Swiggy Instamart has become one of the fastest-moving digital shelves in Indian grocery retail. Prices shift through the day, sponsored listings rotate constantly, and stock status can change by the hour. None of that is available through a public API. This guide covers what data you can extract from Swiggy Instamart, how Smacient’s Swiggy Instamart Data Extractor works, and how brands, pricing teams, and researchers use it.
Table of Contents
Why Swiggy Instamart Data Matters
Swiggy Instamart is no longer a side channel for grocery and FMCG brands. It is a real-time storefront where visibility, pricing, and promotions decide who gets the sale in the next ten minutes. A handful of reasons this data has become critical to track:
Pricing intelligence. Prices on Instamart move with demand, promotions, and competitor activity, sometimes multiple times a day. Brands that track pricing manually are always reacting a day late.
Sponsored placement visibility. Instamart runs paid product placements inside search results. Every sponsored slot a competitor buys is a slot your product does not occupy, and tracking this tells you exactly how aggressively competitors are bidding for visibility on specific keywords.
Stock monitoring. A stockout is a lost sale and often a switched customer. Knowing the moment a competitor goes out of stock, or the moment your own product does, is one of the highest-value signals in quick commerce.
Category and search ranking. Where a product lands in search results for a given keyword affects discovery more than almost anything else. Tracking rank over time shows whether your merchandising and pricing strategy is actually working.
Assortment and pack size tracking. Brands frequently run different SKUs and pack sizes across platforms. Extracting product data at scale shows you exactly what is listed, in what sizes, and at what price per unit.
Why There Is No Official Swiggy Instamart API
Swiggy does not publish a public API for Instamart product data. Like most quick commerce apps, Instamart runs on internal APIs built to serve its own app and website, not external developers. Several things make pulling this data directly difficult:
Authentication requirements. Many internal endpoints expect session tokens or cookies that are not available outside the app.
Frequently changing endpoints. Swiggy updates its app regularly, and internal API structures change without notice, which breaks naive integrations.
Rate limits. Sending too many requests in a short window triggers throttling or temporary blocks.
Anti-bot protection. Automated traffic is actively detected and blocked through fingerprinting, IP reputation checks, and other bot defenses.
This is why most businesses rely on purpose-built extraction tools rather than trying to reverse-engineer the app’s internal API themselves.
What Data You Can Extract
Smacient’s Swiggy Instamart Data Extractor (Apify actor: smacient/swiggy-instamart-data-extractor) returns a structured row for every product it finds, with the following fields:
| Field | Description |
| name | Product name |
| brand | Brand name |
| quantity | Pack size (e.g. 50 g, 1 L) |
| price | Current selling price (₹) |
| mrp | Maximum retail price (₹) |
| discount | Discount label (e.g. 20% OFF) |
| savings | Amount saved in ₹ |
| priceperunit | Unit price (e.g. ₹49.8/100 g), useful for comparing pack sizes |
| rating | Product rating |
| ratingCount | Number of reviews |
| inStock | Whether the product is currently available |
| isAd | True if the result is a sponsored listing |
| adTitle | Sponsored banner headline (ad products only) |
| category / superCategory | Product category and broader category grouping |
| shortDescription | One-line product description |
| imageUrl | Product image |
| city | City the data was scraped from |
| scrapedAt | Timestamp of extraction |
Meet the Swiggy Instamart Data Extractor
The Swiggy Instamart Data Extractor is an Apify actor built by Smacient, priced from $5.00 per 1,000 results. It runs entirely in the cloud, meaning there is nothing to install and no proxies or infrastructure to manage on your end. It carries a Full Permissions security rating on Apify and sits in the AI, Automation, and E-commerce categories.
The actor is built specifically for Indian quick commerce, with support for any Indian city Instamart operates in, and is designed to handle both keyword search and category browsing in the same tool.
How to Use It
There are two ways to pull data, depending on whether you are chasing a specific keyword or an entire category.
Search by keyword
Set City and Search Query, and run the actor. For example, City: Chennai, Search Query: Sunscreen, Max Results: 40 returns all matching products for that search term in that city.
Browse by category
If you want every product in a specific category instead of a keyword search, use a Collection ID:
- Open Swiggy Instamart in your browser.
