Best Amazon Datasets in 2026: A Practical Comparison

Amazon runs on numbers. Every product listing, price change, review, and seller rating adds up to one of the largest retail datasets on the internet. For businesses trying to understand pricing trends, track competitors, or fuel machine learning models, that data is often more valuable than the products themselves.

The problem is getting it. Scraping Amazon directly takes serious infrastructure, and the site is not exactly friendly to automated requests. That is why a growing number of companies now sell ready-made Amazon datasets instead, structured, cleaned, and delivered on a schedule that suits the buyer.

This guide looks at seven providers worth knowing about in 2026. The list is based on publicly available information from each company, and it is meant to give a fair picture rather than push any single option. What works for a five-person startup will not always work for an enterprise team, so pricing and use case matter as much as raw record counts.

Quick Overview

• Oxylabs: Custom-built e-commerce datasets with flexible delivery
• Bright Data: Large-scale Amazon datasets with smart updates
• APISCRAPY: Real-time extraction with AI-assisted filtering
• Grepsr: Best seller and historical trend tracking
• TagX: Multi-platform product data across Amazon, Flipkart, and more
• AWS Data Exchange: Marketplace access to hundreds of third-party datasets
• Exellius: Seller contact data for outreach and lead generation

Oxylabs

Oxylabs is best known for its proxy network, but its e-commerce dataset service has become a solid option for companies that do not want to manage scraping infrastructure themselves. The datasets cover Amazon and other major marketplaces such as Walmart, and include product names, brands, prices, seller counts, review volumes, ratings, and availability.

What sets Oxylabs apart is how the service is built. There is no fixed catalog to browse. Instead, the company works with each client to define a data schema, then delivers samples before committing to a full contract. Delivery options include JSON or CSV files, SFTP, or direct upload to cloud storage such as AWS S3 or Google Cloud Storage, on a one-time, monthly, quarterly, or bi-annual basis.

Strengths

• Fully custom data schema rather than a fixed template
• GDPR and CCPA compliant sourcing
• Flexible delivery methods and frequency
• Dedicated support channel for ongoing projects

Limitations

• No public pricing, so smaller buyers need to request a quote
• Turnaround depends on how complex the custom request is

Pricing: Listed starting price begins around $1,000, though the final cost depends on scope, refresh frequency, and data volume.

Bright Data

Bright Data offers one of the larger ready-made Amazon dataset catalogs on the market, with more than 1.4 billion records spread across seven distinct datasets covering products, reviews, seller profiles, best sellers, and cross-platform comparisons with Walmart. Each dataset can be filtered using a plain language AI prompt, which cuts down on manual filtering work before export.

Formats include JSON, CSV, NDJSON, XLSX, and Parquet, and delivery can go through Snowflake, Amazon S3, Google Cloud, Azure, or SFTP. The company also positions its datasets as ready for AI and LLM use, with documentation and code samples in several programming languages for teams building on top of the data rather than just analyzing it in a spreadsheet.

Strengths

• Broad catalog covering products, reviews, and seller data separately
• AI-based filtering to narrow large datasets before purchase
• Multiple export formats and cloud delivery options
• Subscription discounts for recurring updates

Limitations

• Costs can add up quickly for high volume or frequent refresh needs
• Some technical setup is required to make full use of the API delivery

Pricing: Starts at $250 for a minimum order, with per-record pricing going up to roughly $0.0025 depending on volume and refresh rate. You can review the full breakdown on Bright Data’s Amazon dataset page.

APISCRAPY

APISCRAPY focuses on real-time Amazon data extraction rather than static, periodically refreshed files. The service pulls product details, pricing, ratings, and reviews as they change, which makes it a better fit for businesses tracking fast-moving categories where a weekly or monthly snapshot would already be outdated.

The platform also collects seller performance data alongside product listings, and it uses automation to reduce the manual work usually needed to keep large scraping jobs running. Because there is no infrastructure for the buyer to maintain, setup time is generally shorter than building an in- house scraper.

