Platform · Swiggy

Swiggy Data Scraping Services

With verticals collected separately, because food delivery and quick commerce behave nothing alike.

Swiggy data scraping is the automated collection of publicly visible Swiggy data with its verticals kept distinct — food delivery menus at item and modifier level, fee stack components, membership pricing where publicly displayed, and restaurant coverage by locality — rather than merged into one confused catalogue.

Swiggy is several businesses on one brand. Food delivery, quick commerce and dining out have different pricing logic, different availability behaviour and different fields that matter. Collecting them into one dataset produces a table where most columns are null on most rows.

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swiggy_menu.jsonl LIVE FEED

{"swiggy_restaurant_id":"sw-771204", "vertical":"food_delivery", "city":"Bengaluru","locality":"Koramangala", "serves_locality":true, "menu_item_id":"mi-88120", "item_name":"Paneer Butter Masala", "base_price":365.00, "price_member":339.00, "delivery_fee":32.00, "platform_fee":12.00, "surge_fee":18.00, "delivery_promise_min":31, "item_available":true} {"swiggy_restaurant_id":"sw-771204", "locality":"Whitefield", "serves_locality":false, "note":"coverage gap — competitor serves this locality"}

2 of 11,204,700 menu-item rowsverticals kept separate · localities: 340 · schema v4.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Swiggy or its owners. Swiggy and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

Our Data Powers

B2C Marketplace

amazon

D2C + Marketplace

NYKAA

D2C + Marketplace

Walmart

FMCG Marketplace

udaan

Food Delivery

Uber Eats

Quick Commerce

blinkit

Taxi Aggregator

Uber

E-Commerce

Tmall

Swiggy at a glance

How we handle Swiggy specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Platform: Swiggy food delivery, with quick commerce and dining verticals kept separate
Structural choice: Vertical separation — each with its own schema rather than one merged table
Granularity: Menu item and modifier level for food delivery
Fee stack: Delivery, platform and surge fees decomposed as displayed
Membership: Membership pricing captured where publicly shown, as a separate field
Coverage: Restaurant presence by city and locality, with new listings and closures
Refresh: Daily standard; sub-daily during peak hours
Region: India

Platform specifics

What makes Swiggy data different

These are the reasons a Swiggy dataset needs its own handling rather than a shared retail schema.

Vertical separation is the design decision that matters

Swiggy operates food delivery, quick commerce and dining-out under one brand. They share an app and share nothing else that matters to a dataset.

Food delivery is menu items with modifiers, kitchen-driven availability and restaurant-level identity.
Quick commerce is packaged SKUs with pack sizes, unit pricing, dark store ranging and pincode-level catalogues.
Dining out is offers and reservations with no item-level pricing at all.

A merged schema means pack size and unit price are null on every restaurant row, and modifier trees are null on every grocery row. The dataset looks large and is mostly empty.

We collect each vertical with its own schema and its own refresh logic. Most clients want one, occasionally two. Where you want quick commerce specifically, that runs through the same pincode-level design as our other quick commerce collection rather than being bolted onto food delivery.

The fee stack changes faster than the menu

On Swiggy the customer-facing total is item price plus delivery fee, platform fee, surge and taxes, less any promotion. Those components are displayed separately and move independently.

The practical point most clients discover late: fee structures change considerably more often than menu prices. A dataset that captured fees once and menu prices daily will drift, and the drift shows up as unexplained gaps between your modelled basket cost and reality.

We capture each component at observation time on every collection, not once at setup. Fee changes then become their own visible signal — and platform fee changes across the market are often a sharper read on platform economics than anything in the menu data.

Delivery promise in minutes is captured alongside, since it varies by locality and time of day and is a direct competitive comparison against other platforms serving the same address.

Locality matters more than city

Restaurant availability, delivery fees and promise times vary within a city, not just between cities. A restaurant serving one locality may not serve the next, and fees differ by distance.

Collecting at city level produces an average across localities that describes no actual customer experience. It also misses coverage gaps, which are exactly what a brand or a chain wants to know about — where can customers actually order from us, and where can they order from a competitor instead.

