Food Delivery Dataset: App, Pricing & Review Data for Enterprise Intelligence
Structured, continuously refreshed food delivery data across the world’s leading food delivery apps, restaurant aggregators, and meal delivery platforms — built for enterprise teams that need decision-grade food delivery market intelligence at scale.
- GDPR-Compliant Sourcing
- Restaurant-Level Accuracy
- Deduplicated & Normalized
- Enterprise SLA
- AI & ML Ready
500M+
Food delivery records indexed
50+
Food delivery platforms covered
60+
Countries & markets
Daily
Menu & pricing refresh
What Is the WebDataInsights Food Delivery Dataset?
The WebDataInsights Food Delivery Dataset is a comprehensive, production-ready collection of structured data sourced from the world’s leading food delivery apps, restaurant aggregator platforms, and on-demand meal delivery services. It captures restaurant listings, menu pricing, delivery intelligence, and customer experience data across all major food delivery markets globally.
Food delivery is one of the most data-intensive segments in digital commerce. Menu prices change daily. Restaurant rankings shift with every algorithm update. Customer sentiment reflects both food quality and delivery execution. Our food delivery dataset is engineered to capture that complexity — validated, deduplicated, and normalized across all covered food delivery platforms before delivery to enterprise teams.
Enterprise teams use this food delivery dataset to power restaurant competitive intelligence, benchmark menu pricing across food delivery apps, train AI recommendation and demand forecasting models, monitor brand reputation across delivery platforms, and conduct food delivery market sizing research across cities and geographies.
The dataset is structured around three specialized sub-datasets — each independently licensable or available as a unified food delivery intelligence package — designed to serve distinct use cases across food delivery app intelligence, menu pricing analytics, and customer review sentiment data.
Food Delivery App Dataset
Comprehensive restaurant and menu listing data across food delivery apps: restaurant profiles, cuisine types, menu items, dish descriptions, dietary tags, operating hours, delivery zones, and platform ranking signals.
Food Delivery Pricing Dataset
Menu-level and item-level pricing intelligence: dish prices, delivery fees, surge pricing signals, platform discount patterns, promotional offers, minimum order values, and historical price time-series across food delivery platforms.
Food Delivery Review Dataset
Structured customer review data from food delivery platforms: restaurant ratings, dish-level feedback, delivery experience scores, NLP sentiment analysis, verified order flags, and aspect-level tags across all covered food delivery apps.
Three Specialized Food Delivery Sub-Datasets
Each sub-dataset is purpose-built for specific food delivery industry use cases, delivered in normalized, schema-consistent format for immediate integration into your analytics, pricing, or AI infrastructure.
Restaurant & Menu Listing Intelligence
Complete restaurant and menu catalog data across all major food delivery apps — structured for competitive benchmarking, catalog enrichment, platform ranking analysis, and AI recommendation model training across every covered market.
- Restaurant name, cuisine type & category tags
- Full menu structure: categories, items & descriptions
- Dish names, portion sizes & customization options
- Dietary & allergen tags (vegan, gluten-free, halal, kosher)
- Restaurant operating hours & delivery zone coverage
- Platform ranking signals & sponsored listing flags
- Estimated delivery time (EDT) by restaurant & city
- Restaurant profile images & dish image asset URLs
Menu Pricing & Delivery Fee Intelligence
Multi-dimensional food delivery pricing intelligence — from real-time menu item prices to 18-month historical archives — built for restaurant competitive pricing engines, food delivery market research, and platform commission analysis.
- Menu item prices: base price & platform-listed price
- Delivery fee, surge delivery fee & service charge
- Minimum order value (MOV) by restaurant & platform
- Platform discount offers & promo code classification
- Historical menu price time-series (up to 18 months)
- Multi-platform price comparison by dish & restaurant
- Platform commission markup indicators
- Subscription & membership pricing variants (Swiggy One, Zomato Gold)
Customer Review & Food Experience Sentiment
Millions of structured customer review records from food delivery apps enriched with NLP-derived sentiment analysis covering food quality, delivery experience, and packaging — purpose-built for restaurant reputation monitoring, brand intelligence, and LLM fine-tuning.
- Overall restaurant rating (1–5) & review count
- Review title & full review body text
- Food quality sentiment score & label
- Delivery experience sentiment score & label
- Packaging quality sentiment signal
- Verified order flag & order item reference
- Aspect-level tags (taste, portion size, temperature, accuracy)
- Review date, platform source & reviewer city
Data Schema & Field Reference
Every food delivery sub-dataset ships with a standardized schema and comprehensive field documentation. Below are the core fields across all three datasets — delivered in your chosen format, ready for immediate pipeline integration.
