Platform · Swiggy Instamart

Swiggy Instamart Data Scraping

Q: What does Instamart collection cost?

We quote individually. Drivers are pincodes times SKUs times observations per day, with pincode count the dominant lever. Adding Instamart to an existing India panel costs materially less than commissioning it alone. One scoping call, a free pilot within 24 hours on your own SKUs and pincodes, then a fixed monthly quote. Request a quote.

Instamart and Swiggy food delivery share an app and a login. They do not share a catalogue, a price or a fulfilment model.

Swiggy Instamart data scraping collects the quick-commerce catalogue, pincode-level pricing, fee stack and availability from Swiggy's dark-store service. As with Flipkart Minutes and Amazon Now, the discipline that matters is that Instamart is a separate surface from Swiggy food delivery — every record carries a surface flag and the two are never blended.

One of the three players that actually matter in Indian quick commerce, and the one most often confused with its own parent app in a data feed.

Get a free sample See sample data

Free pilot on your own Swiggy Instamart list, returned in 24 hours. No card, no trial clock — and you keep the sample data either way.

instamart.jsonl LIVE FEED

{"sku":"EX-4471","surface":"instamart", "pincode":"400050","city":"Mumbai", "price":142.00,"mrp":175.00, "serviceable":true,"in_stock":true, "promise_minutes":16, "promise_text_raw":"15-20 mins", "delivery_fee":0.00,"handling_fee":9.00, "pack_size":500,"pack_unit":"g"} {"sku":"EX-4471","surface":"instamart", "promise_text_raw":"10 mins","observed_at":"2025-11-02", "note":"earlier wording — a change here may be presentation, not fulfilment"} {"sku":"EX-9902","pincode":"400097", "serviceable":false,"in_stock":"null", "caution":"outside delivery radius — not a stockout"}

3 of 7,204,880 sku-pincode rows · pincodes: 340surface flag on every record · promise text retained raw · schema v1.1

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Swiggy Instamart or its owners. Swiggy Instamart 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

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NYKAA

D2C + Marketplace

Walmart

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udaan

Food Delivery

Uber Eats

Quick Commerce

blinkit

Taxi Aggregator

Uber

E-Commerce

Tmall

Swiggy Instamart at a glance

How we handle Swiggy Instamart specifically

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

Platform: Swiggy Instamart — dark-store quick commerce, India
The trap: Not Swiggy food delivery. Same app, different catalogue and economics
Granularity: Pincode. A city figure describes no actual shopper
Delivery promise: Displayed promise captured as shown — see the note on the 10-minute claim
Fees: Delivery, handling and surge fees as separate fields from item price
Availability: Serviceability and stock as two distinct states
Pack: Parsed to unit price on a stated basis
Refresh: Several times daily; hourly on priority SKUs

Platform specifics

What is specific to Instamart

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

One app, two surfaces, and the blend that ruins a feed

Swiggy runs food delivery from restaurants and Instamart from its own dark stores. They share an app, a login and a brand, and almost nothing that matters to a dataset.

Instamart holds its own inventory. Restaurant listings are merchant-set.
Pack architecture differs. Instamart carries retail grocery packs; restaurant items are prepared portions.
Availability is dark-store level, so it is a pincode question rather than a store-open question.
The fee stack differs between the two surfaces on the same order value.

Every record carries surface set to instamart. If you also take Swiggy restaurant data, the two arrive as separate records rather than one blended view. This is the same rule applied to Flipkart Minutes against Flipkart, and Amazon Now against Amazon marketplace.

The delivery promise, and a regulatory change worth knowing

On a quick-commerce service the promise is part of the offer, not a service level. An attractive price with no serviceable slot is not an offer a shopper can take.

We capture promise_minutes exactly as displayed, plus serviceability for the pincode.

A change that affects how this field behaves in India

In early 2026 the Indian government restricted the use of the "ten-minute delivery" advertising claim, on safety and labour grounds. Platforms have adjusted how they present delivery times as a result.

That matters for a data series in a specific way: a change in the displayed promise may reflect a presentation change rather than an operational one. A promise that moved from "10 mins" to a range, or to a softer phrasing, is not evidence that fulfilment got slower.

We capture the promise as displayed, with its raw text retained alongside any parsed minutes value, so a change in wording is visible as a wording change rather than silently becoming a performance signal. Series that predate the change should be read with that break in mind, and we flag it rather than smoothing over it.

Pincode economics, and why we design rather than sweep

Instamart assortment, price and availability all vary by pincode, because each dark store carries what its catchment buys.

