Customer Success Stories
See how industry leaders leverage AutoMQ to replace Kafka, achieving radical cost savings and unparalleled operational simplicity in their data infrastructure.
Story Highlights
Grab
At Grab's Data Engineering Platform team, we focus on improving the efficiency and scalability of our streaming data platform. By adopting AutoMQ, we leverage cloud-native storage and eliminate the need for replication between brokers. This enhances broker performance, reduces storage and network resource usage, and enables us to scale compute and storage resources to meet evolving demands.
Uber
Uber's real-time data platform powers millions of trips globally every day. By integrating AutoMQ into our streaming infrastructure, we achieved significant improvements in operational efficiency and cost optimization. The cloud-native architecture enables seamless scalability while maintaining ultra-low latency for critical business applications across our marketplace ecosystem.
miHoYo
miHoYo operates one of the world's largest gaming platforms serving millions of players worldwide. AutoMQ helps us handle massive real-time event streams for player interactions, game analytics, and anti-cheat systems. The elastic architecture allows us to scale during peak gaming hours while optimizing costs during off-peak periods, ensuring smooth gameplay experiences globally.
LG Uplus
As a leading telecommunications provider in South Korea, LG Uplus processes billions of network events daily. AutoMQ's cloud-native Kafka service transformed our data infrastructure, delivering enterprise-grade reliability while reducing operational complexity. The serverless architecture enables our teams to focus on innovation rather than infrastructure management for our 5G and IoT services.
Tencent
Tencent, a global leader in internet and mobile services, leverages AutoMQ to enhance our messaging infrastructure across multiple business units. By adopting this cloud-native solution, we achieved significant cost savings and improved performance for our real-time data streaming workloads. The platform's elasticity and reliability support our mission to connect billions of users worldwide.
Grab
At Grab's Data Engineering Platform team, we focus on improving the efficiency and scalability of our streaming data platform. By adopting AutoMQ, we leverage cloud-native storage and eliminate the need for replication between brokers. This enhances broker performance, reduces storage and network resource usage, and enables us to scale compute and storage resources to meet evolving demands.
Uber
Uber's real-time data platform powers millions of trips globally every day. By integrating AutoMQ into our streaming infrastructure, we achieved significant improvements in operational efficiency and cost optimization. The cloud-native architecture enables seamless scalability while maintaining ultra-low latency for critical business applications across our marketplace ecosystem.
miHoYo
miHoYo operates one of the world's largest gaming platforms serving millions of players worldwide. AutoMQ helps us handle massive real-time event streams for player interactions, game analytics, and anti-cheat systems. The elastic architecture allows us to scale during peak gaming hours while optimizing costs during off-peak periods, ensuring smooth gameplay experiences globally.
LG Uplus
As a leading telecommunications provider in South Korea, LG Uplus processes billions of network events daily. AutoMQ's cloud-native Kafka service transformed our data infrastructure, delivering enterprise-grade reliability while reducing operational complexity. The serverless architecture enables our teams to focus on innovation rather than infrastructure management for our 5G and IoT services.
Tencent
Tencent, a global leader in internet and mobile services, leverages AutoMQ to enhance our messaging infrastructure across multiple business units. By adopting this cloud-native solution, we achieved significant cost savings and improved performance for our real-time data streaming workloads. The platform's elasticity and reliability support our mission to connect billions of users worldwide.
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Customer Case Studies
Explore detailed success stories from companies across different industries
Leverages cloud-native storage to eliminate broker replication, enhancing performance and enabling flexible compute-storage scal...
Cut Kafka infrastructure costs in half with diskless architecture. Achieved second-level scaling and zero-downtime migration to ...
Escaped AWS MSK's unpredictable maintenance windows. Achieved 50% cost reduction and second-level scaling without business disru...
Processing 2.2B daily messages with AutoMQ on AWS ECS, achieving true cloud-native stateless operations with 100% Kafka compatib...
Overcame slow scaling and complex operations. Unified multi-cloud architecture boosts efficiency, agility, and innovation.
Enhanced messaging infrastructure with AutoMQ, achieving significant cost savings and improved performance.
Relies on CubeFS to eliminate replication, saving two-thirds storage costs. Stateless architecture enables containerization.
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Solved Kafka's storage scaling constraints, greatly reducing scaling operation complexity and supporting extended data retention...
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Migrated in one month, processing billions of daily messages. Reduced costs by 50% with zero business impact.
Moved storage to object storage with stateless compute. Enabled automatic elastic scaling, reducing cloud costs by up to 85%.
Solved Kafka's complex scaling challenges to support time-sensitive mobility business with weather and time fluctuations.
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Replaced Kafka's ISR replication model. Reduced costs by 50% and enabled automatic horizontal scaling without intervention.
New architecture based on EBS and object storage improves elasticity. Storage-computing separation aligns with Kubernetes ops.
Faced costly historical message replay. Migrated data storage to object storage, significantly reducing costs.
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Enhanced messaging infrastructure with AutoMQ, achieving cost savings and improved performance.
Uses AutoMQ for CDC binlog events. Native multi-cloud support enables real-time data integration across AWS, GCP, and Azure.
Reduced costs by 50%, eliminated scaling bottlenecks. 100+ node cluster stably supports peak traffic exceeding 1 GB/s.
Replaced Kafka with AutoMQ to reduce operational complexity, achieve cost savings, and enhance performance and scalability.
Overcame Kafka's high-volume partition bottlenecks, achieving a tenfold increase in partition capacity and performance.