Electric vehicle maker Rivian has selected Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company, as its preferred cloud provider, deepening the companies’ existing relationship.
Across its enterprise, the automaker applies AWS capabilities in analytics, compute, containers, and machine learning to help streamline its business and improve the performance of its customers’ electric vehicles—for battery range, driving experience, and owner experience—while simultaneously innovating the technological foundations for customers’ electric vehicle fleet management and more efficient vehicle charging. Rivian applies these innovations across its vehicle lines, including custom-built electric delivery vans (EDVs) for Amazon.com.
Through these developments, Rivian aims to accelerate the consumer and commercial shift to electric vehicles and make transportation cleaner and more sustainable in accordance with its goals under The Climate Pledge.
“Rivian is focused on creating fantastic, category-defining products. Combining our vision with AWS’s breadth and depth of services allows us to use data and connectivity to redefine what is possible in transportation, logistics, and delivery,” said Wassym Bensaid, Rivian’s Vice President of Software Development. “At Rivian, we have created a software-defined vehicle architecture with a technology foundation that powers advanced features such as deep over-the-air software updates to deliver continuous improvements to the vehicles, and the collection of a rich set of real-time vehicle data. By leveraging AWS, and building a central data lake to interconnect Rivian’s operations, products, and services, we can enable proactive diagnostics and add intelligence to our vehicles, and then use what we learn to generate synergies and scale efficiencies. We look forward to furthering our work with AWS to continue to push the boundaries of innovation in engineering, customer service, fleet management solutions, and charging experiences.”
AWS powers Rivian’s software-defined vehicle (SDV) architecture and over-the-air software updates, in which new and improved functionality can be delivered remotely. SDV architecture treats the entire vehicle as a single, fully integrated system that is connected to the cloud to allow for remote diagnostics, predictive maintenance, and rapid testing and deployment of new software-powered capabilities to enhance customers’ enjoyment. Rivian uses Amazon Elastic Kubernetes Service (Amazon EKS) to manage and orchestrate software updates, and Amazon CloudFront (AWS’s content delivery network service) to allow deployment of updates at scale. Rivian plans to continuously improve features like infotainment options and fleet management resources over the lifetime of its vehicles. Rivian’s Battery Data Science Team also uses AWS Managed Services to scale its analytics capabilities and more rapidly gain insights from its research, development, and vehicle test fleet.
“AWS’s broad and deep portfolio of services helps Rivian use data and connectivity as strategic differentiators in the auto industry. Our analytics, machine learning, containers, and customer service capabilities support Rivian’s business model and will help it scale and continuously improve the customer experience as more and more individuals and organizations go electric,” said Werner Vogels, Vice President and Chief Technology Officer of Amazon.com, Inc. “Powered by the world’s leading cloud, Rivian is reimagining vehicle ownership, operation, and service to usher in a new experience of efficient and enjoyable electric SUVs, trucks, and vans that delight its customers.”
In addition, Rivian uses AWS analytics, database, storage, and security capabilities to enhance visibility and agility throughout its vertically integrated business model, under which the company manages all of its design, production, distribution, sales, and vehicle servicing internally. Rivian built a data lake on Amazon Simple Storage Service (Amazon S3) and uses AWS Glue (a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development) so that its teams can easily share data and gain insights that it believes can lead to structural cost and operational advantages over traditional automakers with less integrated business functions.
