How to Compare Flipkart Listings and Product Details

The Flipkart Listings Scraper accepts search terms, category URLs, and direct product URLs. It returns listing prices and identifiers, with optional product details. I maintain the Actor.

This example researches wireless mice. You will collect 25 listing records, identify comparable models, and decide whether specifications or reviews justify using detail mode.

Check proxy access and choose the source

The deployed input defaults to residential proxies in India. Flipkart commonly challenges datacenter traffic, so residential access is a practical prerequisite. Keep the Indian proxy configuration for this example and include proxy traffic in your budget.

Use searchQueries to discover products, categoryUrls for a browse page, or productUrls for specific product pages you already know. maxItemsPerInput applies to each query or URL, not the total run. Zero removes that input cap but does not remove Flipkart's pagination limits.

includeDetails: false returns listing data. Enabling it visits product pages for description, brand, sample reviews, image gallery, and detailed specifications. Choose that mode when missing specifications prevent a fair comparison, rather than requesting it automatically for a broad search.

Budget for the sample

Pricing as of September 6, 2026. A listing costs $0.001. With includeDetails enabled, the item-detail event costs $0.008 instead of the listing event for that product. The Actor-start event is $0.00005 per GB of allocated memory, with a minimum of one event. Check current Actor pricing and the run charge breakdown; these calculations describe Actor events, and separately billed platform or proxy resources may also apply.

Collect 25 Flipkart products

Open the Actor on Apify. In Input, switch to the JSON editor and paste:

{
  "searchQueries": [
    "wireless mouse"
  ],
  "categoryUrls": [],
  "productUrls": [],
  "maxItemsPerInput": 25,
  "includeDetails": false,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ],
    "apifyProxyCountry": "IN"
  }
}

Click Start, then open the completed run’s output dataset.

After completion, open the dataset and inspect productId, title, price, and url. Check whether the search contains wired products, bundles, or accessories that do not match your question. Tighten the term before increasing the cap.

If requests hit challenges, inspect the proxy configuration and run log. A larger item limit will not resolve a proxy-access problem.

Read the returned fields

Keep price separate from mrp. MRP is the displayed maximum retail price, not a verified previous transaction price. A percentage derived from those two fields should be labeled a discount from displayed MRP.

Use productId for model-level matching and retain listingId when present to distinguish listing information. Ratings and review counts are different measures; the number of ratings does not tell you how many written reviews were retrieved.

Make a useful product comparison

Export CSV and keep product ID, title, price, MRP, currency, URL, and any relevant keySpecs. Add your own columns for model, connection type, package contents, and review status.

Separate products by exact model and variant before sorting on price. For mice, a Bluetooth model and a USB receiver model may have similar titles but different features. Remove replacement parts and bundles that would distort the comparison.

If the card does not answer those questions, collect a small set of actual returned URLs through productUrls with details enabled. Preserve product IDs when joining the detailed results back to the initial list. Sample reviews can help identify questions to investigate, but they are not a complete review corpus.

Record collection time and source region with the comparison. A displayed price and availability are observations from that session, not a promise of the checkout price at every delivery address. Verify the shortlisted product page and delivery context before acting on a low price.

For price tracking, save repeated snapshots keyed by product ID and retain the original title and listing ID. Flag price changes for review, including whether the apparent change coincided with a different variant or offer.

Avoid false discounts and incomplete results

Do not replace missing prices with zero. If many rows lack prices or requests fail, inspect challenges and proxy access before treating the dataset as a market sample. Use a small retry to establish that extraction works.

If MRP is absent, leave discount blank. If a direct product URL is stale, reopen the source link and verify the product still exists. If the search is broad enough to hit pagination limits, narrow the product group rather than calling the capped result a complete catalog.

For another retail listing workflow, see Walmart price snapshots. Keep region, currency, model, and seller context separate when comparing platforms.

For collecting Flipkart product observations, see the Node.js example. Use the current input fields above if the example differs.

Frequently asked questions

Can I scrape one product URL?

Yes. productUrls accepts direct Flipkart product pages.

What does includeDetails change?

It visits product pages for descriptions, specifications, image galleries, sample reviews, and related detail fields.

Is the limit global?

No. maxItemsPerInput applies to each query or URL.