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Trust & CompanySwiggy Instamart India Twice a Day Store, Pricing & Inventory Dataset
Two captures a day of Swiggy Instamart at dark-store level — 21 columns covering MRP, selling price, discount, pack quantity, stock status and inventory as a real unit count rather than a yes-or-no flag.
Quick-commerce stores hold very little. In the supplied sample the counts ran from 0 to 4 units and 17 of 50 rows were already out of stock — at that depth a store can sell through between a morning and an evening crawl, and a once-daily file never sees it.
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 a daily one if stock-outs matter more to you than price.
Two readings every day
Morning and evening. 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, 3, 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 and pin-code that served it. Instamart 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 21 columns.
Coverage
Captured twice a day across the dark stores and pin-codes on your feed. The supplied sample spans 10 stores across 10 Mumbai pin-codes.
| Pin-code | Store ID | Rows in sample | Share out of stock |
|---|---|---|---|
| 400012 | 1404993 | 5 | 20% |
| 400007 | 1404909 | 5 | 40% |
| 400003 | 1135722 | 5 | 60% |
| 400018 | 1135845 | 5 | 20% |
| 400022 | 1404871 | 5 | 40% |
| 400026 | 1135690 | 5 | 20% |
| Four further pin-codes | — | 20 | 35% |
Ten 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 Instamart store-level records from February 2025 onwards.
Ask about historical dataNeed more data points?
We can extend this dataset beyond the standard 21 columns — a populated SKU name and category tree are the most requested additions, along with competitor SKU matching and delivery fee capture.
Request custom fields“Our stock looked fine every morning and our sales said otherwise. Two captures a day showed six of ten stores hitting zero by evening and restocking overnight.”
What people use this dataset for
FMCG and grocery brands
See which dark stores hold 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.
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 one of India's three largest quick-commerce platforms.
About Swiggy Instamart dark-store data
Swiggy Instamart is one of the three platforms that decide grocery pricing in urban India, alongside Blinkit and Zepto. All three fulfil from local dark stores, which means price and availability are store facts rather than national ones — and a single national figure for Instamart describes nothing any customer experienced.
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 ran from 0 to 4 units per store, and 17 of 50 rows were at zero. Watching a store move from 4 to 2 to 0 across captures is a replenishment story you can act on; watching true become false is not.
What a second capture actually buys you
At quick-commerce stock depths a store can go from available to sold out in a few hours. A morning-only file reports it as available for the whole day, including the evening peak when most of the volume actually happens. The second capture is what makes evening stock-outs visible at all.
Price is a store decision
In the supplied sample the same ice cream ran ₹332 in some stores and ₹320 in others, on the same MRP of ₹400, on the same morning. Any dataset that reports one Instamart price per product has averaged genuinely different numbers.
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
Product Name is populated on every row, so nothing is lost, but if you need a separate normalised SKU name say so and we will populate it.
Your Data Needs
-
Free Sample in 2 HoursShare your requirement, get 500 rows of real data — no commitment. -
Plans from $500/monthFlexible pricing for startups, growing brands, and enterprises. -
US-Based SupportOffices in New York & California. Aligned with your timezone. -
ISO 9001 & 27001 CertifiedEnterprise-grade security and quality standards.