Platform · Zepto

Zepto Data Scraping Services

Q: What does Zepto collection cost?

We quote individually, driven by pincodes times SKUs times frequency, plus whether Super Saver and Cafe verticals are included as additional catalogues. A focused SKU set across a well-chosen zone sample at several-times-daily refresh sits at the lighter end. Large SKU sets across many zones at hourly refresh with all verticals sits considerably higher. One scoping call, a free pilot on your own SKUs and zones within 24 hours, then a fixed monthly quote. Request a quote.

With Pass pricing held as a separate field and the Super Saver and Cafe verticals kept out of the main catalogue.

Zepto data scraping is the automated collection of publicly visible Zepto data at pincode level — standard and Pass pricing as separate fields, Super Saver and Cafe verticals kept as distinct catalogues, listed and in_stock held apart, and delivery promise and fee structure captured per zone.

Zepto runs several pricing layers and several catalogues on one app: standard pricing, Pass member pricing, a Super Saver pack-size vertical and Cafe. Flattening them into one dataset produces a price series that jumps for reasons nobody can explain afterwards.

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zepto_pincode_2026-08-05.jsonl LIVE FEED

{"zepto_product_id":"zp-771204", "pincode":"400050", "catalogue":"grocery", "listed":true,"in_stock":true, "price_standard":249.00, "price_member":219.00, "effective_price":219.00, "mrp":299.00,"discount_pct":26.8, "pack_size":"1 L", "unit_price_computed":219.00, "handling_fee":7.00, "delivery_promise_min":9} {"zepto_product_id":"zp-990112", "catalogue":"cafe", "price_member":"null", "pack_size":"null", "note":"cafe schema — pack fields not applicable"}

2 of 4,102,900 sku-pincode rows · run 2026-08-05T10:00Zpincodes: 148 · member price on 22.8% of lines · schema v4.2

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Zepto or its owners. Zepto 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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Zepto at a glance

How we handle Zepto specifically

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

Platform: Zepto across Indian metro and tier-2 delivery zones
Granularity: Pincode level, since catalogues are selected per dark store
Pricing layers: Standard and Pass pricing captured as separate fields
Verticals: Super Saver and Cafe kept as distinct catalogues, not merged
The critical split: listed vs in_stock as separate fields, never merged
Pack architecture: Super Saver larger packs parsed and unit-normalised against standard packs
Refresh: Hourly on priority zones and SKUs; several times daily as standard
Region: India

Platform specifics

What makes Zepto data different from other quick commerce platforms

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

Pass pricing is a second price, not a promotion

Zepto Pass is a membership giving members lower prices on a large share of the catalogue. Where the member price is publicly displayed on the listing, it functions the same way Clubcard Prices do in UK grocery: it becomes the price a large share of volume transacts at.

Index direction can flip. A competitor above Zepto on standard price can be below on Pass price.
Promotional intensity is understated. Pass pricing is a discount mechanic. Excluding it makes Zepto look less aggressive than it is.
It is not a temporary promotion. Pass pricing persists rather than running on a promotional cycle, so treating it as a promo end-dates something that has no end date.

We capture price_standard and price_pass as separate fields with effective_price computed. Where a Pass price is not publicly displayed on a listing, the field is null with a reason code rather than being inferred. We do not create accounts or buy memberships to reach gated prices.

Super Saver and Cafe are separate catalogues

Zepto operates verticals with genuinely different economics, and merging them corrupts analysis in specific ways:

Super Saver carries larger pack sizes at lower unit prices, positioned against supermarket bulk buying rather than immediate-consumption quick commerce. Merged into the main catalogue, it makes Zepto's unit prices look lower than the main-range reality and makes pack architecture analysis incoherent.
Cafe is prepared food and beverages. It has nothing to do with packaged grocery pricing, and its presence in a grocery price index is simply noise.

We flag vertical on every record so each catalogue is analysable separately. Super Saver is particularly worth isolating: it is where Zepto competes with general trade on unit economics, and that comparison needs its own analysis rather than being blended into a 10-minute-delivery dataset.

Pack sizes across verticals are parsed and unit-normalised on a consistent basis, so a Super Saver pack and a standard pack are comparable where you want them to be — by choice rather than by accident.

Zone-level everything, and listed versus in-stock

As with every quick commerce platform, dark store catalogues are locally selected, so assortment, price, stock, delivery promise and fees all vary by pincode. Every record carries its zone.

And as with every quick commerce platform, listed and in_stock must remain separate. A SKU absent from a zone's range is a commercial ranging decision; a SKU listed but unavailable is a replenishment issue. Merging them produces an unavailability figure that sends supply chain teams after category decisions.

Availability percentages are computed only across the listed population. Range gaps surface as their own metric.