App Dataset Schema
Pricing Dataset Schema
Review Dataset Schema
See the Food Delivery Data Before You Buy
A representative composite record from the Food Delivery App + Pricing Dataset — normalized, enriched, and schema-consistent across all covered food delivery platforms and city markets.
{
"restaurant_id": "WDI-FD-MUM-00293841",
"restaurant_name": "Behrouz Biryani - Andheri",
"cuisine_types": ["Mughlai", "Biryani", "Indian"],
"city_code": "MUM",
"platform_source": "swiggy",
"dish_name": "Royal Chicken Biryani",
"dish_id": "BHR-MUM-DISH-00412",
"dietary_tags": ["non-vegetarian", "contains-dairy"],
"base_price": 349.00,
"platform_listed_price": 399.00,
"delivery_fee": 40.00,
"surge_delivery_fee": 0.00,
"min_order_value": 149.00,
"discount_offer": "60% off up to INR 120",
"currency_code": "INR",
"est_delivery_mins": 32,
"is_sponsored": false,
"overall_rating": 4.4,
"review_count": 12847,
"food_quality_sentiment": 0.86,
"delivery_sentiment": 0.74,
"packaging_sentiment": 0.79,
"snapshot_ts": "2025-06-15T12:20:00Z"
}* Sample records are illustrative. Full dataset records include all schema fields above, plus extended restaurant and city-level attributes. Request a full sample export →
Global Food Delivery Data Coverage at Platform Depth
Our food delivery data collection infrastructure spans 50+ food delivery platforms, restaurant aggregators, and meal delivery apps across 60+ countries and 200+ metropolitan markets — capturing restaurant listings, menu pricing, and customer review data at daily and sub-daily refresh cadences calibrated to how quickly food delivery menus and prices actually change.
Food delivery data requires city-level and restaurant-level resolution that national-level market reports cannot provide. Our collection methodology captures pricing and menu signals at the city and neighbourhood level, enabling the granular competitive intelligence that restaurant operators and food delivery market researchers need.
Coverage spans all major food delivery cuisine segments: Indian, Chinese, Italian, Mexican, American fast food, Japanese, Middle Eastern, Mediterranean, Southeast Asian, and more — with category-level and cuisine-level filtering available on request for enterprise configurations.
Enterprise clients can configure coverage by specific food delivery platform, metropolitan market, cuisine category, restaurant tier (QSR / casual dining / premium), or brand scope — with custom collection additions available for regional platforms and dark kitchen networks not in the standard coverage list.
Food Delivery Platforms & Apps We Cover
Our food delivery dataset spans the world’s most commercially significant food delivery apps, restaurant aggregators, and on-demand meal delivery platforms — segmented by region and market for precision competitive coverage.
DoorDash
Food Delivery · US
Uber Eats
Global Food Delivery
Grubhub
Food Delivery · US
Deliveroo
Food Delivery · Europe
Wolt
Food Delivery · Europe
Just Eat
Food Delivery · Europe
Glovo
Food & Delivery · Europe
Rappi
Food Delivery · Latin America
Swiggy
Food Delivery · India
Zomato
Food Delivery · India
EatSure
Multi-brand · India
Magicpin
Hyperlocal Food · India
ONDC Food
Open Network · India
Dunzo Food
Instant Delivery · India
Grab Food
Food Delivery · SEA
Gojek GoFood
Food Delivery · SEA
FoodPanda
Food Delivery · Asia
Talabat
Food Delivery · MENA
HungerStation
Food Delivery · KSA
Meituan
Food Delivery · China
Ele.me
Food Delivery · China
15+ More
Custom on request
Also covering: QSR brand direct ordering portals (McDonald’s, KFC, Pizza Hut, Domino’s), dark kitchen networks, corporate meal platforms, and regional restaurant aggregators. Request custom platform coverage →
How Enterprise Teams Use Food Delivery Data
From restaurant competitive intelligence to AI-powered recommendation engines, the WebDataInsights Food Delivery Dataset powers commercial and strategic decisions across the full food delivery ecosystem.
Restaurant Competitive Intelligence
Monitor competitor restaurant menus, track dish-level price changes across food delivery apps, identify new menu launches, and benchmark your own restaurant pricing and assortment against competitive restaurants in the same city and cuisine category — in real time.
- Dish-level price tracking across platforms
- Menu launch & item discontinuation alerts
- Cuisine category benchmarking by city
- Delivery fee & MOV competitive analysis
QSR & Restaurant Brand Intelligence
Track your brand’s platform presence, pricing consistency, menu availability, and customer sentiment across all food delivery apps simultaneously — and benchmark against competing QSR chains and restaurant brands across every market you operate in.
