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# Swiggy Instamart Analysis: Prices, Stock & Assortment (2026)
- URL: https://www.blog.datahut.co/post/swiggy-instamart-analysis-prices-stock-assortment-2026/
- Published: 2026-10-02T08:32:14.000Z
- Updated: 2026-10-02T08:32:14.000Z
- Author: Aarathi J
- Tags: Ecommerce Data, Ecommerce Operations, Product Data

India's quick-[commerce market](https://www.blog.datahut.co/post/e-commerce-category-management-challenges-opportunities-and-best-practices/) is growing fast, and fresh produce is one of the categories where prices, stock and assortment can change quickly.

For consumers, a missing mango or a small price difference may not seem significant. [For brands, suppliers and retailers](https://www.blog.datahut.co/post/ecommerce-web-scraping-for-non-technical-store-owners/), thousands of these changes can show how a platform manages inventory, pricing and assortment.

We analyzed Swiggy Instamart's fruits and vegetables catalogue across **Delhi, Mumbai, Bangalore, Chennai, Kolkata and Kochi**.

![](https://www.blog.datahut.co/content/images/2026/10/hero.webp)

The dataset contains **881 listings covering 367 unique products**. We looked at stock availability, prices, discounts, assortment and product attributes.

The results show a highly localized catalogue. Availability varies by city and category, the same products can have very different prices between cities, and only a small share of products appear across all six markets.

For context, the India Brand Equity Foundation estimates India's quick-commerce segment at **US$7–8 billion in FY25**.

## What we found

- **41% of listings were out of stock** when the data was collected: 358 of 881.
- **Delhi had the highest stock-out rate at 52%.** The rate remained 50% after mango and raw mango were excluded.
- **Fruits were out of stock 51% of the time**, compared with 21% for vegetables.
- **Leafy greens and herbs reached 72%**, while combo kits reached 87%.
- **Mango and raw mango accounted for about 26% of all stock-outs**, despite representing about 13% of the catalogue.
- **Every mango listing in Mumbai and Kochi was out of stock** in the collected data.
- **About 75% of listings carried a displayed discount of exactly 19% or 20%.**
- **Bangalore was about 7.5% below the cross-city average**, while Kochi was about 5.6% above it on a like-for-like basis.
- **Only 21 of 367 products appeared in all six cities**, while 156 appeared in only one city.
- **160 products had different names across cities**, creating a product-matching problem for cross-city analysis.

These numbers point to clear areas for competitor monitoring: stock, pricing, promotions and assortment.

## Study at a glance

![](https://www.blog.datahut.co/content/images/2026/10/instamart_study_at_a_glance-1.webp)

The analysis covers the catalogue visible in the six selected cities at the time of collection. Stock, pricing and assortment can change throughout the day, so these results are a point-in-time view rather than a permanent measure of Instamart.

Swiggy's public Instamart catalogue also organizes fresh produce into categories such as vegetables, fruits, leafy produce, seasonal fruits and other fresh-food groups. [Swiggy Instamart](https://www.swiggy.com/instamart?utm%5Fsource=chatgpt.com)

## How we analyzed Instamart's fresh-produce catalogue

We looked at several parts of the catalogue together:

- Stock availability by city
- Stock availability by category
- Product and pack sizes
- Product prices
- Price per kilogram for weight-based products
- Discount percentages
- Catalogue overlap between cities
- Product naming differences
- Seasonal product attributes

Of the 881 listings, **432 were sold by weight and could be normalized to a price-per-kilogram measure**.

For cross-city pricing, we compared the same products rather than simply comparing city-wide averages. This matters because the assortment isn't the same in every city.

For a broader framework on comparing competitor prices, products, promotions and availability, see Datahut's [competitive analysis for e-commerce](https://www.blog.datahut.co/post/competitive-analysis-for-e-commerce-a-data-driven-framework-with-examples/?utm%5Fsource=chatgpt.com)

## How many fruits and vegetables are out of stock on Instamart?

![](https://www.blog.datahut.co/content/images/2026/10/fig01_oos_by_subcategory.webp)

**41% of all listings were out of stock** when the catalogue was collected: 358 out of 881.

The overall figure changes quite a bit by category.

![](https://www.blog.datahut.co/content/images/2026/10/instamart_out_of_stock_category.webp)

Fruits were unavailable more than twice as often as vegetables.

The highest individual sub-category rates included:

- Combo kits: **87%**
- Muskmelon: **82%**
- Mango: **80%**
- Raw mango: **74%**
- Cherries: **70%**

Avocado, beans and chilli were among the more available categories.

