BigBasket Scraper - Extract Price and Stock Data

A BigBasket price is a service area price. Move the delivery address and the shelf, the discount and the promised minutes all change, so we collect the catalogue area by area.

BigBasket Scraper
Solutions

How we run BigBasket scraping as a managed feed, not a script

You give us the cities or delivery areas, the categories, brands or item ids, the fields you need and a cadence. We design the schema, build the crawler, run it, watch it and repair it when the storefront moves, and you receive files rather than a repository. Output is CSV, JSON, XLSX, a Google Sheet, a direct database load or a BigBasket API endpoint we host and you call, delivered over email, S3, SFTP or webhook.

Anti-bot handling, proxy rotation and CAPTCHA solving are part of the service and never become your problem. Coverage is planned as a grid of delivery areas, because one anonymous pass sees one service area and one entry context. We stay on public pages, never sign in, never touch basket, checkout or payment, honour the paths the site closes to crawlers, and collect no personal data - reviewer names and anything else that identifies a shopper stay out of the feed.

BigBasket product data: what a listing card and an item page actually carry

The unit of work is one item id resolved against one service area. Category listings return forty eight cards a page, brand listings forty, and each card is a full record, not a stub. What we lift from it:

  • Identity: numeric item id, description, brand with its slug and brand URL, the pack string as printed (1 kg, 500 g, 6 pcs), the pack type (Bag, Pouch, Carton, Bottle) and a normalised magnitude plus unit.
  • Money: selling price, regular selling price, MRP, the rupees-off line, the discount campaign block with its ids and percentage, a subscription price where the item allows one, and the per-unit price with its base unit, for example Rs 7.32 per 100 g. Loose produce carries decimals, so a cucumber pack reads Rs 19.5.
  • Availability: a status code with its button text - the in-stock state reads Add, the 010 state reads Notify Me under the label We are currently not delivering this - plus an express flag and, where the express shelf answers, a short eta reading 5 mins.
  • Signals: average rating, ratings count, review count, the sku id the rating belongs to, and a sold-in-the-last-month counter running from a few thousand units on a staple rice to over a million on onions.
  • Context: top, middle and leaf category names with numeric ids, the veg or non-veg food mark, a best-value flag, a variable-weight note saying loose items are billed on actual weight, images in five sizes on the bbassets CDN, and an EAN field - on a mixed set of listings a little under half carried a real thirteen digit barcode, the rest repeated the internal id.

The item page adds the regulated block Indian food retail requires. Tabs run About the Product, Ingredients, Nutritional Facts, Storage, Sourcing and Composition, and an Other Product Info tab prints the EAN code, the marketer line, the FSSAI licence number, country of origin and shelf life - a fresho onion says use within four days of delivery. On own-label staples that tab lists a separate packer per city, each with its own FSSAI licence, so one id carries Bangalore, Kolkata, Chennai and Mumbai manufacturers at once. The page carries the pack ladder as child items too: one rice line resolves to 1 kg, 5 kg, 10 kg, 25 kg and a 26 kg bag, each a separate id with its own price per 100 g.

BigBasket product data: what a listing card and an item page actually carry
Facets, review histograms and own labels: the fields most grocery feeds drop

Facets, review histograms and own labels: the fields most grocery feeds drop

Facets carry the platform's own vocabulary and are cut per category, not site-wide. A single brand listing exposes dozens of facet groups: Age of the Rice, Dal Type, Banana Varieties (Nendran, Poovan, Hill, Raw), Mango Varieties (Alphonso, Badami, Banganapalli, Chausa, Dasheri), Fish Cut Type, Meat Cut Type, Bone Type, Allergen Info, Serving Size and a Pack Size list of hundreds of printed values such as Approx 70 - 100 g or 900 g Vacuum Packed 10-14 pcs. Every facet value is also a shelf under /ts/, so the taxonomy can be crawled rather than guessed at.

