Platform · Flipkart Minutes

Flipkart Minutes Data Scraping

Q: What does Flipkart Minutes collection cost?

We quote individually. Drivers are pincodes times SKUs times observations per day, and pincode count is the main lever. One scoping call, a free pilot within 24 hours on your own SKUs and pincodes, then a fixed monthly quote. Request a quote.

A different catalogue, a different price and a different availability model from Flipkart itself — on the same app.

Flipkart Minutes data scraping collects the quick-commerce catalogue, pincode-level pricing and availability from Flipkart's dark-store service. The critical point is that Minutes is not Flipkart: the same SKU can carry a different price, a different pack and a different availability state on the two surfaces, and merging them produces a price series that describes neither.

Buyers ask for Blinkit, Zepto, Instamart, Flipkart Minutes and Amazon Now together, as one competitive set. That is the right way to buy it — and Minutes is the one with a marketplace attached.

Get a free sample See sample data

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

flipkart_minutes.jsonl LIVE FEED

{"sku":"EX-4471","surface":"minutes", "pincode":"110016","city":"Delhi", "price":148.00,"mrp":175.00, "serviceable":true,"in_stock":true, "promise_minutes":11, "delivery_fee":0.00,"peak_fee":15.00, "pack_size":500,"pack_unit":"g", "price_per_unit":0.296, "observed_at":"2026-08-25T19:20Z"} {"sku":"EX-4471","surface":"marketplace", "price":129.00,"pack_size":1000, "note":"same SKU, other surface — delivered separately, never averaged"} {"sku":"EX-9902","pincode":"110092", "serviceable":false,"in_stock":"null", "caution":"outside delivery radius — not a stockout"}

3 of 6,204,110 sku-pincode rows pincodes: 284surface flag on every record · never blended with marketplace · schema v1.1

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

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Flipkart Minutes at a glance

How we handle Flipkart Minutes specifically

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

Platform: Flipkart Minutes — dark-store quick commerce
The trap: Same app as Flipkart marketplace, different catalogue and price
Granularity: Pincode. A city-level figure describes no actual shopper
Catalogue: Dark-store range, far narrower than the marketplace
Availability: Per dark store, so it changes across a city within hours
Surface flag: surface: minutes on every record, never merged with marketplace
Delivery promise: Minutes-level, and it gates whether an item is orderable at all
Refresh: Several times daily; hourly during peak windows

Platform specifics

What makes Flipkart Minutes different from Flipkart

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

One app, two catalogues, and merging them is the classic error

Flipkart Minutes runs from dark stores. Flipkart marketplace runs from sellers and warehouses. They share an app, a login and often a product identity — and almost nothing else.

Price differs for the same SKU, sometimes substantially, and moves independently.
Pack architecture differs. Quick commerce favours smaller packs; marketplace favours bulk.
The Minutes catalogue is a fraction of the marketplace range, chosen per dark store.
Availability is dark-store level, so it is a pincode question, not a national one.

Every record carries surface set to minutes. If you also take our Flipkart marketplace feed, the two arrive as separate records against the same product key rather than one blended price. A blended price across the two would be an average of two different offers a shopper never sees together.

Pincode is the unit, and it is the whole cost driver

The inquiries we receive name this precisely: daily pricing across all pincodes of Delhi. That framing is correct and it is also what makes quick-commerce data expensive.

Assortment differs by pincode, because each dark store carries what its catchment buys.
Price can differ by pincode on the same SKU.
Availability differs constantly and is the field that changes most.
Serviceability is a state. A pincode outside the delivery radius is not the same as a pincode with the item out of stock, and conflating them understates your coverage gap.

Cost scales with pincodes × SKUs × observations. We sample rather than sweep: a designed pincode set that covers the catchment types you care about beats an exhaustive sweep that costs four times as much and moves the same way.

Delivery promise decides whether a price is real

On a ten-minute service the promise is not a service level, it is part of the offer. An item priced attractively with no delivery slot is not an offer a shopper can take.

We capture the displayed promise in minutes, the serviceability state for the pincode, and any surge or peak-hour fee as a separate field from the item price. Fees on quick commerce move independently of product price and folding them together hides the mechanic that actually varies.

This is the same discipline as our delivery promise data, applied where the promise is the product.

Scope

What we collect on Flipkart Minutes, 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 minutes, so it is never merged with Flipkart marketplace
Dark-store catalogue as ranged for that pincode
Availability per pincode, with serviceability as a separate state
Displayed delivery promise in minutes
Delivery, handling and peak-hour fees as separate fields from item price
Pack architecture parsed, with unit price on a stated basis
Promotional mechanics as displayed, kept separate from base price
Category and subcategory as the app presents them

❌ What we do not, and why

A blended price across Minutes and Flipkart marketplace
Sales, order volumes or demand, none of which is published
Customer, rider or order data
Prices behind a signed-in session or account-specific offers
A national price, which does not exist on a dark-store service

Core Flipkart Minutes 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: minutes. Never blended with marketplace records
pincode / city	Mandatory. The unit of analysis
price / mrp	Displayed price and the stated MRP
serviceable / in_stock	Two distinct states, never collapsed
promise_minutes	Delivery promise as displayed
delivery_fee / peak_fee / handling_fee	Fee components, separate from item price
pack_size / pack_unit / price_per_unit	Parsed pack architecture and unit price
promo_mechanic / promo_text	Mechanics as displayed
category / subcategory	As the app presents them
observed_at	Timestamp, required because availability moves hourly

Use cases

What teams do with Flipkart Minutes data

Five-platform India quick-commerce panel

Flipkart Minutes alongside Blinkit, Zepto, Instamart and Amazon Now on the same pincode set, which is how buyers actually ask for it and the only way the comparison is valid.

Pincode-level assortment gap analysis

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

Marketplace versus Minutes price positioning

The same product on both surfaces, delivered as separate records, showing where the quick-commerce premium sits and where it does not exist.

Fee and promise competitiveness

Delivery, peak and handling fees tracked separately from item price, because on a ten-minute service the fee stack moves independently and shapes the real cost.

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

Send us a Flipkart Minutes 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.

Flipkart Minutes is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Flipkart Minutes 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 Flipkart Minutes-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

Flipkart Minutes 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 here. Minutes runs from dark stores with its own catalogue, its own prices and pincode-level availability. Flipkart marketplace runs from sellers and warehouses.

Every record carries surface: minutes. If you take both feeds they arrive as separate records against the same product key, never blended.

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. It is the same reason our Blinkit and Zepto work is pincode-first.

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 well-designed sample at four times the price.

We design a pincode set covering the catchment types that matter to you 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 a single stockout or a promotion moves ranking sharply.

We deliver availability and ranking as what they are, and leave any demand inference to you rather than shipping it as a number.

It is designed to sit in the same panel. The inquiries we receive name Blinkit, Zepto, Instamart, Flipkart Minutes and Amazon Now together, which is correct — the comparison only works on a shared pincode set and a shared observation schedule.

We scope them as one engagement rather than five, so the records are comparable by construction.

We quote individually. Drivers are pincodes times SKUs times observations per day, and pincode count is the main lever.

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 Flipkart Minutes 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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