We extract hyper-local SKU catalogues, dynamic pricing, stock availability, and delivery fee structures from Gopuff. 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 Product Inventory objects from gopuff.com. All fields typed and schema-versioned.
"product_id": "PRD-98231", "name": "Ben & Jerry's Half Baked Ice Cream", "brand": "Ben & Jerry's", "category": "Ice Cream & Desserts", "unit_size": "16 oz", "puff_points_value": 450, "image_url": "https://cdn.gopuff.com/images/prd-98231.jpg"
| # | product_id | name | brand | category | sub_category | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Local Pricing & Stock objects from gopuff.com. All fields typed and schema-versioned.
"location_id": "MFC-104", "zip_code": "19123", "product_id": "PRD-98231", "current_price": 6.49, "original_price": 7.99, "discount_pct": 18, "in_stock": true, "age_restricted": false
| # | location_id | zip_code | product_id | current_price | original_price | discount_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Store / MFC Data objects from gopuff.com. All fields typed and schema-versioned.
"store_id": "MFC-104", "city": "Philadelphia", "state": "PA", "zip_code": "19123", "is_open": true, "delivery_fee": 3.95, "min_order_value": 12.99, "estimated_delivery_time": "15-25 min"
| # | store_id | address | city | state | zip_code | lat |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Complete list of extractable fields for Promotions & Deals objects from gopuff.com. All fields typed and schema-versioned.
"promo_id": "PROMO-SUMMER26", "title": "20% Off Ice Cream", "discount_type": "PERCENTAGE", "discount_value": 20, "min_spend": 15.0, "eligible_categories": "['Ice Cream & Desserts']", "end_date": "2026-08-31T23:59:59Z"
| # | promo_id | title | description | discount_type | discount_value | min_spend |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Category Taxonomy objects from gopuff.com. All fields typed and schema-versioned.
"category_id": "CAT-045", "name": "Snacks", "parent_id": "ROOT", "url_slug": "/c/snacks", "product_count": 1240, "sort_order": 2, "is_active": true
| # | category_id | name | parent_id | url_slug | product_count | banner_image_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Gopuff scraper handles the complexities of hyper-local inventory: geo-coordinate injection, dynamic pricing capture, out-of-stock monitoring, and API payload parsing - all with session management built in.
Simulate exact latitude and longitude coordinates or zip codes to capture micro-fulfilment centre specific catalogues.
Extract base prices, active discounts, and promotional bundles that vary by neighbourhood and time of day.
Track in-stock status and low-stock warnings across thousands of SKUs to map supply chain efficiency.
Monitor variable delivery fees, small order fees, and estimated delivery times based on driver availability.
Capture age-restricted flags, local alcohol tax variations, and operating hour restrictions for liquor delivery.
Extract banner promotions, multi-buy discounts, and Puff Points reward values associated with specific products.
Analyse share of shelf for CPG brands within specific categories across different geographic zones.
Run continuous pipelines to capture intraday price shifts and out-of-stock events during peak demand hours.
Extract the full category tree, including parent-child relationships and shelf placements.
Track real-time delivery ETAs to model operational capacity at specific micro-fulfilment locations.
Brief in. Clean data out.
Provide zip codes, coordinates, or category lists. We design the extraction schema together.
We configure API interceptors, proxy rotation, and geo-spoofing logic for gopuff.com.
Schema validation, null-rate checks, and location accuracy verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Instant delivery platforms rely on complex API architectures and strict geo-fencing. Here is how we maintain reliable extraction.
Gopuff inventory is strictly tied to local micro-fulfilment centres. We inject precise latitude and longitude coordinates into the session context, ensuring the API returns the exact catalogue and pricing for your target delivery zones.
Rather than scraping the DOM, our parsers intercept the underlying GraphQL and REST API payloads used by Gopuff's frontend. This yields cleaner data, captures hidden metadata like internal stock flags, and improves pipeline speed.
Instant delivery inventory turns over rapidly. We support high-frequency polling schedules to capture out-of-stock events and dynamic price changes during peak evening or weekend hours without triggering rate limits.
We route requests through localised residential proxies to match the simulated delivery coordinates. This prevents IP-based blocking and ensures the platform serves authentic local pricing rather than default fallback data.
Delivery platforms update their frontend frameworks frequently. By targeting the underlying API structures and maintaining strict schema validation, we ensure your downstream warehouse receives consistent column formats regardless of UI changes.
Retailers and instant delivery competitors track Gopuff's hyper-local pricing and delivery fee structures to optimise their own pricing models.
Consumer packaged goods brands monitor their visibility, category placement, and promotional presence across Gopuff's dark store network.
Supply chain analysts track product availability rates to identify distribution bottlenecks and regional demand spikes.
Marketing teams analyse discount depths, multi-buy offers, and Puff Points allocations to benchmark promotional spend.
Aggregators monitor dynamic delivery fees and minimum order values to understand Gopuff's unit economics in different geographic markets.
Real estate and expansion teams map Gopuff's active delivery zones and operational hours to identify underserved neighbourhoods.
"Gopuff operates hundreds of dark stores, each with unique pricing and stock levels. Capturing this data requires precise, high-frequency geo-spoofing at the zip-code level."
Extracting data from instant-delivery platforms introduces unique concurrency challenges. Inventory turns over in minutes, and pricing shifts based on local demand and driver availability. DataFlirt handles the complex coordinate simulation, session management, and payload parsing required to normalise Gopuff's hyper-local data into a unified warehouse schema.
Everything supported by our gopuff.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.
Scrapy orchestrates requests across thousands of zip codes simultaneously, managing strict concurrency limits to prevent rate-limiting while ensuring rapid data collection.
Playwright handles complex session handshakes and cookie generation, allowing our HTTP clients to query Gopuff's internal APIs directly for clean, structured JSON payloads.
Pipelines run on AWS Lambda for burst scaling during peak delivery hours. Airflow handles scheduling and dependency management, with all state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About gopuff.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available inventory, pricing, and location data is generally permissible. DataFlirt targets only public, non-authenticated storefront data. We do not extract personal user data or circumvent authentication walls. Clients should review Gopuff's terms of service and consult legal counsel for specific use cases.
We maintain a database of valid coordinate pairs and zip codes. Our crawlers inject these coordinates into the session context via headers and cookies, ensuring the platform returns the exact catalogue available for that specific location.
Yes. We can configure high-frequency polling pipelines that check stock status for specific SKUs at defined intervals, providing a clear timeline of when items go out of stock and when they are replenished.
Yes. Our pipelines capture the full catalogue, including alcohol, tobacco, and other regulated categories, along with any age-restriction flags and specific local taxes applied to these items.
Refresh frequency depends on the scope. A small list of critical SKUs across key locations can be polled every 15-30 minutes. Full catalogue sweeps across thousands of locations are typically run daily or twice daily.
We utilise large pools of residential ISP proxies, matching the IP location to the target delivery zone where possible. We also manage request concurrency strictly and simulate realistic session establishment to avoid triggering security systems.
Absolutely. We provide a sample run covering up to 5 zip codes and a selection of categories as part of the pre-engagement scoping process, allowing you to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off inventory dump or a continuous price-monitoring feed across 10,000 zip codes - we scope, build, and operate the pipeline. Tell us what you need.