Calendar and wellness rails sit beside the permanent tree: Ganesh Chaturthi, Krishna Janmashtami, Varamahalakshmi store and Ugadi pachadi on one side, Diabetic Friendly, Elderly Care, Gut and Digestive Health, Low Carb, Preservative Free and Women's Wellness on the other. They come and go with the season, so they need a date stamp, not a fixed tree.

Ratings run deeper than the star on the card. A review page keyed to the parent sku publishes the one to five histogram, ratings and review totals, per-category feature scores - a rice line is scored on Fluffiness, Appearance and Packaging - and a recommendation percentage. Each review record carries a city id, so sentiment cuts by area exactly like price.

Own labels are the other half of the story: fresho! covers the fresh shelf end to end, while bb Royal, bb Popular and bb Home cover premium staples, value staples and homeware. Inside one service area the fresho listing runs several times longer than Amul's, so a brand-share report built on national brands alone reads the market wrong.

How bigbasket.com is put together, from Tata Digital down to one service area

BigBasket is India's online supermarket, run by Innovative Retail Concepts Private Limited under the Supermarket Grocery Supplies Pvt Ltd copyright in its footer. It sits inside Tata Digital beside Tata 1mg, Croma and Tata CLiQ, and the home page top strip routes shoppers into the group's NeuPass programme. One catalogue feeds two delivery models: slotted delivery with a chosen window from early morning to late night, and bbnow, the express shelf sold as groceries in fifteen to thirty minutes in selected cities only. The site puts its footprint at more than three hundred cities and towns; the rotating footer list runs from Bangalore and Mumbai down to Marthandam, Saundatti and Rajgir.

Addresses follow a short grammar a BigBasket scraper can plan around: /cl/slug/ for a top category, /pc/l1/l2/ for a subcategory, /pc/l1/l2/l3/ for a leaf, /pb/brand/ and /pb/brand/leaf/ for brands, /pd/id/name-slug/ for an item, /ts/number/facet/value/ for an attribute shelf and /product-reviews/parent-sku-id/ for ratings. The sitemap index splits that tree into eleven consumer top categories plus the bbdaily nodes, ninety five subcategories, close to five hundred leaves, roughly eight and a half thousand brand pages, over twenty three thousand brand-in-leaf pages and nine product files listing a little over two hundred thousand items.

The robots file is where a crawl plan starts. It closes internal search under /ps/, the older item URL forms, /similar-items/, /choose-city/ and the recipe section, and it permits paging only as far as page nine on category, leaf and brand listings. Since a top category such as Beauty and Hygiene runs to hundreds of pages, coverage is cut by leaf, brand and facet rather than walked page after page.

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25

Developers

customers
500+

Customers worldwide

pages
1 500 000 000+

Pages extracted

stime
15000+

Hours saved for our clients

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€199 / one-time

setup fee - included

Data limits100,000
Frequencyone-time
Run timeup to 5 days
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Data limits250,000
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Data storing14 days

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setup fee €499

Data limits1,000,000
Frequencyweekly
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Data storing30 days

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setup fee €799

Data limits3,000,000
Frequency3 times daily
Run timesame day
Data storing90 days

Why a BigBasket price only means something with the service area attached

The delivery point never appears in the address bar. It lives in the session: the storefront resolves a shopper into an entry context and one or more service area ids, and the same item page then answers with different money, different stock and sometimes nothing at all. One city can map to several service areas, and some resolve only to the slotted storefront while others also open the express one. That decides how BigBasket scraping has to be built.

Held against eight delivery areas, the Fresh Vegetables leaf returned between fifty two and sixty listings each, a union of a hundred and fifty five item ids, and only four that appeared everywhere. All four were priced differently: a 500 g cucumber ran from Rs 10 to Rs 30, a bitter gourd pack from Rs 29 to Rs 55 and a fresho onion pack from Rs 59 to Rs 78 on the same afternoon. Assortment moves first, price second, and the two do not move together.

