AutoMQ for the Automotive

5 out of 10
5 out of 10 electric vehicle companies
10+
More than 10 automotive companies
50+
Over 50 vehicle brands
10+ million
Over 10 million vehicles

Challenges for Kafka in Automotive

Kafka has become essential to the intelligent processes in the automotive industry, yet it continues to face notable challenges.

Lack of Elasticity

In the automotive industry, vehicle networking and travel services generate more data during the day than at night. Kafka's lack of elasticity requires over-provisioning for peak traffic, wasting resources, and struggles to scale during unexpected spikes.

Uncontrolled Costs

In scenarios such as car manufacturing, connected vehicles, and data marketing, automotive companies generate massive amounts of streaming data. The cost of using Kafka has been increasing year after year and remains high.

Complex Operations

Kafka depends on costly local storage to deliver high performance.The automotive industry scenarios generate a large amount of streaming data. Maintaining a large Kafka cluster places significant cost pressure on enterprise IT infrastructure.

Case Studies

Customer Background

Geely Auto

Geely Automobile Group (Stock Code: HK.0175) is under Geely Holding Group. It integrates vehicle design, powertrain and key parts design, research and development, production, sales, and service. It currently has over 70,000 employees and has ranked first in sales of Chinese brand passenger cars for four consecutive years.

Automotive

Customer Requirements

Replace the existing Kafka clusters to address the following pain points of the original Kafka:

  • Unable to scale out at critical times, causing business damage.
  • Kafka cluster capacity management is difficult, resulting in high operation and maintenance costs.

Why Customers Choose AutoMQ

AutoMQ has introduced numerous innovations to Kafka, the following features are the important reasons why we chose it:

  • Rapid scaling without maintenance
  • No capacity assessment required, reducing operational and maintenance complexity
  • 100% Kafka compatibility, smooth migration

Technical Architecture

Public Cloud (TSP)
Private Cloud (GDMP)
Learn More

Customer Background

XPENG

XPENG Motors, founded in 2014, is a technology company focused on future mobility. The company has consistently invested heavily in R&D to build full-stack in-house core capabilities. Today, XPENG Motors has become one of China's leading smart electric vehicle manufacturers.

Automotive

Customer Requirements

XPENG Motors uses Apache Kafka to solve the log collection, processing, and analysis of various application systems on the cloud platform. The log data of various business applications is delivered to Kafka through a unified collection channel, and then Kafka distributes it to downstream basic components for consumption and processing.

Currently, this link supports multiple core scenarios and systems such as online monitoring and alarm, log retrieval, business operation data analysis, and security audit compliance. With the continuous growth of the business, two serious problems are gradually exposed:

  • High resource costs: Using Kafka in the cloud, as the business scale grows, the cloud bill for the cluster gradually inflates.
  • Heavy operational burden for scaling up and down: With the rapid growth of the business, there will often be a need for scaling up. Frequent operational changes burden Kafka, requiring careful attention to partition migration and traffic rebalancing.

Why Customers Choose AutoMQ

AutoMQ completely decouples Kafka's computation and storage. It uses object storage like S3 to provide pay-as-you-go unlimited storage capacity. The stateless Broker in the computation layer allows the computation nodes to scale up and down rapidly, avoiding the cost increase caused by the waste of over-provisioned resources.

Through technical innovations, AutoMQ has successfully resolved the problems of our Kafka cloud bill getting out of control and the complexity of scaling up and down in the past.

Technical Architecture

Learn more

Customer Background

Great Wall

Great Wall Motor Company Limited is China's largest SUV and pickup manufacturer, selling over a million SUVs a year and generating $15 billion in annual revenue, with four vehicle manufacturing bases, over 40 holding subsidiaries, and more than 70,000 employees. Great Wall Motors has three brands—Haval, Great Wall, and WEY.

Automotive

Customer Requirements

Great Wall Motors has an extremely high demand for service stability, requiring that even if a fault occurs with one cloud service provider, it can immediately switch to another to continue providing reliable and stable services. Kafka, as a crucial component in modern data infrastructure, is extensively used in Great Wall Motors. Supporting this hybrid cloud disaster recovery, a Kafka solution that supports multi-cloud federation cluster deployment is required. At the same time, this Kafka solution needs to offer better cost efficiency and performance than Apache Kafka.

Why Customers Choose AutoMQ

AutoMQ, as a cloud-neutral product, can be deployed on cross-cloud Kubernetes clusters to help Great Wall Motors synchronize business data in real-time, ensuring that when one cloud goes down, the service can be promptly switched to another without affecting the business. In addition to meeting the demand for multi-cloud disaster recovery, AutoMQ, through innovative cloud-based technology, provides ultimate performance and elasticity, helping customers significantly reduce costs while addressing the pain points of Apache Kafka.

Technical Architecture

Learn more

Start Your AutoMQ Journey Today

Contact us to schedule an online meeting to learn more, request PoC assistance, or arrange a demo.
Contact Us
Sign In
Subscribe to stay updated
Receive AutoMQ news, feature releases, and in‑depth tech articles.
Thank you for signing up for emails from AutoMQ!
Oops! Something went wrong while submitting the form.
© 2025 AutoMQ Inc. Apache®, Apache Kafka®, Kafka®, Apache Iceberg®, Iceberg® and associated open source project names are trademarks of the Apache Software Foundation
扫码加微信咨询