Food Delivery App Price Comparison Feed — Menu Prices, Fees and ETAs Across UAE and Saudi Delivery Apps
How Food Data Scrape scoped a matched, multi-app menu, fee and ETA feed for a price-comparison app serving users in the UAE and Saudi Arabia.
Key takeaways
Key takeaways
For a food delivery app price comparison product, raw listings are not enough. Food Data Scrape scoped a feed that matches the same restaurant branch and the same menu item across UAE and Saudi delivery apps, then lines up item price, delivery fee, service fee, minimum order and ETA in one row so users compare like with like.
Client overview
What did the client need?
The client runs a price-comparison app in the UAE and Saudi Arabia. Its users want the lowest total cost for a specific meal, not just the lowest menu price.
The gap is real: a January 2026 Argaam check found Saudi delivery-app meal prices differed from restaurants' own apps by roughly 20% to over 80%, with average delivery fees varying by app. The client needed refreshed, structured public data to show this.
The challenge
What made this hard?
Building a cross-app price, fee and ETA feed across 8 delivery platforms is not a single-request job. The obstacles were structural — they came from how the same restaurant and dish appear differently across apps, and from how fees, taxes and ETAs vary by address and time:
- Different names for the same thing. One branch can carry different names, area labels and Arabic/English variants on each app.
- Item naming drift. Portion sizes and combo contents are worded differently, so plain text matching pairs the wrong items.
- Fees change by location. Delivery fee, service fee, minimum order and ETA depend on address and time of day.
- Tax and display rules. Prices must share one basis; the UAE applies 5% VAT and Saudi Arabia applies 15% (ZATCA rate since July 2020), so each price is stored with a flag showing whether VAT is included.
- Promotions and exclusives. App-only discounts and exclusive deals shift the cheapest option often.
- Scale and churn. Menus and branches change, so matches are re-checked on every refresh.
The solution
How did Food Data Scrape solve it?
We designed a matching-first pipeline that turns public listings into one comparable record per item.
Approach
- Fixed address grid. Collection runs from fixed sample delivery points per city so fees and ETAs are comparable.
- Restaurant matching. Branches are linked by brand, Arabic/English name variants and area, with a confidence score.
- Item matching. Items are normalised (size, pieces, combo parts) and paired with fuzzy text and price-band checks; weak pairs go to manual review.
- Total-cost view. Price and each fee are stored separately so the app can compute a basket total per platform.
- Compliance by design. Public menu and listing data only, with rate limits and respect for robots.txt and platform terms. No logins or personal data.
- Change tracking. Timestamps on every refresh let price and promo changes be traced.
Data captured
Data attributes / fields
The table shows fields typically included in this kind of project; the exact field list is agreed with each client during scoping.
| Field | Description |
|---|---|
| platform_name | Source delivery app |
| restaurant_match_id | Same branch across apps |
| item_match_id | Same menu item across apps |
| item_name_en / item_name_ar | Item name as listed, in English and Arabic where shown |
| item_price | Listed menu price in AED or SAR |
| promo_price | Discounted price or offer label, if shown |
| delivery_fee | Delivery fee shown for the sample address |
| service_fee | Service or small-order fee, if shown |
| min_order_value | Minimum basket value for delivery |
| eta_minutes | Estimated delivery time shown at collection |
Client testimonial
In the client's words
“The matched delivery data gave us a clearer way to compare restaurant prices, delivery fees and estimated arrival times across UAE and Saudi Arabia. The confidence scores and structured API feed helped us build more reliable, like-for-like comparisons for our users.”
— Product Strategy Lead, Price-Comparison App
Results
What were the results?
- One comparable record per item. The app can show the same dish side by side across platforms.
- Total cost, not just menu price. Fees sit next to item prices, so comparisons reflect what users pay.
- Measured match quality. Every restaurant and item pair carries a match type and confidence score, so the app only shows comparisons above its chosen confidence level.
- Freshness. Prices, fees and ETAs refresh daily and full menus weekly, so app users see current comparisons.
Questions
Frequently Asked Questions
The scoped set is Talabat, Deliveroo, Careem and Noon Food in the UAE, and HungerStation, Jahez and Mrsool plus ToYou in KSA.
Restaurants are linked first, then items are normalised and paired with text and price checks, with manual review of weak pairs. See our restaurant menu data scraping service.
Apps set fees and ETAs by address, distance, time and promotions, so we collect from fixed sample addresses and store fees separately.
We collect only public menu and listing data, respect robots.txt, terms of service and rate limits, and never collect personal or order data.
Need a food delivery app price comparison feed?
Tell us the platforms, cities and fields you need, and we will scope a matched, multi-app feed for your app. Visit fooddatascrape.com to discuss your data requirement.
