By industry
Pricing & Promotions
Brand & Intelligence
Digital Shelf & Search
Assortment
For Retailers
E-Commerce Dashboard
Flipkart Insights (Live) 
Grocery Intelligence.svg)
Grocery Price (U.S.).svg)
Quick Commerce (India) 
Food & Restaurant
Fashion Intelligence
Automotive
Travel & Hospitality
Real Estate
Food Delivery Intelligence 
OTT & Streaming 
Fuel & EV Charging Price Tracker 
By Use Case
Pricing Intelligence
Digital Shelf Analytics
MAP Monitoring 
Cross-Border Price Parity 
Share of Search
Review Sentiment
Kitchen Market Gaps 
Dynamic Pricing / AI Repricing 
Promotions & Deals Alerts 
B2B / POI & Lead Data 
AI Search Visibility Dashboard 
Baby Products on Quick Commerce 
Hotel & Flight Rate Parity 
By Region
Flipkart Real-Time Insights
Quick Commerce — Zepto · Blinkit
Pincode Price Tracker
GCC Q-Commerce — Talabat · Noon 
UK Grocery — Tesco, Sainsbury's, Asda
AU Grocery — Coles & Woolworths 
Marketplace Scrapers
Developer APIs
Universal APIs
Delivery & SDKs
Knowledge Center
Guides & Playbooks
Downloads & Tools
Trust & CompanyBlinkit India Thrice a Week Q-Commerce Store, Pricing & Inventory Dataset
The same dark-store extract as our daily Blinkit file — 21 columns with MRP, selling price, discount, pack quantity, stock status and inventory as a real unit count — delivered Monday, Wednesday and Friday.
Three files a week is the sensible middle. It is frequent enough to catch a store running down before it empties, and cheap enough to run across a few hundred stores rather than a handful.
The file already exists, because this pipeline runs three mornings a week 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. Same columns as the twice-a-day file, on a schedule most teams can actually act on.
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.
Three mornings a week
Monday, Wednesday and Friday before 9am IST. Frequent enough to see a store draw down across the week without paying for readings nobody opens.
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 three mornings a week across the dark stores and pin-codes on your feed. The supplied sample spans 17 stores in 16 pin-codes across 4 cities.
| City | Pin-codes in sample | Dark stores | Share of rows out of stock |
|---|---|---|---|
| Bengaluru | 560032, 560048, 560073, 560092 and 6 more | 11 | 9% |
| Pune | 411014, 411045 | 3 | 33% |
| Mumbai | 400053 | 2 | 0% |
| Hyderabad | 500032 | 1 | 0% |
Seventeen stores is what the sample shows, not what the feed is limited to. Store count is what drives the price — most clients run several hundred. Tell us the cities and we will quote 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“Daily was more than our team could act on and weekly was too slow to catch a store emptying. Three times a week turned out to be exactly the rhythm our replenishment cycle already ran on.”
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. Each store holds its own assortment and its own stock, so a national availability figure for Blinkit describes nothing that any actual customer experienced.
Why three times a week is usually the right cadence
Quick-commerce stock turns fast, but most replenishment decisions do not. A category team reviewing twice a week gets no extra value from five files it never opens, and the lighter schedule lets you spend the same money on many more stores — which is almost always the better trade. If you are running automated alerts, take the twice-a-day feed instead.
Why a unit count beats an in-stock flag
A boolean tells you the state now. A count tells you the trajectory. In the supplied sample counts ranged from 0 to 6 units per store, with 7 of 50 rows at zero. Watching a store move from 4 to 2 to 0 across a week is actionable; watching true become false is not.
What the empty columns mean
Category Name, SKU Name and Variation_ID came through empty across the whole supplied sample. Category can be populated on a custom feed, scoped free.
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. Check it against your requirement before buying.
