We extract restaurant catalogues, menu items, dynamic pricing, delivery fees, and ETAs from Postmates. 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 Restaurants objects from postmates.com. All fields typed and schema-versioned.
"restaurant_id": "8f7b2a9c-1d4e-4f3a-9b2d-8c7e6a5b4c3d", "name": "Shake Shack", "category": "Burgers", "rating": 4.7, "review_count": 1240, "delivery_fee": 2.99, "is_open": true
| # | restaurant_id | name | chain_name | category | rating | review_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Menu Items objects from postmates.com. All fields typed and schema-versioned.
"item_id": "a1b2c3d4-e5f6-7a8b-9c0d-1e2f3a4b5c6d", "name": "ShackBurger", "base_price": 6.89, "currency": "USD", "category": "Burgers", "popular_badge": true, "out_of_stock": false
| # | item_id | restaurant_id | name | description | base_price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Modifiers objects from postmates.com. All fields typed and schema-versioned.
"group_name": "Add Bacon", "min_selections": 0, "max_selections": 1, "option_name": "Applewood Smoked Bacon", "price_delta": 1.5, "default_flag": false
| # | modifier_group_id | item_id | group_name | min_selections | max_selections | option_id |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Delivery Pricing objects from postmates.com. All fields typed and schema-versioned.
"base_delivery_fee": 1.99, "service_fee_pct": 15.0, "surge_multiplier": 1.2, "eta_min_minutes": 25, "eta_max_minutes": 35, "distance_miles": 2.4
| # | restaurant_id | location_id | timestamp | base_delivery_fee | service_fee_pct | small_order_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Search & Promos objects from postmates.com. All fields typed and schema-versioned.
"keyword": "sushi", "position": 3, "sponsored_flag": true, "promo_text": "$5 off your order", "promo_type": "FLAT_DISCOUNT", "minimum_spend": 20.0
| # | keyword | search_location | position | restaurant_id | sponsored_flag | promo_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Postmates scraper handles precise coordinate injection, GraphQL payload parsing, and dynamic pricing extraction - bypassing Uber's perimeter defence to deliver clean, structured data.
Extract categories, items, descriptions, and images for any merchant on the platform.
Capture base fees, service fees, and surge multipliers bound to specific delivery coordinates.
Map nested customisation options, required selections, and price deltas accurately.
Inject precise latitude and longitude coordinates to simulate local user sessions.
Monitor delivery time estimates and routing distances in real time.
Log merchant promotions, free delivery thresholds, and BOGO offers.
Extract weekly schedules, special holiday hours, and current open status.
Group multiple virtual brands operating from single physical coordinates.
Pull merchant star ratings, review counts, and categorical tags.
Navigate Postmates' Uber-backed infrastructure and unified merchant IDs.
Brief in. Clean data out.
Provide ZIP codes, coordinate grids, or merchant URLs. We map the extraction schema together.
We configure Playwright sessions, coordinate injection, and proxy rotation for Postmates.
Schema validation, null-rate checks, and coordinate accuracy verification before launch.
JSON, CSV, or Parquet pushed to your data warehouse or S3 bucket on agreed cadence.
Postmates shares Uber's aggressive anti-bot infrastructure. Here is how we maintain access and extract complex menu structures.
Postmates requires exact coordinate injection. We override browser geolocation APIs to simulate hyper-local traffic, ensuring you see the exact delivery fees and ETAs for a specific address.
Menu structures load via complex GraphQL queries. We intercept network payloads directly to build clean modifier trees, rather than parsing fragile DOM elements.
Postmates shares Uber's perimeter defence. We rotate residential IPs and spoof TLS fingerprints to maintain access without triggering CAPTCHAs or rate limits.
Delivery fees change based on driver supply. Our pipelines capture timestamped fee snapshots for accurate historical analysis of surge pricing models.
Menu options nest deeply. Our schema flattens these relationships into queryable relational tables, making it easy to analyze add-on pricing.
Compare menu markups and delivery fees against DoorDash and UberEats.
Identify virtual brands and map them to physical host restaurant locations.
Discover high-performing local restaurants for aggregator sales teams.
Analyze surge pricing patterns and service fee structures by neighbourhood.
Track category popularity, new item launches, and regional dietary trends.
Monitor competitor discount strategies, minimum spend thresholds, and BOGO frequency.
"Postmates holds hyper-local pricing and menu data, but extracting it requires precise coordinate injection and bypassing Uber's perimeter defence."
Food delivery aggregators rely on complex GraphQL APIs and aggressive rate limiting. Scraping Postmates at scale means managing thousands of concurrent residential proxy sessions, injecting accurate geolocation headers, and parsing deeply nested modifier trees. DataFlirt handles this infrastructure so your team can focus on market analysis.
Everything supported by our postmates.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 maintain coordinate grids mapped to residential proxies, ensuring requests originate from the exact neighbourhood being queried.
Instead of scraping the DOM, our middleware intercepts Postmates' internal API responses, extracting clean JSON before rendering.
Pipelines run on Kubernetes and AWS Lambda. Airflow handles scheduling across thousands of coordinate permutations.
Data delivered to where your team already works — no new tooling required.
About postmates.com scraping, legality, and pipeline operations.
Ask us directly →We inject exact latitude and longitude coordinates into the browser session and match them with geographically proximate residential IPs.
Yes. We parse the underlying GraphQL payloads to extract every modifier group, option, and price delta, delivering it as a flattened relational schema.
Postmates uses Uber's anti-bot infrastructure. We bypass this using residential proxies, TLS fingerprinting, and realistic request timing.
For defined merchant lists, we can run high-frequency pipelines capturing fee and ETA changes at sub-hourly intervals.
We cross-reference merchant coordinates and addresses to identify multiple virtual storefronts operating from a single physical kitchen.
Pipelines start at defined city grids or specific merchant lists. Contact us to scope your geographic requirements.
Yes, we log active merchant promotions, free delivery thresholds, and specific item discounts visible to the unauthenticated user.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off menu catalogue dump or a continuous fee-monitoring feed across 10,000 locations - we scope, build, and operate the pipeline. Tell us what you need.