We collect per locality with the locality recorded on every row, and design the locality set with you. As with pincode design in quick commerce, a well-chosen locality sample usually answers more than exhaustive coverage at a fraction of the cost, because adjacent localities frequently return near-identical results.

Scope

What we collect on Swiggy, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

Food delivery menus at item and modifier level with the full option tree
Verticals collected separately, each with its own schema
Delivery, platform and surge fees captured at every observation
Membership pricing where publicly displayed, as a separate field
Restaurant coverage by city and locality, with new listings and closures
Delivery promise in minutes per locality and observation time
Item availability through the day
Rating distribution and review velocity without reviewer profiles
Promotional offers with displayed mechanics

❌ What we do not, and why

Restaurant commission rates or platform economics, which are not published
Order volumes or delivery partner data
Customer account data, order history or personalised offers
Membership prices visible only after signing in
Reviewer names, profiles or review histories

Core Swiggy fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
swiggy_restaurant_id	Platform restaurant identifier, the join key
vertical	food_delivery, quick_commerce or dining — kept separate, never merged
city / locality	Geographic context, with locality mandatory since fees and coverage vary within cities
menu_item_id / item_name	Item identity and name as published
base_price / modifier_price	Item price and per-modifier pricing across the option tree
price_member	Membership price where publicly displayed, null with a reason code where absent
delivery_fee / platform_fee / surge_fee	Fee components captured at every observation
delivery_promise_min	Displayed delivery minutes at observation
item_available	Availability at observation time
serves_locality	Whether the restaurant serves this locality, for coverage gap analysis
rating_avg / rating_distribution	Rating metrics without reviewer identity

Use cases

What teams do with Swiggy data

Coverage gap analysis by locality

Restaurant serviceability is collected per locality, showing where customers can order from you and where only competitors are available — invisible in city-level data.

Fee structure monitoring

Fee components are captured at every observation rather than once at setup, so platform fee changes surface as their own signal instead of drifting silently into basket models.

Cross-platform menu price comparison

Item and modifier pricing joins to the same restaurant on other platforms with confidence-scored matching, making channel price gaps measurable.

Chain menu consistency auditing

Item and modifier prices are compared across a chain's outlets and localities, revealing deviations from intended menu pricing.

The 24-hour sample — run on your sources, not ours

Send us a Swiggy item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned inside two business days
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Swiggy is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Swiggy data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what food delivery data covers, and a Swiggy-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Swiggy data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Because they share an app and nothing else that matters to a dataset. Food delivery is menu items with modifiers and kitchen-driven availability; quick commerce is packaged SKUs with pack sizes and pincode-level ranging; dining out has no item-level pricing at all.

Merged, pack size is null on every restaurant row and modifier trees are null on every grocery row. The dataset looks large and is mostly empty.

Yes, but through our pincode-level quick commerce design rather than bolted onto food delivery. It needs listed versus in-stock separation, pack size parsing and zone-level catalogues — none of which food delivery data uses.

Most clients want one vertical or the other. Where you want both, you get two clean datasets that join on locality rather than one merged table.

Because restaurant availability, delivery fees and promise times all vary within a city. A restaurant serving one locality may not serve the next.

City-level collection averages across localities and describes no actual customer experience. It also hides coverage gaps — which is usually exactly what a brand or chain wants to know.

Considerably more often than menu prices, which catches most clients out. A dataset that captured fees once at setup and menu prices daily will drift, and the drift shows up as unexplained gaps between modelled and actual basket cost.

We capture every fee component at every observation. Platform fee changes across the market are often a sharper read on platform economics than anything in the menu data.

Where it is publicly displayed to an anonymous user, yes, as a separate field with the standard price retained. Where it requires signing in, we do not collect it and the field is null with a reason code.

We never backfill a member price with the standard price. That substitution is the same trap as loyalty pricing in grocery data and it can invert a comparison.

We quote individually. Drivers are restaurant count, locality coverage, whether full modifier trees are required, refresh frequency and how many verticals you need.

A defined restaurant set across selected localities at daily refresh sits at the lighter end. Broad locality coverage with modifier trees and peak-hour collection sits higher. One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real Swiggy data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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