Platform · Wolt

Wolt Restaurant Data Scraping

Q: How do you handle markets the platform has left?

We record markets_observed per batch rather than assuming a fixed list, so a withdrawal appears as a structural change rather than as a silent drop in coverage. That discipline came from exactly this problem on another platform.

The restaurant surface, not the grocery one. On this platform those are two different businesses with two different price setters.

Wolt restaurant data scraping collects restaurant menus, pricing, fees and availability across the platform's European and Nordic markets. This is deliberately the restaurant surface rather than the platform's own grocery stores — on Wolt those are different businesses: merchants price restaurant items, the platform prices its own stores.

Our Wolt Market page covers the grocery side. This is the other half, and keeping them apart is the whole point.

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wolt_restaurant.jsonl LIVE FEED

{"platform":"wolt","surface":"restaurant", "price_setter":"merchant", "country":"FI","merchant_id":"wl-44120", "item_name_local":"as published", "item_price":14.90,"currency":"EUR", "min_realisable_price":16.40, "panel_schedule_id":"wolt-both-surfaces"} {"surface":"wolt_market","price_setter":"platform", "note":"the OTHER surface. platform sets that price. never blended with this one"} {"subscription_gated":true,"gated_share":0.19, "markets_observed":21, "caution":"markets recorded per batch. a withdrawal shows as structural, not as lost coverage"}

3 of 2,204,110 merchant-item rows · EU / Nordicsrestaurant surface · merchant sets the price here · schema v1.0

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

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NYKAA

D2C + Marketplace

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Food Delivery

Uber Eats

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Taxi Aggregator

Uber

E-Commerce

Tmall

Wolt at a glance

How we handle Wolt specifically

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

Platform: Wolt — Europe and the Nordics
This page: The restaurant surface
The other: Wolt Market — the platform's own stores
Price setter here: The merchant
Price setter there: The platform
So: surface on every record. Never blended
Countries: Many, priced independently
Refresh: Daily standard; matched to the grocery surface where both are collected

Platform specifics

Two surfaces, two price setters

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

Why this is a separate page from Wolt Market

The platform operates a restaurant marketplace and its own grocery stores under one app. Those are different commercial arrangements, and conflating them is the failure our surface separation page describes.

On the restaurant surface the merchant sets the price, frequently above dine-in to cover commission.
On the grocery surface the platform sets the price for its own stock.
Availability means different things — a restaurant pausing versus a store being out of an item.
Assortment logic differs entirely.

So surface and price_setter are on every record, and a blended series across both would move with surface mix rather than with pricing.

Where both are collected

They are observed on a shared schedule, because a comparison between surfaces taken hours apart contains a timing artefact. panel_schedule_id is shared.

Many markets, and Nordic conventions

The platform operates across a wide European and Nordic footprint, and the markets differ.

country is a dimension on every record, with regional figures as computed rollups.
Currencies differ, including several non-euro Nordic currencies. FX stamped per observation.
Languages differ widely — names retained exactly, translation additive, matching on structure rather than titles.
Fee structures differ by market, so delivery, service and small-order fees are separate fields per market.

Subscription

The platform operates a subscription offering delivery-fee benefits. Where a benefit requires a signed-in subscription, the field is null with a reason and gated_share is reported. We do not create accounts.

Standard marketplace mechanics

Modifier groups make the headline item price not the entry price, so min_realisable_price is the comparable figure. Availability is address-level. All as our Uber Eats page sets out.

Market presence and what we do not collect

Footprint changes

The platform has entered and exited markets. markets_observed is recorded per batch rather than assumed from a list, so a withdrawal appears as a structural change — the discipline our foodpanda page established after exactly this problem.

Merchant overlap

Restaurants frequently list on more than one platform in these markets, at different prices because commission differs. also_on_other_platform is recorded where both are in scope, and the price gap is computed from paired records rather than asserted.

What we do not collect

Subscription-gated pricing. Measured as a gap, never closed with an account.
Order volumes, merchant revenue or platform take rate. Not published.
Courier, customer or reviewer identity. Counts and ratings only.
Grocery-surface data on this scope. That is the Wolt Market page, deliberately.

Scope

What we collect on Wolt, 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 grocery surface is also collected
country as a dimension, with FX stamped per observation
markets_observed recorded per batch, never assumed
Subscription-gated pricing null with a reason, with gated_share reported
Modifier groups with minimum realisable price
Fees as separate fields, per market
Names retained exactly, translation additive and dated
also_on_other_platform where more than one platform is in scope

❌ What we do not, and why

Restaurant and grocery surfaces blended
The two surfaces observed on different schedules and compared
An account created to reach subscription pricing
A markup inferred where only one platform is observed
Order volumes, revenue, courier, customer or reviewer data

Core Wolt 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	As displayed
fx_rate / fx_observed_at	Stamped at the observation moment
modifier_groups / min_realisable_price	The number a customer can pay
delivery_fee / service_fee / small_order_fee	Each separately, per market
subscription_gated / gated_share	Where a benefit requires a tier
also_on_other_platform	Where more than one platform is in scope
panel_schedule_id / observed_at	So cross-surface comparison holds

Use cases

What teams do with Wolt data

European and Nordic restaurant pricing

Country as a dimension with FX per observation across a wide footprint, where markets differ in currency, fee structure and competitive position.

Cross-surface comparison done correctly

Restaurant and grocery surfaces on a shared schedule with price setter recorded, so the difference between a merchant-set and a platform-set price is an observation rather than a blend.

Cross-platform merchant markup

The same merchant on more than one platform as separate records, so the commission-driven price difference is computed from paired records.

Market presence monitoring

markets_observed per batch, so an entry or exit appears as a structural change rather than as lost coverage.

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

Send us a Wolt 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.

Wolt is usually collected alongside its competitors

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

Wolt data scraping: frequently asked questions

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

Different surface, different price setter. On the restaurant surface the merchant sets the price; on the grocery surface the platform sets it for its own stock.

Availability also means different things — a restaurant pausing versus a store being out of an item. Blending them produces a series that moves with surface mix rather than with pricing.

Often yes, and if you do they are observed on a shared schedule. A comparison between surfaces taken hours apart contains a timing artefact.

The gap between a merchant-set restaurant price and a platform-set grocery price is a real finding, but only on matched timing.

Where publicly displayed, yes. Where a benefit requires a signed-in subscription, no — we do not create accounts in any market.

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

Frequently, because commission rates differ so the merchant's markup differs. We record presence on other platforms where they are in scope and compute the gap from paired records.

Where only one platform is observed, no markup is inferred.

We record markets_observed per batch rather than assuming a fixed list, so a withdrawal appears as a structural change rather than as a silent drop in coverage.

That discipline came from exactly this problem on another platform.

We quote individually. Country count is the main driver rather than merchant volume, because each market needs its own currency, language and fee handling.

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

See real Wolt 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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