We extract restaurant menus, dynamic delivery fees, prep time estimates, and retail inventory from Wolt. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Restaurant Venues objects from wolt.com. All fields typed and schema-versioned.
"venue_id": "5e1a1b2c3d4e5f6g7h8i9j0k", "name": "Burger Joint Central", "category": "Burgers", "rating": 8.4, "review_count": 1245, "delivery_fee_base": 1.99, "delivery_time_min": 25, "delivery_time_max": 35, "wolt_plus_eligible": true, "is_open": true
| # | venue_id | name | category | rating | review_count | address |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Menus & Items objects from wolt.com. All fields typed and schema-versioned.
"item_id": "item_987654321", "name": "Double Smash Burger", "description": "Two 100g beef patties, cheddar, pickles, house sauce.", "category": "Mains", "base_price": 12.5, "currency": "EUR", "popular_badge": true, "dietary_tags": "['Contains gluten', 'Contains lactose']", "out_of_stock": false
| # | venue_id | item_id | name | description | category | base_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Modifiers & Add-ons objects from wolt.com. All fields typed and schema-versioned.
"item_id": "item_987654321", "modifier_group_name": "Choose your side", "option_name": "Sweet Potato Fries", "price_impact": 2.5, "is_required": false, "max_selections": 1, "min_selections": 0, "default_selection": false
| # | item_id | modifier_group_id | modifier_group_name | option_id | option_name | price_impact |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Delivery & Fees objects from wolt.com. All fields typed and schema-versioned.
"venue_id": "5e1a1b2c3d4e5f6g7h8i9j0k", "distance_meters": 1850, "delivery_fee": 3.49, "service_fee": 0.99, "small_order_surcharge": 0.0, "total_minimum_spend": 12.0, "estimated_time_minutes": 30, "dynamic_surge_multiplier": 1.2
| # | venue_id | user_latitude | user_longitude | distance_meters | delivery_fee | service_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Grocery & Retail objects from wolt.com. All fields typed and schema-versioned.
"venue_id": "wolt_market_helsinki", "ean_code": "6411200109215", "product_name": "Oat Barista Edition", "brand": "Oatly", "weight_volume": "1L", "price": 2.45, "unit_price": "2.45/L", "stock_status": "In stock", "promotion_badge": "Discounted"
| # | venue_id | ean_code | product_name | brand | weight_volume | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Wolt scraper navigates spatial APIs, dynamic pricing, and deep modifier trees to extract precise delivery intelligence across all supported countries.
Inject precise latitude and longitude payloads to reveal location-restricted venues, accurate delivery fees, and actual prep times.
Capture categories, items, descriptions, dietary tags, and high-resolution image URLs across restaurant and retail venues.
Extract nested add-on groups, price impacts, required selections, and default options to reconstruct accurate cart logic.
Monitor base delivery fees, service fees, small order surcharges, and surge pricing multipliers mapped against specific delivery distances.
Scrape FMCG product catalogues, EAN codes, unit pricing, and stock availability from Wolt Market and retail partners.
Aggregate venue scores, review counts, and customer feedback text to measure brand performance and customer satisfaction.
Extract data across Finland, Germany, Japan, Israel, and 20+ other Wolt markets using localised headers and currency normalisation.
Track real-time venue status, temporary closures, and scheduled operating hours to measure actual availability.
Run continuous pipelines at hourly or daily cadences. We emit only changed records to reduce your storage footprint.
Brief in. Clean data out.
Provide target cities, coordinate grids, or specific venue URLs. We map the extraction schema.
We configure coordinate injection, XHR interception, proxy rotation, and anti-bot circumvention for wolt.com.
Schema validation, null-rate checks, and modifier tree depth verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on schedule.
Wolt relies heavily on spatial APIs and dynamic state. Here is how we maintain stable extraction where basic HTTP clients fail.
Wolt does not show delivery fees or venue availability without a user location. Our pipeline accepts coordinate grids (lat/lon) and injects them into API payloads to extract accurate distance-based pricing and delivery estimates.
Parsing React DOM elements is slow and fragile. We intercept Wolt's backend XHR and GraphQL responses directly, capturing clean, heavily nested JSON data for menus and modifiers before it hits the browser rendering engine.
Wolt employs strict rate limiting and bot detection. We route requests through ISP-grade residential proxies matching the target country, coupled with realistic TLS fingerprints and HTTP/2 headers to maintain high success rates.
Restaurant menus feature deep modifier logic (e.g. 'Choose 2 sides', 'Extra cheese +1.50'). We extract and normalise these nested arrays into queryable relational structures suitable for SQL databases.
Delivery algorithms change frequently. We monitor schema drift and alert on sudden spikes in null values for delivery fees or service charges, ensuring your pricing models remain accurate.
Restaurant chains track competitor menu pricing, promotional offers, and combo deals across specific delivery zones.
Aggregator platforms and logistics teams monitor Wolt's dynamic delivery fees, surge multipliers, and service charges by distance.
CPG brands monitor product availability, stock status, and retail pricing within Wolt Market dark stores.
Operators identify underserved cuisine categories and track virtual brand saturation within specific geographic radii.
B2B suppliers extract venue lists, ratings, and operating hours to identify high-performing independent restaurants for sales outreach.
Real estate and investment firms map venue density and delivery time estimates to evaluate neighbourhood commercial activity.
"Wolt's hyper-local architecture means pricing and availability change per coordinate. Extracting it requires geographic precision, not just static page parsing."
Most teams fail at food delivery scraping because they ignore spatial dynamics. Wolt requires precise latitude and longitude injection, session hydration, and dynamic fee tracking. DataFlirt manages the coordinate grids and API orchestration so your analysts get structured location-aware data.
Everything supported by our wolt.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
We manage geographic coordinate grids and inject precise spatial payloads into Wolt's API to extract accurate, location-dependent pricing and availability.
Playwright network interception captures raw GraphQL and XHR responses directly from Wolt's backend, ensuring data completeness without fragile DOM parsing.
Pipelines run on AWS Lambda and ECS. Apache Airflow handles scheduling, coordinate batching, and SLA alerting. All state is stored in managed PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About wolt.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Wolt is generally permissible under applicable law. DataFlirt targets only public, non-authenticated restaurant, menu, and pricing data. We do not extract personal user data or circumvent authentication walls. Clients should review Wolt's ToS and consult legal counsel for their specific use cases.
Wolt requires a user location to calculate delivery fees and times. We accept a list of target coordinates (latitude/longitude) from you and inject these into the API payloads during extraction, ensuring the fees and times reflect exact delivery routes.
Yes. We extract full retail inventory from Wolt Market dark stores and partner grocery retailers, including EAN codes, unit pricing, stock status, and promotional badges.
Restaurant menus contain nested modifier groups (e.g., size, toppings, side choices). We extract these deep arrays and can deliver them as nested JSON or flatten them into relational CSV tables depending on your warehouse requirements.
We can configure pipelines to run at hourly, daily, or weekly intervals. For dynamic data like delivery surge pricing or out-of-stock statuses, we recommend higher frequency runs scoped to specific high-priority coordinate grids.
Yes. Our infrastructure supports extraction across all Wolt markets, including Finland, Germany, Japan, Israel, and Eastern Europe. We handle local currency normalisation and language headers automatically.
Yes. We provide a sample run of up to 50 venues or a specific coordinate grid as part of our pre-engagement scoping process. This allows you to validate schema fit and modifier completeness before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off city extraction or continuous dynamic fee monitoring across multiple countries, we build and operate the pipeline. Tell us what you need.