Zone design is the main cost lever: pincodes multiplied by SKUs multiplied by frequency. We sample one pincode per dark store cluster, weight toward revenue concentration, include income-tier variation, and validate with a rotating low-frequency sweep — the same discipline we apply across quick commerce, because the arithmetic is the same.

Scope

What we collect on Zepto, 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 pricing, assortment and availability with zone on every record
Standard and Pass pricing as separate fields where Pass pricing is publicly displayed
Vertical flag separating main catalogue, Super Saver and Cafe
listed and in_stock as separate fields, with availability computed on the listed population
Pack size parsed with unit price computed on a consistent basis across verticals
Delivery promise in minutes and displayed fee structure per zone
Category shelf position with sponsored placement flagged
Stock-out timing and duration on the hourly tier
New SKU appearance and delisting detection per zone

❌ What we do not, and why

Buying a Zepto Pass membership or creating accounts to reach gated pricing
Customer accounts, order history or personalised offers
Dark store inventory quantities, which are not published
Availability revealed only by adding items to a basket
Reviewer or customer personal data of any kind

Core Zepto fields

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

Field	What it is on this platform
zepto_product_id	The platform identifier, used as the join key
pincode / zone_id	The delivery zone the record reflects, mandatory on every row
vertical	main, super_saver or cafe — kept separate rather than merged
price_standard	The non-member price as displayed
price_pass	Pass member price where publicly displayed, null with a reason code otherwise
effective_price	Computed from Pass pricing where displayed, otherwise standard
listed / in_stock	Ranging decision and replenishment state, held separately
pack_size / pack_unit / unit_price	Parsed pack architecture and computed unit price
mrp / discount_pct	Printed MRP and computed discount against the effective price
delivery_promise_min / fees	Displayed delivery minutes and fee structure at observation
shelf_position / is_sponsored	Category ordering and whether the placement was paid

Use cases

What teams do with Zepto data

Membership-aware price benchmarking

Standard and Pass prices are held separately with an effective price computed, so an index reflects what a large share of volume actually transacts at rather than the non-member price.

Super Saver unit economics against general trade

Super Saver is isolated as its own vertical with unit-normalised pricing, so the comparison against supermarket bulk buying is analysed on its own terms rather than blended into quick commerce pricing.

Range gap versus replenishment separation

listed and in_stock are held apart with availability computed on the listed population, so ranging decisions never reach the supply chain team as unavailability.

Zone-level share of shelf and availability

Per-pincode collection with shelf position and sponsored flags gives local share of shelf and availability, which national figures cannot represent.

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

Send us a Zepto 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.

Zepto is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Zepto 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 Zepto-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

Zepto data scraping: frequently asked questions

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

Where it is publicly displayed on a listing, yes, as a separate field with an effective price computed. Pass pricing functions like loyalty pricing in UK grocery: it becomes the price a large share of volume transacts at.

We do not buy memberships or create accounts to reach gated prices. Where a Pass price is not publicly shown, the field is null with a reason code rather than being inferred or substituted.

Because they have different economics. Super Saver carries larger packs at lower unit prices, positioned against supermarket bulk buying rather than immediate consumption. Merged in, it makes Zepto's unit prices look lower than main-range reality.

Cafe is prepared food and beverages, which is simply noise in a packaged grocery index. We flag vertical so each is analysable separately, and Super Saver is particularly worth isolating.

The zone-level discipline is the same because the underlying problem is the same. The platform-specific differences are what each page handles: Zepto needs Pass pricing as a second price field and Super Saver and Cafe as separate verticals.

Most clients collect both platforms together, since the useful analysis is comparative. A combined engagement runs on one schema and one schedule rather than two contracts.

Because they answer different questions. A SKU absent from a zone's range is a commercial ranging decision. A SKU listed but unavailable is a replenishment issue.

Merged into one unavailability figure, they send supply chain teams chasing category decisions. Availability percentages are computed only across the listed population, and range gaps surface as their own metric.

Fewer than the initial instinct. Cost scales with pincodes times SKUs times frequency, so the arithmetic punishes over-scoping fast.

We sample one pincode per dark store cluster to remove redundancy, weight toward revenue concentration, include income-tier variation deliberately, and add a rotating low-frequency sweep to validate the dense sample. That design happens before we quote.

We quote individually, driven by pincodes times SKUs times frequency, plus whether Super Saver and Cafe verticals are included as additional catalogues.

A focused SKU set across a well-chosen zone sample at several-times-daily refresh sits at the lighter end. Large SKU sets across many zones at hourly refresh with all verticals sits considerably higher. One scoping call, a free pilot on your own SKUs and zones within 24 hours, then a fixed monthly quote. Request a quote.

See real Zepto 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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