The difference is useful for brands and retailers because the overall 41% number doesn't tell you where the gaps are.

A better question is:

> Which products repeatedly go out of stock, in which cities, and for how long?

That requires repeated collection.

## Which city has the highest Instamart stock-out rate?

![](https://www.blog.datahut.co/content/images/2026/10/fig02_oos_by_city.webp)

Delhi has the emptiest shelf. Of its 215 listings, 111 (52%) were out of stock, ahead of Kochi (46%), Mumbai (41%), Chennai (34%), Bangalore (33%) and Kolkata (32%).

Mangoes push some cities up, so we also ran the numbers without mango and raw mango. Kochi drops to 35% and Mumbai to 32%, but Delhi barely moves at 50%. Delhi is served by two seller entities in the data, and both sit above the overall rate (54% and 43%), so the problem does not come from one store.

Delhi also has the widest catalogue, with 215 listings across 83 sub-categories against 110 listings and 58 sub-categories in Kochi. Instamart lists more in Delhi and has more of it unavailable. For context, our earlier Zepto analysis found 71% of Delhi listings out of stock, although the two datasets were collected separately and are not a ranking.

**What this means for brands and retailers:** A city with persistently poor availability is unmet demand. A brand that can keep supply steady into Delhi has room to win share, and procurement and pricing plans probably should not be identical across all six cities.

Delhi had the highest stock-out rate in the dataset.

![](https://www.blog.datahut.co/content/images/2026/10/instamart_out_of_stock_by_city.webp)

Delhi had **111 out-of-stock listings out of 215**.

We also checked what happens when mango and raw mango are removed.

![](https://www.blog.datahut.co/content/images/2026/10/instamart_oos_overall_vs_excluding_mango.webp)

Delhi barely changes.

That matters because mango is a major source of stock-outs in some cities. In Delhi, it doesn't explain the overall availability gap.

Delhi also had the largest catalogue in the dataset, with **215 listings across 83 sub-categories**. Kochi had 110 listings across 58 sub-categories.

An earlier \[Datahut analysis of Zepto's fruits and vegetables catalogue\] found a 71% stock-out rate in Delhi in its separate dataset. The two datasets were collected separately, so they shouldn't be treated as a direct comparison between platforms. [Zepto fruits and vegetables analysis](https://www.blog.datahut.co/post/zepto-fruits-vegetables-data/?utm%5Fsource=chatgpt.com)

For a brand or supplier, a city with repeated availability problems can be worth investigating. The next step is to determine whether the problem lasts for hours, days or longer.

## What factors are associated with Instamart stock-outs?

The dataset shows differences in availability by price and pack size.

### Price

Listings priced up to ₹30 had a **32% stock-out rate**.

Listings priced between ₹101 and ₹200 had a **51% stock-out rate**.

### Pack size

Multi-packs, such as a 2 × 250 g pack, were out of stock **28% of the time**, compared with **44% for single packs**.

### Category

Fruits and several specialized fresh-produce categories had higher stock-out rates than vegetables.

These figures show patterns in the collected data. They don't establish that price or pack size causes a product to go out of stock.

Repeated observations would be needed to investigate the reasons.

## Mango: a case study in seasonal availability

![](https://www.blog.datahut.co/content/images/2026/10/fig03_mango_oos_by_city.webp)

**Scale:** mango is the largest sub-category in the data, 99 listings across 40 products, or 118 listings (13% of the catalogue) once raw mango is included.

**Availability:** 80% of ripe mango listings were out of stock overall, and mango and raw mango together make up 26% of all out-of-stock listings on the platform. In Mumbai and Kochi, every single mango listing was unavailable; Kolkata was at 83%, Bangalore and Delhi at 72%, and Chennai at 53%.

**Pricing:** where mangoes were available, the median price per piece ranged from ₹40 in Bangalore to ₹53 in Delhi. That is a median across varieties, so it mixes cheap and premium types.

**Seasonality:** all 64 "Carbide Free" tags in the data sit on mango or raw mango. Grapes carried the other seasonal marker, "Season ending soon", on 11 listings, 64% of which were out of stock.

**Business implication:** mango behaves like a case study in how fast a seasonal category can swing from fully stocked to effectively unavailable. We only have one snapshot, so we cannot say whether this is the tail end of the season or a gap that refilled a day later. Repeated collection is what answers that.

Mango is one of the clearest examples in the dataset.

There were **99 mango listings across 40 products**. Including raw mango, mango-related listings rose to **118**, or about 13% of the catalogue.

Mango and raw mango accounted for **26% of all stock-outs**.