Depth moves as well. The Dairy leaf held six hundred and ten listings in one area, four hundred and ninety seven in another and two hundred and thirty five in a third where every card carried an express flag and a five minute eta - the express shelf is a shorter shelf, not the same shelf delivered faster. A sitemap URL is not a product everywhere: close to half the item addresses sit in the id range used by hyperlocal seller listings, which return an empty page outside the area that stocks them. So share of shelf, out-of-stock rate, promo depth and pack-price ladders only compute when every row remembers its area, its entry context and its capture time.

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Who builds and runs the feed

ScrapeIt is a managed web scraping agency. We are not a library and not a self-serve tool: we build the crawler, run it on your schedule, monitor it and fix it when the site changes, so you keep a data feed instead of a codebase. A sample lands before sign-off, so you can check pack strings, MRP handling, per-unit price and the service area on every row against live pages yourself. Each row carries the delivery area, the entry context and the capture time, which is what lets separate deliveries stack into one comparable series. We have no affiliation with BigBasket.

FAQ

Does BigBasket have a public API for product and price data?

No. There is no documented public BigBasket API and no developer portal on bigbasket.com: the api subdomain answers with nothing usable, and the business subdomain is another storefront rather than a data product. The site builds its own pages from internal endpoints that expect a session carrying a service area and an entry context, and those are undocumented and free to change without notice. What we supply instead is a BigBasket API endpoint we host: a stable, versioned schema over the public storefront, so a change on their side becomes our repair job rather than yours.

The BigBasket catalogue changes by city - how do you handle that?

We treat the delivery area as part of the key. You name the cities or areas, we resolve each one to the service area that answers for it, and we crawl the catalogue area by area. Every row then carries its service area, its entry context - the slotted shelf or the bbnow express shelf - and its capture time, so a price is the price at that address rather than a national average. On one vegetable leaf held against eight delivery areas, only four item ids were common to all eight, and all four were priced differently, so city coverage is planned as a grid of areas and never as a single call.

Which BigBasket fields come through, and what is simply not there?

From the listing: item id, description, brand, pack string, pack type, normalised weight, selling price, regular price, MRP, rupees off, per-unit price, discount campaign, availability code, express flag and eta, rating, ratings count, review count, sold-last-month counter, the three level category path with ids, and the EAN where one is published. The item page adds ingredients, nutrition, storage, sourcing, FSSAI licence, marketer, country of origin, shelf life and the full pack ladder. What does not exist publicly: order volumes, margin, per-store inventory counts and anything behind a login. We do not sign in, so nothing inside an account is in scope.

How often can BigBasket prices and stock levels be refreshed?

As often as the job needs. Discount campaigns move through the day, the express shelf changes with the store behind it, and availability flips between Add and Notify Me without warning, so an hourly run over a watchlist of item ids paired with a daily sweep of leaves is the usual shape. Full coverage of a city is slower than a watchlist, because it has to be sliced by leaf, by brand and by facet: listings can be paged only as far as page nine, and one top category runs to hundreds of pages. We agree the cadence per job and hold it, and every delivery is stamped so you can diff one run against the last.

Is it legal to scrape BigBasket, and what do you refuse to collect?

We take only pages bigbasket.com publishes to the open web without a login, and we honour the paths its robots file closes, internal search included. We do not create accounts, do not place orders and do not touch basket, checkout or payment. Personal data is out by default: reviewer names are printed beside reviews and we drop them, and we collect no shopper identities or private addresses. The terms of use restrict deep-linking, page-scraping, robots, spiders and data mining, so the commercial call is yours to make with your own counsel. What we hold to is the technical discipline - public pages only, polite rates, and a clear record of what was taken and when.

How does it Work?

Step 1 - Make a Request

You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.

Step 2 - Configuring Custom Web Crawlers

Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.

Step 3 - Collect and Deliver

Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.

Step 4 - Maintain and Support

Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.

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