- Navigate to any category page.
- Copy the collectionId value from the URL, for example https://www.swiggy.com/instamart/category?collectionId=1234.
- Paste it into the Collection ID field and leave Search Query blank.
Other inputs include Specific Location (optional, for hyperlocal targeting within a city) and Max Results, which caps how many products are returned per run.
Sponsored Product Detection
Every result includes an isAd field. When it is true, the product was served as a sponsored listing rather than an organic search result. The adTitle field captures the exact banner headline Swiggy displayed for that ad, for example “High performance sunscreens.” This makes it straightforward to separate organic and paid results in your dataset and see precisely which brands are paying to appear for a given keyword.
Example Workflows
Launch monitoring
Search Query: Sunscreen, City: Chennai, Max Results: 40. Within a few minutes, the extractor returns roughly 40 matching listings with current prices, MRP, discounts, price per unit, ratings, stock status, and a clear flag on which listings were sponsored.
Seasonal campaign tracking
Search Query: iPhone, City: Delhi, Max Results: 100. Useful during a festive sale window to see search rankings, sponsored products, ad titles, current prices, price per unit, savings, and category placement all in one dataset.
How the Extraction Process Works
The workflow behind every run follows the same basic path:
Search query or Collection ID, then City, then a managed proxy network, then a request to Instamart’s platform, then structured extraction into clean fields, then export as CSV or JSON.
You provide the search term or category, choose the city (and optionally a specific location), and the actor handles the rest, returning a clean dataset instead of raw webpage data. From there, results can be pulled into a spreadsheet, a BI tool, or a pricing dashboard.
Use Cases
FMCG brands track products across categories, cities, and competitors to understand where they stand in a given market.
Shelf visibility teams monitor where products land in search results and category pages, including how often sponsored placements affect what customers see first.
Marketing and growth teams keep an eye on advertising activity, promotional campaigns, and sponsored listings across the day to understand competitor ad spend patterns.
Pricing teams monitor price changes, discounts, and unit pricing across products and competing brands, often as part of a daily or hourly tracking routine.
Market researchers use category-wide pulls (via Collection ID) to build a full picture of assortment, pricing spread, and brand share within a category.
Tips for Better Results
Broader search terms return more results. “Sunscreen” returns far more listings than “SPF 50 sunscreen gel,” so start broad if you want full category coverage and narrow down afterwards.
Max Results caps the output per run. Set it higher when you need a complete category catalog rather than a quick spot check.
priceperunit is the most reliable way to compare products sold in different pack sizes, since list price alone can be misleading.
Instamart availability varies by city. If a city returns no results for a query, try the nearest metro area before assuming the product is not listed at all.
Exporting and Integrating the Data
Results are available as a table, with product images shown directly in the Apify dataset overview, or as JSON for programmatic use. Data can be exported to CSV or pulled through the Apify API, which makes it easy to feed into spreadsheets, BI dashboards, pricing tools, or any existing analytics workflow. Apify also supports scheduled runs, so the extractor can be set to run automatically on a recurring basis without manual triggering.
Who Should Use This Tool
| Who It’s For | How They Benefit |
| Ecommerce and D2C brands | Monitor competitor pricing, availability, discounts, and assortment on Instamart |
| FMCG companies | Track digital shelf visibility, sponsored placements, and product performance across cities |
| Pricing teams | Monitor dynamic pricing and make faster, data-driven pricing decisions |
| Marketing agencies | Monitor campaigns, sponsored listings, and competitor activity for multiple clients |
| Market researchers | Collect large-scale product and pricing data to identify category trends |
| Investors and analysts | Track category growth, platform activity, and emerging consumer behavior |
FAQs
For pricing and stock monitoring, hourly or daily runs are common. For broader market research, weekly runs are usually enough.
Every run pulls live data from Instamart at the moment it executes, so results reflect what is on the platform right then.
Yes. Run the actor once per city and combine the datasets, since pricing, stock, and sponsored listings can all vary by location.
Yes, using the Collection ID field to pull every product in a given category page.
Yes. Apify supports scheduled runs, so you can automate daily or hourly extraction and build a historical dataset over time.
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