## Mango availability by city

![](https://www.blog.datahut.co/content/images/2026/10/instamart_mango_oos_by_city_compressed.webp)

Every mango listing in Mumbai and Kochi was out of stock in the collected data.

Where mangoes were available, the median price per piece ranged from **₹40 in Bangalore to ₹53 in Delhi**.

The dataset also contains seasonal product attributes.

All 64 listings tagged **"Carbide Free"** were mango or raw mango products. The 11 listings tagged **"Season ending soon"** were all grapes, and 64% of those were out of stock.

We have one snapshot, so we can't tell whether those products stayed unavailable or were replenished later.

For seasonal categories, that distinction matters. Tracking the same products over time can show which varieties sell out first, where shortages last longest and when inventory returns.

## How does Instamart discount fresh produce?

![](https://www.blog.datahut.co/content/images/2026/10/fig04_discount_distribution.webp)

The discount distribution is unusually concentrated.

About **75% of listings, or 658 of 881, had a displayed discount of exactly 19% or 20%**.

Another **92% fell between 18% and 24%**.

The median MRP was about **1.25 times the selling price**, which corresponds closely to a 20% displayed discount.

A small number of products were exceptions, with discounts between 42% and 80%.

The concentration around 19–20% is worth tracking because a competitor's displayed discount can be as useful as its selling price when you're trying to understand its pricing strategy.

Useful fields include:

- MRP
- Selling price
- Discount percentage
- Price changes
- Discount changes
- Promotion frequency
- Category-level promotional depth

Datahut's covers how scraping can be used to track prices, products, promotions and availability over time. [competitor price monitoring guide](https://www.blog.datahut.co/post/how-to-leverage-web-scraping-to-create-a-competitor-price-monitoring-strategy/?utm%5Fsource=chatgpt.com)

## What does fresh produce cost on Instamart?

Pack sizes in the dataset ranged from 10 g of rosemary to multi-piece fruit packs.

We converted weight-based listings to price per kilogram where the data allowed it. That gave us 432 normalized listings.

![](https://www.blog.datahut.co/content/images/2026/10/Bar-chart-of-median-price-per-kilo.-Cherry----648--litchi----569--grapes----520--plum----480--garlic----350--ginger----253--down-to-potato----54-and-onion----34.--1-.png)

Pack sizes run from 10g of rosemary to a six-piece box of mangoes, so we converted everything sold by weight (432 of 881 listings) to a price per kilo. Onion is the cheapest staple at a median of ₹34, followed by potato (₹54) and tomato (₹76).

At the other end sit cherries (₹648), litchi (₹569), grapes (₹520) and plums (₹480) as the most expensive items in the catalogue. Garlic, at ₹350, costs more than most fruit on the list, worth knowing if you are benchmarking against produce prices in general rather than against fruit specifically.

Some of the lowest median prices were:

![](https://www.blog.datahut.co/content/images/2026/10/instamart_median_price_per_kg_compressed.webp)

At the higher end:

![](https://www.blog.datahut.co/content/images/2026/10/instamart_highest_median_prices_compressed.webp)

An overall city average would hide much of this variation because each city carries a different mix of products.

For pricing work, the useful comparison is the same product in different markets.

## How much do Instamart prices vary by city?

We compared **123 products listed in at least three cities**, measuring each product against its own cross-city average.

![](https://www.blog.datahut.co/content/images/2026/10/fig06_city_price_index.webp)

City medians on raw numbers show no clear winner, mainly because each city stocks different products. So we compared the same products instead: 123 products listed in at least three cities, each measured against its own average price across those cities. Bangalore came out about 7.5% cheaper and Kochi about 5.6% dearer, the two clearest outliers. Kolkata leans expensive too (+4.2%), while Delhi, Mumbai and Chennai sit within about 1% to 2% of the average, close enough to call them typically priced.

**What this means for brands and retailers:** A single national reference price for Instamart will mislead. Track each city separately, and track the same product in each. Our guide to [competitor price monitoring](https://www.blog.datahut.co/post/how-to-leverage-web-scraping-to-create-a-competitor-price-monitoring-strategy/) covers how to set that up.

![](https://www.blog.datahut.co/content/images/2026/10/instamart_price_difference_cross_city_compressed.webp)

Bangalore was about **7.5% below the comparison average**, while Kochi was about **5.6% above it**.

Delhi, Mumbai and Chennai were close to the comparison average.

The important point is that city-level price differences become clearer when you compare equivalent products rather than entire catalogues.

## How much can the same product cost in different cities?

Individual products can vary much more than the city averages suggest.

For the **17 priced products available across all six cities**, the median gap between the cheapest and most expensive city was **55%**.

