}

Gulbahar Karatas

Industry Analyst

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Gülbahar is an AIMultiple industry analyst focused on web data collection, applications of web data and application security.

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