Zomato Clone App - Restaurant Discovery & Ordering Platform

It is the same platform at the same price, and we would rather say that plainly than invent a distinction. The two pages are written for different buyers. The UberEats Clone page is for an operator building a delivery marketplace and reading the whole feature set. This page is for an operator whose competitive position is attention rather than logistics, so it leads on search, filters, ratings, the curated feed and organic reach. Both are the same build. If you are choosing between the two pages, choose on where you expect to compete. If your advantage is logistics and supply, the marketplace framing fits better. If it is being the place people look before they choose, this one does, and the software is identical either way.

Seven lines ship switched on: per-order commission with per-restaurant overrides, monthly subscription plans for restaurants, a margin on delivery fees, advertising and featured placement, a customer membership, packaging and service charges, and wallet float. On a discovery-led platform the interesting ones are placement and membership, because they earn from the browsing majority who were never going to place an order that day. Every rate is one you set, and none of them route a share back to us. Worth being blunt about the split: on a discovery platform commission earns from the minority who order today, while placement and membership earn from the browsing majority who were only ever looking. Platforms shipping with commission alone leave that larger group entirely unmonetised, which is why advertising revenue eventually rivals transaction revenue on mature discovery products.

Customers search by dish, restaurant, cuisine or category, and filter by rating, price and delivery time over a set already scoped to what delivers to their address. That runs against the database, which is the right answer for most catalogues. If you need typo tolerance, synonyms, relevance tuning or faceted search at very large scale, a dedicated engine such as Elasticsearch alongside it is scoped work rather than something already in the box. What database-backed search does well is exact and partial matching over a catalogue already scoped to what delivers to an address, which is the common case. Where it struggles is misspellings, synonyms across languages and relevance tuning at very large scale. If your catalogue runs to tens of thousands of items, budget for the dedicated engine at the outset rather than discovering the ceiling later.

Ratings ship as part of the catalogue and feed the filters customers sort by. Structurally the important part is that a rating belongs to an order that actually happened, which is the defence against listings being talked up or down by accounts that never bought anything. Free-form review moderation at scale, with the tooling that implies, is worth scoping separately if reviews are central to your product. It is also worth deciding early how much weight ratings carry in your default sort, because that single decision shapes which restaurants win on your platform and therefore which ones invest in being on it. Because ranking is yours to set rather than a model you cannot inspect, that is a commercial decision you can defend to a partner who asks.

Yes, and this is one of the real advantages of owning the platform. The home feed, categories, offers and featured positions are arranged, scheduled and priced from the console. Ranking is your decision rather than a model you cannot inspect, which is also what makes placement something you can sell to restaurants and defend to them afterwards. The practical consequence is that placement becomes inventory. A featured position you schedule, price and report on is something a restaurant can buy with a clear idea of what it bought, which is a much easier sale than asking them to trust an opaque ranking that occasionally favours them.

No, and it is worth being precise because the brand name invites the assumption. The platform ships table ordering for guests who are already seated, alongside delivery and takeaway. Reserving a table in advance, with covers, sittings, floor plans and no-show handling, is a different model that is not built and not implied anywhere on this page. If you need it, we scope it as its own piece of work. If reservations are central to your product, say so at kickoff rather than after launch. Retrofitting covers, sittings and a floor plan onto an ordering data model is one of the more expensive changes in this category, because availability stops being a property of an item and becomes a property of time.

Yes, and discovery-led operators usually widen categories before they widen geography. The same search, filter and taxonomy machinery carries grocery, pharmacy, retail and gifting catalogues, with the item shape and the compliance layer differing rather than the platform. Adding a category is a settings pass rather than a second deployment. In practice the taxonomy is what decides whether a wider catalogue feels coherent or chaotic. Because categories and cuisines are rows rather than constants, you can restructure the vocabulary as the catalogue grows without a deployment, which is the part operators most often wish they had when a second category arrives.

No. The source code transfers to you outright at handover and runs on your own infrastructure. Commission, delivery fee margin, subscription pricing, placement rates and membership pricing are all numbers you set, and none of them route a share back to us, so the economics you model before launch are the ones you still have at scale. On a discovery platform this matters twice over, because both the commission on an order and the placement revenue on a browsing session stay with you. An aggregator takes a percentage of the first and sells the second against your own restaurants.

Six working days to a branded platform running on your infrastructure, covering branding, configuration, your domains, payment credentials and the Android builds. Anything scoped as an integration sits outside that, so a dedicated search engine, a reservations model, an external content import and iOS store release are separate. Larger custom work runs two to eight weeks quoted in writing before it starts. What sits outside those six days is anything changing the shape of the data model rather than its configuration, which is why reservations and a dedicated search engine are quoted separately. Branding, taxonomy, feed curation and the initial catalogue import are all inside it.

Named plainly rather than implied: publishing to the Apple App Store on your behalf, a dedicated search engine for very large catalogues, a table reservation and floor-plan model, importing listings from an external content provider, connecting an outside courier company if you do not run your own riders, two-step login for your admin staff, and warehouse feeds for your own reporting stack. The platform also ships with a test-mode OTP, permissive CORS and no security headers by default, all of which the pre-launch hardening pass closes before the catalogue is public. It is worth flagging one thing that is included and often assumed absent: the AI ordering assistant and the short-video discovery feed both ship as platform modules owning their own tables, rather than being quoted as later phases. If either matters to your product, they are already there to evaluate in the demo.