For example:

- Tomatoes ranged from **₹38/kg in Bangalore to ₹78/kg in Kolkata**.
- Ginger ranged from **₹170/kg in Mumbai to ₹300/kg in Delhi**.

The sample is small, so these examples are best treated as patterns to investigate rather than a general pricing rule.

For brands monitoring competitor prices, the useful unit of comparison is:

**same product + same pack size + same city + timestamp**

That gives you a much cleaner basis for price tracking.

## How different is Instamart's assortment across cities?

![](https://www.blog.datahut.co/content/images/2026/10/fig07_catalogue_overlap.webp)

City medians on raw numbers show no clear winner, mainly because each city stocks different products. So we compared the same products instead: 123 products listed in at least three cities, each measured against its own average price across those cities. Bangalore came out about 7.5% cheaper and Kochi about 5.6% dearer, the two clearest outliers. Kolkata leans expensive too (+4.2%), while Delhi, Mumbai and Chennai sit within about 1% to 2% of the average, close enough to call them typically priced.

**What this means for brands and retailers:** A single national reference price for Instamart will mislead. Track each city separately, and track the same product in each. Our guide to [competitor price monitoring](https://www.blog.datahut.co/post/how-to-leverage-web-scraping-to-create-a-competitor-price-monitoring-strategy/) covers how to set that up.

The catalogue is highly localized.

Of the **367 unique products**:

![](https://www.blog.datahut.co/content/images/2026/10/instamart_product_city_coverage_compressed.webp)

Only **21 products, or 6%, appeared in all six cities**. Meanwhile, 156 products appeared in only one city.

Between any two cities, product overlap ranged from **26% to 44%**.

Delhi had the highest number of exclusive products, with 55 products not found in the other cities.

That means a catalogue from one city doesn't tell you much about the full assortment in another.

## Why product matching matters in quick-commerce data

There is another issue when comparing catalogues across cities: names change.

The dataset identified **160 products with different names across cities**.

Ginger appeared as:

- Adrak
- Shunti
- Inji
- Adda

A tracker that matches products only by name could treat these as four different products.

Cross-city price and assortment analysis therefore needs product matching based on IDs, pack sizes, attributes and other product-level signals.

This is a data-cleaning problem as much as a scraping problem.

## What does Instamart's data tell us about competitive intelligence?

The analysis gives us three useful groups of signals.

## Availability

Track:

- Stock status
- Stock-out duration
- Replenishment
- City-level availability
- Category-level availability

## Pricing

Track:

- Selling price
- MRP
- Discount
- Price changes
- City-level differences
- Seasonal price movements

## Assortment

Track:

- New products
- Removed products
- City-specific products
- Category expansion
- Product overlap
- Pack-size changes

The point isn't to collect every field available on a product page.

Start with the business question, then collect the fields needed to answer it.

Datahut's [competitive analysis framework](https://www.blog.datahut.co/post/competitive-analysis-for-e-commerce-a-data-driven-framework-with-examples/) takes the same approach: collect comparable product, price, promotion and availability data rather than building a large dataset with no clear use for it. [competitive analysis framework](https://www.blog.datahut.co/post/competitive-analysis-for-e-commerce-a-data-driven-framework-with-examples/?utm%5Fsource=chatgpt.com).

## What should brands monitor on Instamart?

A practical monitoring setup can include:

![](https://www.blog.datahut.co/content/images/2026/10/instamart_key_signals_compressed.webp)

The questions become more useful when they're specific.

For example:

- Which products are repeatedly unavailable in Delhi?
- Where is a competitor cheaper for the same product?
- Which products are available in Bangalore but missing from Kochi?
- Which categories receive the deepest discounts?
- Which seasonal products sell out first?

Those questions are difficult to answer reliably through manual browsing.

## From one-time snapshot to continuous monitoring

A snapshot tells you what the catalogue looked like when you collected it.

It doesn't tell you how long a stock-out lasted.

Take Delhi's 52% stock-out rate. A second collection could show whether availability recovered the next day, whether the same products went out again, or whether the original result was a temporary event.

The same applies to price.

A 20% displayed discount tells you what the promotion looked like at one point. Historical data tells you whether that discount stays in place, changes during promotions or varies by category.

The progression is simple:

**Snapshot → history → change detection → competitive intelligence**

Datahut's [Blinkit fresh-basket analysis](https://www.blog.datahut.co/post/inside-blinkit-s-fresh-basket-a-3-day-data-driven-analysis-of-prices-stock-and-strategy/) shows how collecting data over several days can reveal price, stock and promotion changes that a single collection would miss.

