Zepto built its entire business on 10-minute delivery, which means pricing, stock, and assortment can look completely different from one city to the next, sometimes from one micro-market to the next. There is no public API to check any of this. This guide covers what Zepto product data you can extract, how Smacient’s Zepto Data Extractor works, and how businesses use it for competitor tracking and market research.
Table of Contents
Why Zepto Product Data Matters
Zepto’s entire value proposition rests on speed and hyperlocal availability, which makes the underlying product data unusually volatile compared to traditional ecommerce. A few reasons this matters for anyone selling or competing on the platform:
Regional pricing gaps. The same product can carry a different price, discount, or pack size in Mumbai than in Bangalore. Brands that only check one city are missing most of the picture.
Availability swings. Stock on Zepto is tied to local dark stores, so a product can be available in one part of a city and sold out a few kilometers away. These gaps often represent short windows of opportunity for competitors.
Fast-moving promotions. Discounts and offers change frequently and are not always applied uniformly across cities, so tracking promotions in only one location will miss most competitor activity.
New product launches. Watching how quickly a newly launched product becomes available across cities tells you a lot about a competitor’s distribution strategy and rollout pace.
Assortment differences. Product variants and pack sizes are not always consistent across every city or every brand, and tracking this at scale is the only realistic way to see the full assortment landscape.
Why There Is No Official Zepto API
Zepto, like other quick commerce apps, does not offer a public API for product data. The prices, stock levels, and listings you see in the app are served by internal systems built for Zepto’s own use, not for external developers.
Pulling this data directly runs into the same obstacles common across quick commerce platforms: authentication requirements tied to app sessions, endpoints that change without warning as the app is updated, rate limits that block high-frequency requests, and anti-bot protections designed to stop automated traffic. This is why a dedicated, maintained extraction tool is far more practical than building and maintaining your own scraper.
What Data You Can Extract
Smacient’s Zepto Data Extractor (Apify actor: smacient/zepto-data-extractor) returns one structured row per product, with the following fields:
| Field | Description |
| name | Product name |
| brand | Brand name |
| price | Current selling price (₹) |
| mrp | Maximum retail price (₹) |
| discount | Discount applied |
| packsize | Pack size (e.g. 250 g, 1 L) |
| stock | Whether the product is currently available |
| rating | Product rating |
| total_ratings | Number of reviews |
| category | Product category |
| image URL | Product image |
| product_id / variant_id | Unique identifiers for the product and its specific variant |
Meet the Zepto Data Extractor
The Zepto Data Extractor is an Apify actor built by Smacient, priced from $3.50 per 1,000 results. It runs fully in the cloud, so there is nothing to install locally, and it sits in the AI, Automation, and E-commerce categories on Apify. The actor is purpose-built for Zepto specifically, rather than a generic scraper adapted to fit, which keeps the output fields clean and consistent run after run.
How to Use It
The input is straightforward:
queries is an array of product search terms, for example [“chocolate”, “milk”], which lets you pull several products in a single run instead of running the actor once per keyword.
city is the city to search in, chosen from Zepto’s supported list.
maxResults controls how many products are returned per query, with a default of 100.
A typical input looks like this:
json
{
“queries”: [“chocolate”, “sunscreen”],
“city”: “bangalore”,
“maxResults”: 50
}
Enter your search terms, select a city, set a max results value, and run the actor. Data can then be exported as JSON or CSV.
City Coverage
The extractor currently supports Chennai, Bangalore, Delhi, Mumbai, Hyderabad, Pune, Kolkata, and Ahmedabad. Running the same query across several of these cities in separate runs is the most reliable way to build a true national or multi-metro picture of pricing and availability, since a single-city snapshot can be misleading given how localized Zepto’s pricing and stock really are.
Example Workflows
New launch tracking
Queries: [“breakfast cereal”], City: Bangalore, then repeated for Mumbai, Delhi, and Hyderabad. Within minutes per run, you get product listings, current prices, discounts, product images, ratings, stock availability, and available variants for each city, letting you see exactly how a new product is rolling out.
Category benchmarking
Queries: [“chocolate”, “sunscreen”], City: Bangalore, Max Results: 50. This pulls two categories in a single run, useful for a quick multi-category competitive snapshot before a planning meeting.
How the Extraction Process Works
Every run follows the same basic path: search queries and city go in, requests are routed through a managed proxy network, the actor pulls the relevant listings from Zepto, and the result comes back as clean, structured fields rather than raw webpage data. From there, the dataset can be exported to CSV or JSON and loaded into a spreadsheet, BI tool, or pricing dashboard.
Use Cases
Launch tracking. Monitor how a newly launched product appears on Zepto and how quickly it becomes available across different markets.
Regional availability analysis. Compare stock levels, pricing, and availability across multiple cities to identify gaps and opportunities competitors may be missing.
Assortment analysis. Analyze product variants, pack sizes, and category coverage to see how your assortment compares with competitors in the same category.
Price monitoring. Track price and discount changes over time by re-running the same queries on a recurring schedule.
Rating and review tracking. Use rating and total_ratings to see how competitor products are performing with customers, not just how they are priced.
Tips for Better Results
Use the queries array to pull multiple search terms in a single run rather than running the actor separately for each keyword, since this is both faster and easier to keep organized.
Set maxResults based on your goal. The default of 100 per query works well for most category snapshots, but increase it if you need a more exhaustive catalog pull.
Always use one of the eight supported city names exactly as expected by the actor, rather than a neighborhood or locality name, since city is the required location parameter.
Track product_id and variant_id if you are monitoring the same SKU over time, since product names can shift slightly between listings while these identifiers stay consistent.
Exporting and Integrating the Data
Results export cleanly to JSON or CSV, making it straightforward to load into Excel, Google Sheets, Power BI, Tableau, or any other analytics tool. Apify also supports scheduled runs, so you can automate recurring extraction and build a historical dataset for trend analysis without manually triggering each run.
Who Should Use This Tool
| Who It’s For | How They Benefit |
| Ecommerce and D2C brands | Track pricing, stock, and assortment across multiple cities |
| FMCG companies | Monitor product visibility and availability across Zepto’s city footprint |
| Pricing teams | Compare competitor prices and react faster to changes |
| Market researchers | Build multi-city datasets to identify trends and category dynamics |
| Investors and analysts | Track platform expansion and category growth using live marketplace data |
FAQs
Daily or hourly for active pricing and stock monitoring, weekly for broader market research.
Yes. The queries field accepts an array, so you can pull several search terms in a single actor run.
Chennai, Bangalore, Delhi, Mumbai, Hyderabad, Pune, Kolkata, and Ahmedabad.
Every run pulls live data directly from Zepto at the time it executes.
Yes, using the product_id and variant_id fields to match records across multiple runs.
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