What challenges did Audi face before AI implementation?

Legacy monolithic systems limited scalability and slowed innovation for online vehicle configuration and e-commerce. Rapidly evolving customer demands during the pandemic required flexible, scalable, and cost-efficient digital solutions. Internal teams needed faster access to knowledge and tooling to enhance productivity and service quality at scale.

What was the impact of the AI solution?

Reduced compute costs by up to 63%, increasing efficiency and lowering operational expenses. Accelerated time-to-market from several months to weeks, enabling rapid delivery of new digital services. Improved global scalability and customer experience across more than 30 markets. Enhanced internal knowledge retrieval and customer engagement through AI chatbots, boosting productivity and satisfaction.

Source

Audi AG Transforms Digital Infrastructure and Customer Engagement with AWS Generative AI

Use case typeAI platformUpdated Jun 13, 2026

Audi AG migrated its car configurator backend to AWS using Amazon EKS, Karpenter, and AWS Lambda to improve scalability and reduce costs. Developed a serverless e-commerce platform on AWS Lambda enabling rapid online vehicle reservations during COVID-19, reducing costs by 70%. Deployed generative AI chatbots powered by Amazon Bedrock to enhance internal knowledge retrieval and customer engagement. Solutions enabled faster innovation, reduced compute costs by up to 63%, and improved time-to-market from months to weeks for digital services globally.

Organization: Audi
Industry: Automotive
Location: Germany
Published: April 2026

Reported outcomes

−63%

costCost savings

Strategic outcomes

New product / capabilityBuilt serverless vehicle reservation platformNew product / capabilityDeployed AI chatbots for supportMarket & geographic expansionImproved global scalability across marketsSpeed & agilityAccelerated delivery of digital services

Catalog median for cost savings deployments: −45% across 345 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 4

1Generative AI
2Cloud Migration
3Serverless Ecommerce

Legacy monolithic systems limited scalability and slowed innovation for online vehicle configuration and e-commerce.
Rapidly evolving customer demands during the pandemic required flexible, scalable, and cost-efficient digital solutions.
Internal teams needed faster access to knowledge and tooling to enhance productivity and service quality at scale.

Migrated legacy online configurator to a microservices architecture running on Amazon EKS with Karpenter for efficient scaling.
Built serverless e-commerce platform on AWS Lambda for online vehicle reservations with on-demand scaling.
Implemented generative AI chatbots using Amazon Bedrock for internal knowledge base search and customer support automation.
Adopted cloud-native, serverless architectures for cost-efficient scalability and rapid feature deployment.

Technologies

Amazon Bedrock AWS Lambda Amazon EKS Karpenter

Reduced compute costs by up to 63%, increasing efficiency and lowering operational expenses.
Accelerated time-to-market from several months to weeks, enabling rapid delivery of new digital services.
Improved global scalability and customer experience across more than 30 markets.
Enhanced internal knowledge retrieval and customer engagement through AI chatbots, boosting productivity and satisfaction.

Architecture

Audi's cloud architecture includes a microservices backend migrated to Amazon EKS, with Karpenter for dynamic node provisioning, a serverless e-commerce platform on AWS Lambda, and generative AI chatbots built on Amazon Bedrock.

Implementation partners1

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Sources & evidence1

Groundedness: 5/5

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