If your team needs recurring product, price and stock data from quick-commerce platforms, this is where a managed scraping pipeline can save time. Datahut can handle the collection and delivery so your team can work with the cleaned dataset instead of maintaining the scraping infrastructure itself.

## What kind of quick-commerce data should businesses collect?

The useful feeds depend on what you're trying to measure.

### Price tracking

Track product prices by city and timestamp to identify actual price movements.

### Stock monitoring

Record when products go out of stock and when they return.

### Assortment monitoring

Track new products, removed products and city-specific listings.

### Historical data

Keep previous observations so you can compare today's catalogue with last week's or last month's.

### Seasonal monitoring

Track categories such as mangoes and grapes before, during and after the season.

The value comes from keeping these observations consistent over time.

## Limitations of this analysis

There are a few limits to keep in mind.

### Point-in-time data

The analysis represents the catalogue at the time of collection. Prices, stock and assortment can change throughout the day.

### Six-city coverage

The dataset covers Delhi, Mumbai, Bangalore, Chennai, Kolkata and Kochi. The results shouldn't automatically be applied to every Instamart market.

### Stock-outs are snapshots

An out-of-stock listing doesn't necessarily mean a product has a persistent supply problem. It may have been replenished later.

### Like-for-like pricing

Cross-city price comparisons depend on correctly matching equivalent products and pack sizes.

### Association versus causation

The relationships between stock availability, price, pack size and category describe patterns in the dataset. They don't establish why a product became unavailable.

Repeated collection would give a better basis for testing those patterns.

## The bottom line

The 881-listing Instamart dataset shows a fresh-produce catalogue that changes considerably by city.

**41% of listings were out of stock**, with much higher gaps among fruits, leafy greens, herbs and combo kits. Delhi had the highest observed stock-out rate at 52%, and the rate stayed high after removing mango and raw mango.

Pricing also varied by location. Bangalore was about 7.5% below the cross-city average in the like-for-like comparison, while Kochi was about 5.6% above it. Individual products showed larger gaps, with a median cheapest-to-most-expensive city difference of 55% among the 17 products available across all six cities.

The assortment was fragmented too. Only 6% of products appeared in all six cities, while 43% appeared in just one. Product names changed between markets, which makes product matching an important part of any automated comparison system.

The practical takeaway is straightforward: **one catalogue snapshot is useful, but a history of the catalogue is much more useful.**

If you need recurring price, stock and assortment data from Instamart, Zepto, Blinkit or other e-commerce platforms, \[talk to the Datahut team about a managed scraping pipeline\]. [Datahut e-commerce web scraping](https://www.datahut.co/solutions/ecommerce-web-scraping?utm%5Fsource=chatgpt.com)

## Frequently asked questions

### What percentage of Instamart's fruits and vegetables are out of stock?

In this Datahut analysis, **41% of 881 listings** were out of stock at the time of collection. Fruits had a 51% stock-out rate, compared with 21% for vegetables.

### Which city had the highest stock-out rate on Instamart?

Delhi had the highest observed stock-out rate at **52%**, with 111 of its 215 listings unavailable. The rate remained 50% after excluding mango and raw mango.

### Which fruits were most frequently out of stock?

Mango, muskmelon and cherries were among the categories with the highest observed stock-out rates. Mango had an 80% stock-out rate, muskmelon 82% and cherries 70%.

### How much does Instamart discount fruits and vegetables?

About **75% of listings had a displayed discount of exactly 19% or 20%**, while 92% fell between 18% and 24%.

### Is Instamart cheaper in some cities?

Yes. In a like-for-like comparison of 123 products listed in at least three cities, Bangalore was about **7.5% below the cross-city average**, while Kochi was about **5.6% above it**.

### Can the same product have different prices on Instamart?

Yes. Among 17 products priced and listed across all six cities, the median difference between the cheapest and most expensive city was **55%**.

### Does Instamart have the same assortment in every city?

No. Only **21 of 367 unique products**, or about 6%, appeared in all six cities, while 156 appeared in only one city.

### Why is product matching important when comparing Instamart across cities?

The same product can appear under different names in different markets. The dataset identified 160 products with different names across cities, including variations such as Adrak, Shunti, Inji and Adda for ginger.

### How can brands monitor Instamart prices and stock?

Manual monitoring becomes difficult when you're tracking hundreds of products across multiple cities. Scraping can provide recurring feeds of prices, stock status, assortment changes and historical records.

### How often should quick-commerce data be collected?

It depends on how quickly the category changes. Price-sensitive categories may need frequent collection, while assortment and seasonal analysis can use longer intervals. The important part is keeping the collection consistent so changes can be compared over time.