We extract restaurant catalogues, menu pricing, delivery SLAs, Instamart SKUs, and Swiggy Dineout offers. 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 Listings objects from swiggy.com. All fields typed and schema-versioned.
"restaurant_id": "23948", "name": "Meghana Foods", "area": "Koramangala", "rating": 4.5, "rating_count": "10K+", "cost_for_two": 500.0, "delivery_time_mins": 35, "swiggy_one_eligible": true
| # | restaurant_id | name | city | area | latitude | longitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Menu Items objects from swiggy.com. All fields typed and schema-versioned.
"item_id": "11938472", "name": "Chicken Boneless Biryani", "category": "Biryani", "price": 385.0, "is_veg": false, "in_stock": true, "bestseller_tag": true, "is_customisable": true
| # | restaurant_id | item_id | name | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Instamart Inventory objects from swiggy.com. All fields typed and schema-versioned.
"sku_id": "IM_948271", "product_name": "Amul Taaza Toned Fresh Milk", "brand": "Amul", "category": "Dairy & Bakery", "mrp": 28.0, "selling_price": 28.0, "in_stock": true, "delivery_time_mins": 12
| # | store_id | sku_id | product_name | brand | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Offers & Discounts objects from swiggy.com. All fields typed and schema-versioned.
"offer_id": "OFF_4829", "offer_type": "PERCENTAGE", "description": "60% off up to 120", "coupon_code": "TRYNEW", "min_order_value": 149.0, "max_discount": 120.0, "swiggy_one_only": false
| # | restaurant_id | offer_id | offer_type | description | coupon_code | min_order_value |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Dineout & Bookings objects from swiggy.com. All fields typed and schema-versioned.
"restaurant_id": "DO_39281", "name": "Toit Brewpub", "location": "Indiranagar", "dineout_rating": 4.7, "flat_discount_pct": 15, "avg_cost_for_two": 2000.0, "table_availability": true
| # | restaurant_id | name | location | dineout_rating | flat_discount_pct | bank_offers |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Swiggy scraper navigates location based rendering, dynamic pricing, and encrypted API payloads to deliver structured food delivery and grocery data.
Extract names, ratings, FSSAI numbers, delivery SLAs, and promoted tags across entire city grids using automated coordinate mapping.
Track dish prices, category structures, customisation options, and out-of-stock statuses for millions of menu items daily.
Monitor dark store inventory, FMCG brand visibility, MRP versus selling price, and delivery estimates down to the pincode level.
We simulate precise GPS coordinates to bypass Swiggy's hyperlocal geofencing and capture accurate delivery availability.
Capture Swiggy One pricing, bank partner discounts, and coupon codes to analyse promotional density across aggregators.
Track estimated delivery times during peak and off-peak hours to benchmark logistics performance across different zones.
Extract table booking availability, flat discounts, and dine-in specific ratings for restaurants participating in the Dineout program.
Identify which restaurants are paying for ad placements in search results and category pages to understand aggregator monetisation.
Run continuous pipelines that output only price changes or new menu items, reducing warehouse bloat and processing costs.
Brief in. Clean data out.
Provide target cities, pincodes, or specific restaurant URLs. We define the extraction schema and frequency.
We configure coordinate spoofers, API interceptors, proxy rotation, and session management for swiggy.com.
Schema validation, null-rate checks, and geolocation accuracy tests before full production launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or API webhook on agreed cadence.
Swiggy's architecture relies heavily on user location, encrypted payloads, and dynamic rate limiting. Here is how we maintain pipeline stability.
Swiggy menus and Instamart inventories only render based on precise user coordinates. We use a grid-mapping algorithm to spoof GPS locations at 2km intervals, ensuring complete coverage of a city's serviceable area without missing hidden dark stores.
Rather than scraping the DOM, our Playwright instances intercept Swiggy's underlying GraphQL requests. We parse the structured JSON responses directly, ensuring high fidelity data extraction for complex nested fields like menu customisations.
Aggressive crawling triggers WAF blocks and API rate limits. We distribute requests across thousands of Indian residential IP addresses, pacing API calls to mimic normal user browsing behaviour and application load times.
Food delivery platforms frequently update their internal API schemas to support new features like Match Day offers or Swiggy Minis. Our extraction logic uses adaptive mapping to handle unexpected nulls and schema drift without failing the entire run.
Swiggy requires valid session tokens even for unauthenticated browsing. We maintain a pool of fresh browser sessions, rotating cookies and device fingerprints before they expire or trigger bot detection heuristics.
Cloud kitchens and QSR chains monitor competitor pricing, discount strategies, and new item launches across different city zones.
FMCG brands track product availability, share of search, and promotional visibility on Swiggy Instamart against rival brands.
Pricing teams ingest aggregator data to build dynamic pricing algorithms that adjust menu costs based on local demand and competitor offers.
Market researchers compare restaurant overlap, delivery SLAs, and pricing parity between Swiggy and Zomato to estimate market share.
F&B operators analyse restaurant density, cuisine gaps, and average order values by pincode to identify prime locations for new cloud kitchens.
Private equity firms track dark store expansion, active restaurant counts, and discount depths to evaluate the health of the hyperlocal delivery sector.
"Swiggy processes millions of hyperlocal transactions daily. Extracting its catalogue reveals the exact state of urban Indian consumption, block by block."
Most teams underestimate the complexity of scraping hyperlocal aggregators. Extracting Swiggy menus requires precise GPS coordinate spoofing, intercepting encrypted GraphQL payloads, and managing continuous session rotation. DataFlirt handles the infrastructure so your analysts can focus on pricing parity and market share.
Everything supported by our swiggy.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 handles crawl orchestration and grid traversal. Playwright manages coordinate spoofing, cookie sessions, and GraphQL interception.
We route requests through Indian residential IP pools to match the expected geographic origin of Swiggy traffic, preventing WAF blocks.
Pipelines run on AWS ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About swiggy.com scraping, legality, and pipeline operations.
Ask us directly →Swiggy relies on precise GPS coordinates to render available restaurants and Instamart stores. We use a grid-mapping algorithm that feeds spoofed coordinates into Playwright sessions, allowing us to systematically crawl an entire city block by block.
Yes. We track Instamart dark stores by passing specific coordinates, extracting the full FMCG catalogue, MRP, selling price, stock status, and category structures.
Yes. We extract restaurant listings participating in Swiggy Dineout, including flat discount percentages, bank offers, operating hours, and table booking availability.
For targeted competitor monitoring, pipelines can run hourly to capture intraday price fluctuations and out-of-stock events. Full city-wide catalogue refreshes typically run on a daily or weekly cadence.
Yes. Our extraction logic parses the offer strings and promotional tags to differentiate between standard user pricing and Swiggy One member benefits, including free delivery thresholds.
By default, we deliver JSON with nested arrays for menu categories, items, and customisation options (e.g., crust types, add-ons). We can also provide flattened CSVs if required for your warehouse schema.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need to monitor 50 competitor cloud kitchens or map Instamart inventory across an entire city, we build and operate the pipeline. Tell us what you need.
