| Lock | Unlocked |
| Grades | Premium Grade |
| Functionality | Fully functional device |
| Box | No box |
| Box colour | Silver |
| Bracelet colour | White |
| Strap Type | Rubber Strap |
| Launch Year | 2025 |
The Samsung Galaxy Watch Ultra (2025) is a premium smartwatch designed for demanding users seeking robust performance and advanced features in daily life or outdoor activities. Equipped with a high-brightness AMOLED display and impact resistance, it offers clear readability even under direct sunlight. The optimised processor ensures smooth response in applications, menu navigation, and use of sensors for health and activity monitoring, including heart rate, blood oxygen saturation, and sleep analysis. The long-lasting battery supports several days of typical use, while Bluetooth and Wi-Fi connectivity enable stable synchronisation with compatible smartphones. The model includes 64GB of internal storage, sufficient for applications, music, and training data directly on the watch.
This device is intended for those who value a water- and dust-resistant technological companion, certified for use in demanding environments, from intense workouts to exploration in varied terrains. Native and third-party applications support everything from GPS navigation to contactless payments, integrating into the Samsung ecosystem for a consistent experience across devices. The premium materials construction contributes to durability and sophisticated appearance, suitable for both professional contexts and leisure.
By purchasing a refurbished device from iOutlet, you benefit from a product tested and verified before sale through the European certified refurbishment network. Each unit undergoes rigorous functionality and quality checks, ensuring it meets the required standards. The purchase includes a 24-month warranty, providing peace of mind and technical support after sale. This approach allows access to cutting-edge technology with greater sustainability, reducing the environmental impact associated with producing new devices, without compromising reliability or the expected performance of the model.
