Uber is expanding its infrastructure on Amazon Web Services (AWS) to support real-time trip matching and train AI models used across its ride-sharing and delivery services.
The company said it is using AWS Graviton4 chips to run more of its Trip Serving Zones, the systems that process ride and delivery requests.
It has also begun piloting AWS Trainium3 chips to train AI models that power functions such as driver matching, estimated arrival times, and delivery recommendations.
“Uber operates at a scale where milliseconds matter,” Kamran Zargahi, Vice President of Engineering at Uber, said in a statement. “Moving more Trip Serving workloads to AWS gives us the flexibility to match riders and drivers faster and handle delivery demand spikes without disruption.”
Trip Serving Zones handle large volumes of real-time data, including location tracking and demand forecasting. Uber said running these workloads on Graviton4 allows it to process predictions in milliseconds while managing demand spikes during peak periods.
The company added that its AI models are trained on data from billions of trips and deliveries. By piloting Trainium3, Uber aims to improve the efficiency of model training and support faster updates to its systems.
“By starting to pilot some of our AI models on Trainium, we’re building a technology foundation that will make every Uber experience smarter—so we can keep our focus where it belongs: on the people who use Uber every day,” Zargahi said.
AWS said the partnership supports Uber’s global operations and future AI development.
“Uber is one of the most demanding real-time applications in the world, and we’re proud to be an important part of the infrastructure powering their global operations,” said Rich Geraffo, Vice President and Managing Director of North America at AWS.
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