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Trust & CompanyBlinkit India Daily Q-Commerce Store, Pricing & Inventory Dataset
A daily file of Blinkit at dark-store level — 22 columns covering MRP, selling price, discount, pack quantity, stock status and, unusually, inventory as a real unit count rather than a yes-or-no flag.
Blinkit fulfils from local stores, so nothing about price or availability is national. In the supplied sample one ice cream tub appeared across eleven stores, nine of them holding zero units, and the same product was priced ₹492 in Bengaluru against ₹521 in Ghaziabad on the same morning.
The file already exists, because this pipeline runs every morning whether you buy it or not. Most vendors start collecting after you order — which is why they quote a lead time. Here the most recent file is in your account within minutes of payment, with an API key issued at the same time.
What you get, in plain terms
Four things. The inventory column is the reason most people buy this rather than a standard pricing feed.
Inventory as a unit count
Not an in-stock flag. inventory carries the actual number of units the store holds — 0, 1, 2, 4 — which is the difference between knowing a product is low and finding out it has gone.
Dark store, not city
Every row names the Store ID and pin-code that served it. Blinkit fulfils locally, so price and stock are store facts rather than national ones.
MRP, price and discount separately
All three as their own columns, so you can see whether a store is discounting or simply carrying a different MRP. In the sample one store listed the same tub at ₹550 MRP while the rest showed ₹578.
However you want it
Direct download, REST API, Amazon S3, Google Cloud, Snowflake or SFTP. The API key comes with the dataset.
Fields included in this dataset
All 22 columns, in the exact order they appear in the file — taken straight from the sample, not from a brochure. The free sample ships with a data dictionary giving an example value for each one.
Sample rows from the real file
Real rows from the sample file, not an illustration. The free sample is 50 product-by-store rows with all 22 columns.
Coverage
Captured every morning across the dark stores and pin-codes on your feed. The supplied sample spans 12 stores in 8 pin-codes across 5 cities.
| City | Pin-codes in sample | Dark stores | Share of rows out of stock |
|---|---|---|---|
| Bangalore | 560004, 560012 | 4 | 78% |
| Ghaziabad | 201102 | 3 | 33% |
| Delhi | 110091 | 2 | 100% |
| Hyderabad | 500032 | 2 | 100% |
| Noida | 201301 | 1 | 50% |
Twelve stores is what the sample shows, not what the feed is limited to. Store and pin-code count is what drives the price — most clients run several hundred stores. Tell us the cities and we will price it.
Historical data
The last 30 days come with the dataset. Beyond that we hold Blinkit store-level records from February 2025 onwards — enough to cover a full festive quarter. Ask and we will confirm exactly what exists for your cities.
Ask about historical dataNeed more data points?
We can extend this dataset beyond the standard 22 columns — a populated category tree is the most requested addition, along with competitor SKU matching, delivery fee capture and moving to intraday collection.
Request custom fields“Our distributor said the city was covered. Store-level inventory said nine of eleven stores in that city were holding zero units of our best seller, and had been for days.”
What people use this dataset for
FMCG and grocery brands
See exactly which dark stores are holding your stock, how many units, and where you have quietly gone to zero.
Pricing teams
Compare price store by store rather than assuming a national figure. The gaps are routinely five to ten percent.
Supply and replenishment
Use real unit counts to spot draw-down before a stock-out rather than after it.
Analysts
Build a store-level availability and price series for the largest quick-commerce platform in India.
About Blinkit dark-store data
Blinkit is the largest quick-commerce platform in India and fulfils every order from a local dark store. That single fact is why a national price or availability figure for Blinkit is close to meaningless — each store holds its own assortment, its own stock and, frequently, its own price.
Why a unit count beats an in-stock flag
A boolean tells you the state now. A count tells you the trajectory. Watching a store go from 4 units to 2 to 0 across three days is a replenishment story you can act on, and it disappears the moment someone collapses it into true or false. In the supplied sample the counts ran from 0 to 6, and every out-of-stock row carried a clean zero rather than a blank.
Price is a store decision, not a national one
In the supplied sample the same ice cream tub sold at ₹492 in two Bengaluru stores and ₹521 in Ghaziabad, Delhi and Hyderabad — with one Ghaziabad store carrying a different MRP entirely, ₹550 against ₹578 elsewhere. Any dataset that reports one Blinkit price for a product has averaged three genuinely different numbers.
What the empty columns mean
Category Name and Variation_ID came through empty across the whole supplied sample. Category is the one people miss, and we can populate it on a custom feed — but we would rather you saw that here than discovered it after purchase.
Is collecting this data legal?
Collecting publicly visible product and price information is generally lawful in most jurisdictions. Actowiz collects only public pages, respects robots.txt and platform terms, holds no personal data, and aligns with GDPR and CCPA. We are ISO 9001 and ISO 27001 certified, and this dataset carries documented provenance so your legal team can review the source before you buy.
Frequently asked questions
Category Name and Variation_ID came through empty across the whole sample. Category can be populated on a custom feed, scoped free. Check the sample against your requirement before buying.
date column in favour of the capture timestamp. If you need one schema across cadences, say so and we will align them.