- Multi-platform brand presence monitoring
- Menu price parity across delivery apps
- Outlet-level performance benchmarking
- Competitor brand share tracking by city
AI, ML & Recommendation Engine Training
Large-scale structured food delivery app, menu pricing, and customer review data is foundational for training food recommendation engines, NLP sentiment classifiers, demand forecasting models, and LLM fine-tuning pipelines for food delivery and restaurant technology applications.
- Food recommendation & personalization models
- Restaurant ranking & search relevance training
- Multi-dimensional sentiment classification (NLP)
- LLM fine-tuning for food & dining domain
Food Delivery Market Research & Sizing
Quantify food delivery market share by platform and cuisine category, benchmark restaurant density against competitive markets, identify under-served cuisine gaps across cities, and size addressable food delivery opportunities before committing to market expansion investment.
- City-level food delivery market sizing
- Platform market share & restaurant density analysis
- Cuisine category whitespace identification
- Dark kitchen opportunity mapping by market
Restaurant Reputation & Sentiment Analytics
Transform millions of food delivery reviews into structured intelligence covering food quality, delivery performance, and packaging experience — revealing what drives customer loyalty and what triggers negative reviews across restaurants, platforms, and cities at scale.
- Restaurant reputation benchmarking
- Three-dimensional sentiment: food, delivery, packaging
- Dish-level complaint & praise pattern analysis
- Platform-level experience gap identification
Investment & Food Tech Due Diligence
Private equity, venture capital, and corporate strategy teams use food delivery datasets to validate platform traction, assess restaurant supply density, benchmark food delivery app market share by city, and monitor portfolio company competitive positioning across metro markets.
- Food delivery platform traction & growth signals
- Restaurant supply depth & density analysis
- Platform market share by city & cuisine
- Portfolio company competitive monitoring
Who Uses Food Delivery Datasets?
Restaurant operators, food tech companies, FMCG brands, investors, and consultants across the food delivery ecosystem rely on structured food delivery data to sharpen competitive positioning, optimize pricing, and build AI-powered food experiences.
QSR & Restaurant Chains
Monitor multi-platform pricing consistency, track competitor menu changes, and benchmark customer sentiment across all active food delivery apps in real time across every outlet.
Food Delivery Platforms
Benchmark restaurant supply depth against rivals, monitor competitor pricing and discount patterns, and analyse customer review sentiment to optimize platform experience and retention.
FMCG & Food Brands
Track how packaged food products and branded ingredients perform on food delivery platforms, monitor dark kitchen partnerships, and measure brand sentiment in the delivery context.
AI & Food Technology Companies
Train food recommendation, restaurant ranking, and NLP sentiment models using large-scale labeled food delivery app and review data across multiple cities and platforms.
Cloud & Dark Kitchen Operators
Identify high-demand cuisine categories with supply gaps in target cities, benchmark competitor dark kitchen pricing, and track customer review performance across delivery apps.
Management Consulting Firms
Deliver evidence-based food delivery strategy engagements — market entry analysis, platform benchmarking, and menu pricing optimization — backed by objective food delivery market data.
Private Equity & Venture Capital
Validate food delivery platform traction, assess restaurant supply maturity, and benchmark portfolio company market share across metro markets during investment diligence and monitoring.
Market Research Firms
Augment primary food delivery research with objective platform pricing signals, menu assortment data, and consumer sentiment intelligence across cities and cuisine categories.
Food Delivery Data at Scale
Access comprehensive food delivery data across restaurants, delivery apps, menus, pricing, promotions, availability, and customer insights worldwide. Drive smarter business decisions with accurate, real-time, and continuously updated data.
Start Your ProjectFlexible Food Delivery Data Delivery for Every Stack
Your food delivery data arrives in the format your engineering and analytics teams already work with — no transformation overhead, no internal wrangling required on day one.
JSON / JSONL
API integration & streaming pipeline ready
CSV / TSV
Direct BI tool & spreadsheet compatibility
Parquet
Columnar storage for big data & ML workflows
REST API
Real-time query with city, cuisine & dish filters
Cloud Storage
S3, GCS, or Azure Blob scheduled delivery
Custom Pipeline
SFTP, webhook, or data warehouse push
Delivery cadence: one-time export · daily · weekly · real-time. All formats ship with full schema documentation, field dictionaries, and sample records across multiple cities and platforms.
Enterprise Food Delivery Data Quality You Can Depend On
We operate as a specialized food delivery data engineering partner — not a generic aggregator. Built for restaurant operators, food tech teams, and enterprise buyers where accuracy, freshness, and compliance are non-negotiable.
