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Trust & CompanyBlinkit India Twice a Day Q-Commerce Store, Pricing & Inventory Dataset
The highest-frequency Blinkit file we run — two captures a day at dark-store level, with 21 columns covering MRP, selling price, discount, pack quantity, stock status and inventory as a real unit count.
Quick commerce stock does not last a day. A store holding four units at nine in the morning can be at zero by the evening peak, restocked overnight, and a once-daily file records whichever of those three states it happened to catch.
The file already exists, because this pipeline runs twice a day 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. Take this file rather than the daily one if stock-outs matter more to you than price.
Two readings every day
Morning and evening, each with its own timestamp. Enough to see a store sell through and to tell a genuine stock-out from a gap between crawls.
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.
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 21 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 twice a day across the dark stores and pin-codes on your feed. The supplied sample spans 15 stores in 15 pin-codes across 3 cities.
| City | Pin-codes in sample | Dark stores | Share of rows out of stock |
|---|---|---|---|
| Bangalore | 560037, 560048, 560057, 560061, 560070 | 9 | 44% |
| Pune | 411001, 411014, 411045 | 4 | 67% |
| Mumbai | 400053, 400076 | 2 | 50% |
Fifteen stores is what the sample shows, not what the feed is limited to. Store count multiplied by capture frequency is what drives the price — tell us the cities and we will quote it.
Historical data
The last 30 days come with the dataset, at full twice-daily granularity. Beyond that we hold Blinkit store-level records from February 2025 onwards.
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 competitor was going out of stock every evening and restocking overnight, so on a daily morning crawl they looked permanently available. Two captures a day made it obvious in a week.”
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 intraday Blinkit stock data
Blinkit fulfils from local dark stores that hold small quantities and turn them over fast. A store carrying three or four units of an item is normal, which means the interesting events — selling out, restocking — happen well inside a single day.
What a second capture actually buys you
In the supplied sample, inventory counts ranged from 0 to 4 units per store. At that depth a store can move from available to sold out in a few hours, and a once-daily morning file will report it as available for the entire day. Two captures give you the evening state as well, which is when most quick-commerce volume happens.
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 to 2 to 0 across captures is a replenishment story you can act on, and it disappears entirely the moment someone collapses it into true or false.
What the empty columns mean
Category Name, SKU 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 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, SKU Name and Variation_ID came through empty across the whole sample. Category can be populated on a custom feed, scoped free.