Strengths

• Real-time updates rather than fixed refresh cycles
• No infrastructure required on the buyer’s end
• Includes seller performance metrics alongside product data

Limitations

• Relies on public web data, so some fields may be inconsistent across categories
• Higher volume extraction may still need extra technical handling on the client side

Pricing: Plans start at $499 per month for a limited number of source websites, scaling up for larger multi-site packages.

Grepsr

Grepsr’s Amazon offering centers on best seller tracking rather than full catalog data. It provides sales rank information organized by category and sub-category, along with up to two years of historical data, which is useful for spotting seasonal patterns or predicting which products might trend next.

This is a narrower tool than some of the others on this list. It will not replace a full product or review dataset, but for teams specifically focused on competitive intelligence around what sells well on Amazon, the historical depth is a genuine advantage.

Strengths

• Up to two years of historical best seller data
• Organized by category for easier comparison
• Useful for tracking competitor product performance over time

Limitations

• Narrower scope than full product or review datasets
• Less detail on individual product attributes

Pricing: Custom quotes, with reported starting costs around $350 for a defined project scope.

TagX

TagX takes a cross-platform approach, pulling product data from Amazon alongside Flipkart, Tmall, and other marketplaces. This is a useful feature for companies operating in multiple regions or comparing pricing strategy across platforms rather than looking at Amazon in isolation.

Coverage spans more than 70 countries, and the datasets include descriptions, pricing, availability, and customer reviews. Data can be delivered in CSV or XLS format, with updates available regularly for teams that need ongoing monitoring rather than a single export.

Strengths

• Covers multiple e-commerce platforms, not just Amazon
• Wide country coverage for global comparisons
• Real-time updates available

Limitations

• Pricing sits on the higher end for full-scale packages
• Best suited to teams already comfortable working with structured data feeds

Pricing: One time purchases start around $900, with subscription options available for ongoing access.

AWS Data Exchange

AWS Data Exchange is not a single Amazon dataset provider so much as a marketplace where more than 300 vendors list retail, finance, and other industry data, including Amazon-related sets. For teams already running infrastructure on AWS, this can be the path of least resistance, since datasets integrate directly with existing AWS services without extra setup.

The tradeoff is that quality and depth vary by vendor. Buyers need to evaluate individual listings rather than expect a consistent standard across the whole catalog, and pricing is set independently by each data provider rather than by Amazon itself.

Strengths

• Seamless integration for teams already using AWS
• Wide range of industries and data types in one place
• Flexible delivery through APIs, tables, or files

Limitations

• Catalog size can make it harder to find the right dataset
• Quality depends on the individual vendor, not a single standard

Pricing: Varies by dataset and provider, viewable directly in the AWS Data Exchange catalog.

Exellius

Exellius takes a different angle from the rest of this list. Instead of product or pricing data, it focuses on seller contact information, with more than a million verified records covering Amazon and non-Amazon sellers across 250 countries. Each entry can include email addresses, direct phone numbers, and LinkedIn profiles.

This makes it a fit for outreach and lead generation rather than market or pricing analysis. Businesses looking to connect with decision-makers at seller companies, rather than analyze product trends, will get more value here than from a traditional product dataset.

Strengths

• Large, filterable contact database
• Multiple contact points per record, including verified email and LinkedIn
• Free tier available for smaller-scale testing

Limitations

• Not designed for product, pricing, or review analysis
• Larger contact lists can get expensive at scale

Pricing: A free plan offers a limited number of credits, with paid plans starting at $59 per month for expanded access.

Choosing the Right Provider

There is no single best Amazon dataset provider for every use case. Teams that need broad product and review coverage will likely lean toward Bright Data or Oxylabs. Businesses focused specifically on tracking what is selling well may find more value in Grepsr’s historical best seller data. Multi-platform sellers comparing pricing across marketplaces will get more mileage out of TagX, while outreach and partnership teams are better served by something like Exellius.

Budget, refresh frequency, and the specific fields you need are usually better starting points than record count alone. A dataset with a billion rows is not useful if it is missing the three or four fields your analysis actually depends on. It is worth requesting a sample from any provider before committing to a larger contract, since most of the companies above offer one.