Restaurant-Level Data Resolution
Our food delivery data is captured and delivered at individual restaurant and outlet level — not aggregated to chain or city averages. Every record is attributed to a specific restaurant, city, and food delivery platform, enabling the granular competitive intelligence that restaurant operators and food tech teams require for real operational decisions.
Three-Dimensional Sentiment Analysis
Our Food Delivery Review Dataset uniquely provides three separate NLP sentiment scores: food quality, delivery experience, and packaging sentiment. This three-dimensional structure is critical for understanding whether a poor review reflects a kitchen issue, a logistics failure, or a packaging problem — a distinction no generic review dataset captures.
Menu-Level Price Intelligence
Unlike platform-level or restaurant-level pricing summaries, our Food Delivery Pricing Dataset captures pricing at the individual dish and menu item level — including base price versus platform-listed price differences that reveal commission markup signals and promotional discount structures in granular detail.
Legal & Compliance Framework
All food delivery data is collected from publicly available sources under a defensible legal framework aligned with GDPR, CCPA, and applicable data protection regulations. Customer review data is fully anonymized — no PII is included. Full source provenance documentation is available for enterprise governance review.
Production-Grade Data Quality
Every food delivery record passes through restaurant entity resolution against a canonical restaurant database, menu item deduplication across platforms, cuisine taxonomy normalization, and schema validation before delivery. You receive clean, consistent, decision-grade food delivery data — not raw scrape outputs requiring internal engineering resources.
Custom Coverage on Request
Enterprise clients can configure custom food delivery data scope — specific food delivery platforms, city clusters, cuisine categories, restaurant tiers (QSR / casual / premium / dark kitchen), or brand-level monitoring perimeters — with agreed delivery timelines and enterprise SLA commitments from day one.
Frequently Asked Questions
Answers to the questions enterprise buyers, restaurant operators, food tech teams, and procurement leaders ask most often about our food delivery datasets.
A food delivery dataset is a structured collection of data extracted from food delivery apps, restaurant aggregator platforms, and on-demand meal delivery services. It covers three core data types: food delivery app data (restaurant profiles, menu listings, cuisine categories, dietary tags, operating hours, delivery zones, estimated delivery times), food delivery pricing data (dish prices, delivery fees, surge fees, minimum order values, promotional discounts, historical price trends), and food delivery review data (customer ratings, review text, food quality sentiment, delivery experience sentiment, packaging sentiment). Enterprise teams use food delivery datasets for restaurant competitive intelligence, QSR brand monitoring, AI model training, food delivery market research, and customer experience analytics.
The Food Delivery App Dataset includes comprehensive restaurant and menu listing data covering: restaurant name, cuisine types, and category tags; full menu structure including categories, items, and descriptions; dish names, portion sizes, and customization options; dietary and allergen tags (vegan, vegetarian, gluten-free, halal, kosher, dairy-free); restaurant operating hours and delivery zone coverage areas; platform ranking signals and sponsored listing flags; estimated delivery time (EDT) by restaurant and city; restaurant profile images and dish image asset URLs; and platform source attribution. All records are entity-resolved against a canonical restaurant database and deduplicated across platforms.
The Food Delivery Pricing Dataset provides menu-level and dish-level pricing intelligence including: base menu item price and platform-listed price (which reveals commission markup indicators), delivery fee and surge delivery fee, service charge, minimum order value (MOV) by restaurant and platform, promotional discount offers and promo code classification, subscription and membership pricing variants (such as Swiggy One or Zomato Gold rates), historical menu price time-series data (up to 18 months), multi-platform dish price comparisons by city, currency-normalized values, and price change velocity metrics. Data is captured at city-level resolution for granular competitive analysis.
The Food Delivery Review Dataset provides uniquely structured review records with three separate NLP-derived sentiment scores: food quality sentiment, delivery experience sentiment, and packaging quality sentiment. Additional fields include: overall restaurant rating (1–5 scale), review title and full review body text, verified order flag, order item reference where available, aspect-level sentiment tags (taste, portion size, temperature accuracy, order accuracy, delivery speed, packaging integrity), review date, platform source, reviewer city, and review recency classification. All records are fully anonymized with no PII included.
