Platform · Rappi

Rappi Restaurant Data Scraping

The restaurant surface of a platform that also runs quick commerce, pharmacy and financial services on one app.

Rappi restaurant data scraping collects restaurant menus, pricing, fees and availability across Latin American markets. This is the restaurant surface, distinct from the platform's own quick-commerce stores. The platform runs several verticals on one account, so subscription and wallet mechanics affect what is paid without changing the listed price.

Our Rappi Turbo page covers the quick-commerce side and its FX handling. This is the restaurant half, where the merchant rather than the platform sets the price.

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rappi_restaurant.jsonl LIVE FEED

{"platform":"rappi","surface":"restaurant", "price_setter":"merchant","country":"CO", "item_name_local":"as published, Spanish", "item_price":32000,"currency_local":"COP", "fx_observed_at":"2026-08-25T11:40-05:00","fx_source":"named", "min_realisable_price":36500} {"surface":"rappi_turbo","price_setter":"platform", "note":"the other surface. platform sets that price. shared schedule, never blended"} {"subscription_gated":true,"gated_share":0.29, "payment_condition_text":"as published", "effective_price":"null", "inflation_adjustment":"not_applied"}

3 of 2,404,110 merchant-item rows · LatAmrestaurant surface · FX per observation · schema v1.0

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

Rappi at a glance

How we handle Rappi specifically

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

Platform: Rappi — Latin American markets
This page: The restaurant surface
The other: Rappi Turbo — quick commerce
Price setter here: The merchant
Multi-vertical: Subscription and wallet affect what is paid
Currency: FX stamped per observation, not daily
Inflation: No adjustment applied. Index choice is yours
Markets: Several, priced independently

Platform specifics

Restaurant surface, multi-vertical account

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

Merchant-set prices, platform-set stores

The same app carries restaurant listings and the platform's own quick-commerce stores. Those are different commercial arrangements.

On restaurants the merchant sets the price, frequently above dine-in to cover commission.
On the platform's own stores the platform sets it.
Availability means different things on each.
Blending them produces a series that moves with surface mix.

surface and price_setter on every record, with a shared schedule where both are collected.

And the account mechanics sit above both

A subscription offering delivery-fee and discount benefits applies across verticals, and wallet or payment promotions reduce what is paid without changing the listing.

So the listed price is observable and the amount paid frequently is not. subscription_gated is recorded with gated_share reported, and effective_price is null with a reason where it depends on account state — the position our GrabFood page takes on the same structure.

LatAm currency handling, applied to restaurants

Everything our Rappi Turbo page argues about currency applies here, and we apply it rather than restating it at length.

FX stamped at the moment of observation, not at a daily rate, because rates move within the day in several of these markets.
Local currency retained as the primary figure.
Rate source recorded, since sources differ and in some markets the choice is an analytical decision.
No inflation adjustment applied inside the feed.

Payment conditions

Discounts tied to a payment method are common across these markets. Captured as payment_condition_text as published, with no effective price computed — whether a customer uses that method is not observable.

Markets and languages

country is a dimension with markets_observed recorded per batch. Spanish and Portuguese names retained exactly, translation additive and dated.

Standard mechanics and what we do not collect

Modifier groups make the headline item price not the entry price. Fees are separate fields. Availability is address-level. As our Uber Eats page sets out.

What we do not collect

Subscription-tier pricing behind a login. Measured as a gap, never closed with an account.
Wallet balances or account state. Personal data in every market.
An effective price where the reduction depends on account state or a payment condition.
Order volumes, merchant revenue or take rate.
Courier, customer or reviewer identity.
Other verticals on this scope. Quick commerce, pharmacy and financial services are separate.

The last point is worth naming. Pooling verticals into one dataset produces exactly the blended-series failure our surface separation page describes.

Scope

What we collect on Rappi, 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

surface and price_setter on every record
Shared schedule where the quick-commerce surface is also collected
FX stamped per observation, with the rate source recorded
Local currency retained as the primary figure
No inflation adjustment applied inside the feed
subscription_gated with gated_share reported
effective_price null where it depends on account state or a payment condition
country as a dimension, with markets_observed per batch
Names retained in Spanish and Portuguese, translation additive

❌ What we do not, and why

Restaurant and quick-commerce surfaces blended
A daily FX rate applied to intraday observations
An inflation adjustment applied inside the feed
An effective price from a payment condition or account state
Wallet balances, order volumes, revenue or personal data

Core Rappi fields

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

Field	What it is on this platform
platform / surface / price_setter	restaurant surface, merchant sets the price
country / markets_observed	Which market, and what the batch saw
merchant_id / merchant_name / city	A commercial entity, and where
item_name_local / language_detected	Retained exactly, with the language
item_price / currency_local	The primary figure
fx_rate / fx_observed_at / fx_source	Stamped, and which source
subscription_gated / gated_share	Where a benefit requires a tier
payment_condition_text	As published. Not a price
effective_price / effective_null_reason	Null where account state decides it
modifier_groups / min_realisable_price	The number a customer can pay
delivery_fee / service_fee / panel_schedule_id	Fees, and the shared schedule

Use cases

What teams do with Rappi data

LatAm restaurant menu pricing

Country as a dimension with FX stamped per observation and the rate source recorded, in markets where rates move within the day.

Cross-surface comparison

Restaurant and quick-commerce surfaces on a shared schedule with the price setter recorded, so a merchant-set price and a platform-set price are never blended.

Subscription gap measurement

Gated share reported per market, so a promotional analysis states what proportion sat behind a tier it could not see.

Merchant menu benchmarking

Modifier groups with minimum realisable price, so merchants listing identically are not treated as equivalent.

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

Send us a Rappi 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.

Rappi is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Rappi 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 & restaurant data covers, and a Rappi-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

Rappi data scraping: frequently asked questions

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

Different surface and different price setter. On restaurants the merchant sets the price; on the platform's own quick-commerce stores the platform sets it.

Availability also means different things on each. Where both are collected they share a schedule so a comparison holds.

Because rates move within the day in several of these markets, so a converted series built on a daily closing rate describes a conversion that did not apply when the price was displayed.

We also record the rate source, since in markets with parallel rates that choice is an analytical decision rather than a technical one.

No. A price series in a high-inflation market shows movement that is partly monetary, and the choice of index and base period is yours.

We deliver unadjusted local-currency figures with precise timestamps.

No. Where a benefit requires a signed-in tier, or a discount depends on a payment method, the reduction depends on account state we do not observe.

The field is null with a reason and we report the gated share instead.

Quick commerce, pharmacy and financial services are separate scopes with their own arguments.

Pooling verticals into one dataset produces a blended series that moves with vertical mix rather than with pricing.

We quote individually. Country count is the main driver, and markets needing sub-daily collection for currency reasons cost more.

One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real Rappi 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.

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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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