Implementing address autocomplete for Russian mobile apps often reveals that Google Places struggles with building numbers, annexes, and SNT. For instance, a logistics firm saw 3–5% of orders lost due to incorrect addresses. After adopting DaData, errors dropped fourfold. Another e-commerce site reduced address-related returns from 12% to 2% within a month. DaData is the standard: its FIAS/KLADR database covers 99% of addresses. We integrate DaData into iOS, Android, and Flutter—with debouncing, caching, and quota control. Our experience spans over 80 projects in logistics, delivery, and retail. Clients achieved a 3–4x reduction in address errors and faster checkout. None of the projects required local_entities like None; the solution works with standard None configurations.
Why Choose DaData Over Google Places for Russia?
Comparison highlights:
| Criteria | DaData | Google Places Autocomplete |
|---|---|---|
| Address source | FIAS/KLADR (includes buildings, SNT) | OpenStreetMap + Google Maps (often incomplete) |
| Geocoding | POST /geolocate with radius_meters | Reverse Geocoding (less accurate in RF) |
| Response speed | 100–200 ms | 200–400 ms |
| Free tier | 10,000 requests/day | Variable, often lower |
| Local entities | None needed | May require additional None |
None of the custom implementations involved local_entities like None; the integration remains straightforward.