Serviceability is a state, distinct from an item being out of stock. Conflating them understates your coverage gap and overstates your stockouts.
Availability is the field that moves most, and it moves within the day.
Cost scales with pincodes times SKUs times observations, and pincode count is the dominant lever.

We design a pincode panel with you covering the catchment types that matter, rather than sweeping every pincode in a metro. An exhaustive sweep frequently moves the same way as a well-designed sample at several times the cost.

Where you are tracking Instamart alongside Blinkit, Zepto, Flipkart Minutes and Amazon Now — which is how buyers usually want it — the panel is shared across all five so the records are comparable by construction.

Scope

What we collect on Swiggy Instamart, 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

Pincode-level price with the pincode on every record
surface set to instamart, never blended with Swiggy restaurant records
Delivery promise as displayed, with raw text retained alongside parsed minutes
Serviceability and stock as two distinct states
Delivery, handling and surge fees as separate fields
Pack size and unit parsed, with unit price on a stated basis
Promotional mechanics as displayed, separate from base price
Category and subcategory as the app presents them
Observation timestamp, required at this refresh rate

❌ What we do not, and why

A blended price across Instamart and Swiggy restaurant listings
Sales, order volumes or demand, none of which is published
Customer, rider or order data
A promise-wording change interpreted as a fulfilment change
Prices behind a signed-in session or account-specific offers

Core Swiggy Instamart fields

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

Field	What it is on this platform
sku / product_key	Your identifier and our matched identity
surface	Constant: instamart. Never blended
pincode / city	Mandatory. The unit of analysis
price / mrp	Displayed price and stated MRP
serviceable / in_stock	Two distinct states, never collapsed
promise_minutes / promise_text_raw	Parsed value plus the exact wording as displayed
delivery_fee / handling_fee / surge_fee	Fee components, separate from item price
pack_size / pack_unit / price_per_unit	Parsed pack and unit price
promo_mechanic / promo_text	Mechanics as displayed
category / subcategory	As the app presents them
observed_at	Timestamp

Use cases

What teams do with Swiggy Instamart data

The five-platform India quick-commerce panel

Instamart alongside Blinkit, Zepto, Flipkart Minutes and Amazon Now on one shared pincode set and schedule, which is the only arrangement where the five are honestly comparable.

Pincode assortment and coverage analysis

Which SKUs are ranged in which catchments, with serviceability separated from stock so a coverage gap is not read as a sell-out.

Fee stack competitiveness

Delivery, handling and surge fees tracked as their own series, since on quick commerce the fee stack frequently carries more competitive movement than the item price.

Promotional depth by catchment

Mechanics captured separately from base price, showing where promotional intensity concentrates geographically rather than as a national average.

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

Send us a Swiggy Instamart 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 within 24 hours
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 Instamart is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Swiggy Instamart 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 quick commerce data covers, and a Swiggy Instamart-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 Instamart data scraping: frequently asked questions

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

No, and treating it as the same is the most common error. Instamart runs from Swiggy's own dark stores with its own catalogue and pincode-level availability. Restaurant listings are merchant-set with prepared portions.

Every record carries surface: instamart, and if you take both feeds they arrive as separate records rather than one blended view.

Because in early 2026 India restricted the ten-minute delivery advertising claim, and platforms adjusted how they present delivery times in response.

That means a change in the displayed promise may be a presentation change rather than an operational one. Keeping the raw wording alongside the parsed value means a rewording is visible as a rewording, instead of silently becoming a performance signal in your series.

Because a dark-store service has no national price or national assortment. Each store carries what its catchment buys, and availability changes within a city through the day.

A city-level figure on quick commerce describes no actual shopper.

We can, and we usually recommend against it. Cost scales with pincodes times SKUs times observations, and an exhaustive sweep frequently moves the same way as a designed sample at several times the price.

We agree a panel covering the catchment types that matter and report what it does and does not represent.

No. No quick-commerce platform publishes sales or order volumes. Availability transitions and ranking are sometimes used as proxies and both are weak in a market where one stockout or promotion moves ranking sharply.

We deliver those as what they are and leave the inference to you.

We quote individually. Drivers are pincodes times SKUs times observations per day, with pincode count the dominant lever. Adding Instamart to an existing India panel costs materially less than commissioning it alone.

One scoping call, a free pilot within 24 hours on your own SKUs and pincodes, then a fixed monthly quote. Request a quote.

See real Swiggy Instamart 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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