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ONDC (Open Network for Digital Commerce) is India’s government-backed open protocol for digital commerce — an interoperable network architecture that allows any buyer app (Paytm, Magicpin, Pincode by PhonePe, Mystore) to transact with any seller app (restaurant listing platforms) across a shared, decentralised food catalog, without any single private platform owning the listing data or controlling the pricing. ONDC Food Data Scraping is built to read this open network the way it actually operates — aggregating menu pricing across multiple buyer apps to surface the true ONDC price for a given restaurant dish, calculating the ONDC-versus-Zomato/Swiggy price delta that results from the dramatically lower platform commission restaurants pay on ONDC (typically 3–5% versus 15–30% on closed platforms), identifying seller network participant identity for each listing, and discovering the new categories of food seller — home chefs, tiffin services, and micro-restaurants — that ONDC has onboarded and that do not appear on any closed delivery platform. Paired with Food Data Intelligence, this turns India’s decentralised, open-protocol, commission-democratising food delivery network into pricing and market intelligence decisions grounded in how ONDC is reshaping food delivery economics for restaurants, new food entrepreneurs, and Indian consumers.
Food delivery categories tracked — restaurants, cloud kitchens, tiffin services, home chefs, and micro-restaurants listed across ONDC's open buyer and seller network in India
Restaurant menu listings monitored across ONDC buyer apps including Paytm, Magicpin, Pincode by PhonePe, and Mystore in INR across 100+ Indian cities
Field-level accuracy on ONDC menu price, Zomato/Swiggy price delta, seller network participant identity, veg/non-veg classification, and platform commission saving data
Refresh aligned to restaurant menu price updates across buyer apps, new ONDC network participant onboarding, and ONDC-vs-aggregator price parity shift monitoring
ONDC food data scraping is the structured extraction of menu and restaurant listing data from across the ONDC open network — aggregating INR item prices, restaurant and seller details, veg and non-veg classifications, buyer app platform identity, seller network participant (Seller NP) identity, and cross-platform price comparison against Zomato and Swiggy — from a decentralised open-protocol network where there is no single URL or platform to scrape, requiring extraction logic that operates across multiple buyer apps simultaneously to reconstruct a complete picture of ONDC food pricing.
An ONDC Multi-Buyer-App Extraction Engine reads listings as they appear across Paytm, Magicpin, Pincode by PhonePe, Mystore, and other buyer apps, since the same restaurant's ONDC pricing may surface differently across buyer apps and since new buyer and seller network participants join the ONDC network continuously.
Every extraction run follows a consistent schema so restaurant, food brand, and market intelligence teams can compare ONDC pricing against closed-platform aggregators, track seller network participant penetration, and discover new food seller categories without manually checking each buyer app or restaurant listing across cities separately.
Tracking something more specific — ONDC-versus-Zomato price delta by cuisine type and city, ONDC-only home chef and tiffin seller count by city, seller network participant market share by city or category, or ONDC price advantage persistence over time as the network matures? The schema is built per-engagement around what your open-network food strategy or India market intelligence team actually needs.
These use cases demonstrate how ONDC food network data-driven systems improve decision-making and support scalable India open-commerce food strategy and restaurant pricing intelligence.
ONDC vs. Zomato/Swiggy Price Delta Analysis by Restaurant, Dish Type, and City
ONDC-Only Food Seller Discovery (Home Chefs, Tiffin Services, Micro-Restaurants)
Seller Network Participant Market Share and Penetration Tracking
Buyer App Price Consistency Analysis Across ONDC Network
Platform Commission Saving Impact on Consumer Price Benchmarking
ONDC Restaurant Onboarding Rate and Network Growth Monitoring
Web Fusion Data builds extraction logic around how India's ONDC open food network actually operates — not a single-platform menu scraper repointed at a decentralised open protocol where the commercially meaningful intelligence requires aggregation across multiple buyer apps, seller network participant identification, and cross-platform price comparison against closed aggregators, all of which standard food delivery scraping tools are architecturally unable to perform.
Multi-Buyer-App Aggregation Intelligence
Extracts and aggregates ONDC food listings across all major buyer apps — Paytm, Magicpin, Pincode by PhonePe, Mystore, and others — since there is no single ONDC catalog URL and the same restaurant's ONDC listing may surface at different prices or with different availability across buyer apps, making cross-buyer-app aggregation the foundational requirement for complete ONDC food pricing intelligence.