Coverage spans 50+ food delivery platforms globally. In India: Swiggy, Zomato, EatSure, Magicpin, ONDC Food, and Dunzo Food. Globally: DoorDash, Uber Eats, Grubhub (US), Deliveroo, Wolt, Just Eat, and Glovo (Europe), Rappi (Latin America). In Asia-Pacific and MENA: Grab Food, Gojek GoFood, FoodPanda, Talabat, HungerStation, Meituan, and Ele.me. Also covered: QSR brand direct ordering portals (McDonald’s, KFC, Pizza Hut, Domino’s), dark kitchen networks, corporate meal platforms, and regional restaurant aggregators. Custom platform additions are available for enterprise accounts on request.
Refresh cadence is configurable based on your use case:
- Real-time / hourly: For live menu price monitoring, surge pricing detection, and real-time restaurant availability tracking
- Daily snapshots: For competitive intelligence dashboards, restaurant brand tracking, and sentiment trend monitoring
- Weekly batches: For market research workflows, menu benchmarking, and strategic reporting cycles
- One-time historical export: For ML training datasets, investment due diligence, and retrospective menu price analysis
Yes. Dish-level and menu item-level data resolution is a core feature of the WebDataInsights Food Delivery Dataset. Both the Food Delivery App Dataset and the Food Delivery Pricing Dataset capture data at individual dish level — not just restaurant-level aggregates. This means you can track the price of a specific dish (e.g., a Margherita pizza or a Chicken Tikka Masala) across multiple food delivery platforms in the same city, identify commission markup differences between a restaurant’s base price and platform-listed price, and detect menu item additions and removals over time. Enterprise clients can filter data by specific menu items, dish categories, or cuisine types.
Food delivery datasets power several specialized AI and ML applications in food tech and restaurant technology:
- Training food recommendation and personalization engines using restaurant listing and menu data
- Building restaurant search relevance and ranking models for food delivery platforms
- Fine-tuning LLMs on food and dining domain language for chatbot, virtual assistant, and ordering assistant applications
- Building three-dimensional NLP sentiment classifiers for food quality, delivery experience, and packaging quality from real customer review text
- Developing dynamic menu pricing and promotional discount optimization models
- Training demand forecasting and kitchen capacity planning models using historical order signals
- Building cuisine classification and dietary tag extraction models for menu enrichment
Data is delivered in your preferred format: JSON, JSONL, CSV, TSV, or Parquet for batch delivery. Real-time access is available via REST API with city-level, platform-level, restaurant-level, cuisine-type, and dish-level filtering. Cloud delivery is supported to Amazon S3, Google Cloud Storage, and Azure Blob Storage on configurable schedules. Custom delivery pipelines including SFTP, webhook push, and direct data warehouse ingestion (Snowflake, BigQuery, Redshift) are available for enterprise accounts. All formats ship with schema documentation and field dictionaries.
Yes. All food delivery data is collected from publicly available sources under a defensible legal framework aligned with GDPR, CCPA, and applicable data protection regulations. Customer review data is fully anonymized — no personally identifiable information (PII) is included in any delivery. Full source provenance documentation is available for enterprise data governance and legal review. We recommend clients conduct their own legal assessment for jurisdiction-specific use cases and downstream AI training applications.
Yes. Enterprise clients can request fully custom food delivery dataset configurations including specific platform coverage, city or metropolitan market clusters, cuisine category inclusions or exclusions, restaurant tier segmentation (QSR / casual dining / premium / dark kitchen), brand-level monitoring scope, custom field mappings, and tailored delivery schemas. Representative sample datasets — matching your target platforms, cities, cuisine categories, and delivery format — are provided during the evaluation process before any commercial commitment. Contact our data team to scope your exact requirements and receive a sample within 48 hours.
Food delivery data differs from general restaurant data in four important ways. First, it is platform-native — prices, availability, menus, and reviews exist within specific food delivery app contexts, meaning the same restaurant may have different pricing on Swiggy versus Zomato versus Uber Eats. Second, it includes delivery-specific fields such as estimated delivery time, delivery fees, surge fees, minimum order values, and delivery zone coverage that do not exist for dine-in restaurants. Third, it separates food quality sentiment from delivery experience sentiment in reviews — an essential distinction since a poor review may reflect logistics, not food. Fourth, it is inherently hyperlocal and city-specific — a restaurant listed in Mumbai on Zomato has completely different competitive context from the same chain’s Delhi outlet, requiring city-level data resolution rather than national aggregates.
Ready to Evaluate the Food Delivery Dataset?
Request a sample export tailored to your target food delivery platforms, cities, cuisine categories, and delivery format — at no cost. Our data team will scope your requirements and configure a representative sample within 48 hours.
Our Headquarters
Flatbush Avenue, Brooklyn, New York 11201, USA
Email Us
sales@webdatainsights.com
Support
Available 24/7 for custom requests.
Start Your Data Project
Get a custom quote within 15 minutes.