ONDC-versus-Aggregator Price Delta Awareness
Captures the price differential between a restaurant's ONDC listing price and its equivalent Zomato or Swiggy listing price for the same dish, since the lower platform commission restaurants pay on ONDC — typically 3–5% versus 15–30% on closed aggregators — enables restaurants to pass savings to consumers, and the magnitude and persistence of that price advantage is the primary commercial intelligence signal for understanding ONDC's disruption of India's food delivery pricing ecosystem.
ONDC-Only New Seller Discovery Intelligence
Built to flag and track food sellers who list exclusively on the ONDC network and do not appear on Zomato or Swiggy — home chefs, neighbourhood tiffin services, micro-restaurants, and small catering operations — since ONDC's dramatically lower commission structure has onboarded a category of informal food entrepreneurs that closed platforms' commission economics excluded, and tracking their emergence and geographic spread is a distinct market intelligence opportunity that no closed-platform scraper can surface.
Seller Network Participant Ecosystem Tracking
Tuned to identify and track seller network participant (Seller NP) identity alongside every ONDC listing, since the Seller NP landscape — which technology platforms restaurants use to list on ONDC — is itself a commercially meaningful intelligence signal about the open network's technology ecosystem development and about which participants are gaining share in onboarding restaurant supply to the ONDC network.
An ONDC food-focused extraction run moves through four stages built around the structure of India's open-protocol, multi-buyer-app, decentralised food delivery network.
Defines which ONDC buyer apps, seller network participants, food categories, and Indian cities to monitor, keeping the extraction scope focused on the open-network food segment or price comparison question relevant to your strategy.
Pulls restaurant name, food seller type, item price across buyer apps, veg/non-veg tag, Seller NP identity, delivery partner, ETA, and ONDC-only seller flag fields by reading listings as they appear across tracked ONDC buyer apps, using a Multi-Buyer-App Extraction Engine that aggregates into a single consistent schema within one daily run.
Cross-checks ONDC prices against Zomato and Swiggy equivalent listings for the same restaurant and dish to calculate price delta and commission saving, flags new seller onboardings, buyer app price inconsistencies, and ONDC-only seller emergence events before data reaches you.
Delivers structured datasets and city-level open-network food summaries on a daily schedule — with enhanced monitoring during ONDC network expansion events, government-backed promotional campaigns, and periods of significant price delta movement between ONDC and closed aggregator pricing.
| # | Item Name | Restaurant | City | ONDC Price (INR) | Zomato Price | Saving | Seller NP | Veg/Non-Veg | Captured |
|---|---|---|---|---|---|---|---|---|---|
| 01 | Paneer Butter Masala + 2 Roti | Maa Ki Rasoi (Tiffin) | Jaipur | ₹149 | ₹199* | −25% | Seller App A | Veg | 2026-06-29 |
| 02 | Chicken Biryani Half Plate | Biryani Bros Cloud Kitchen | Pune | ₹179 | ₹219 | −18% | Seller App B | Non-Veg | 2026-06-29 |
| 03 | Masala Chai + Veg Puff Combo | Home Chef — Sunita's Kitchen | Indore | ₹65 | Not Listed | ONDC Only | Seller App C | Veg | 2026-06-29 |
| 04 | Thali (Full Meal — 7 Items) | Annapurna Bhoj | Lucknow | ₹220 | ₹269 | −18% | Seller App A | Veg | 2026-06-29 |
| 05 | Double Egg Roll | Kolkata Roll Centre | Kolkata | ₹89 | ₹109 | −18% | Seller App B | Non-Veg | 2026-06-29 |
We bring deep expertise across multiple industries, offering tailored Web Scraping Services that align with your specific goals, delivering high-value insights from complex, unstructured web data.
We build Custom Web Scraping Solutions that scale effortlessly—from small datasets to millions of pages—ensuring speed, reliability, and performance without compromise.
We design every solution from scratch to match your exact data requirements, including specific fields, formats, and frequency—making our Web Data Extraction Services truly flexible.
We ensure precision through smart data parsing, rigorous validation, and multi-step quality checks—so your Data Scraping Service delivers clean, dependable results every time.
We offer seamless API-based Web Scraping, delivering structured data directly to your systems in real time, ensuring easy integration with your existing tools and workflows.
We follow ethical practices and data compliance protocols, helping you stay secure, responsible, and legally sound while extracting public web data using our Web Crawler Services.
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