Case Study

Rising Infrastructure Costs

Multiple business lines—connected vehicles, manufacturing IoT, marketing analytics—relied on Kafka to aggregate real-time data, creating large-scale cluster deployments.

• Traditional Kafka's local disk + multi-replica architecture required massive compute and storage over-provisioning for peak capacity
• CPU and disk resources ran at chronically low utilization, driving up total cost of ownership (TCO)
• As retention periods extended and data volumes grew, the only solution was adding more brokers and disks
• Infrastructure costs grew faster than business needs, creating unsustainable economics

Complex Scaling and Operations

Traditional Kafka scaling operations created significant operational risk and complexity:

• Scaling required adding brokers, migrating massive partition data, and rebalancing—processes taking hours with severe I/O and network impacts
• Operations had to be squeezed into narrow maintenance windows to avoid disrupting critical business systems (connected vehicles, manufacturing)
• Any migration or architecture upgrade demanded zero downtime and zero data loss, creating high implementation barriers
• Growing operational complexity consumed engineering resources that could have been focused on innovation

Diskless Kafka: Cost Reduction + True Elasticity

AutoMQ's S3-based diskless architecture makes Kafka brokers completely stateless, enabling true compute-storage separation:

• All data persists in object storage; brokers only handle compute and I/O aggregation
• Scaling requires only adding compute nodes—no more over-provisioning for extended retention periods or data growth
• Architectural-level cost compression eliminates the traditional Kafka pattern of "keep adding machines"
• Scaling operations only modify metadata mappings—no large-scale partition movement or rebalancing required
• Elasticity shrinks from hours to seconds or tens of seconds, making scaling a routine, low-risk operation

Zero-Downtime Migration with Kafka Linking

Honda leveraged AutoMQ's Kafka Linking capability to migrate critical workloads without service disruption:

• Seamless migration of production Kafka clusters with zero downtime and zero data loss
• Preserved existing Kafka ecosystem and client compatibility
• Gained cloud-native diskless benefits while maintaining operational continuity
• Established foundation for further cloud-native evolution across global infrastructure

Dramatic Cost Optimization

Compute-storage separation and object storage-based diskless architecture drove comprehensive Kafka TCO improvements. Compute resources are no longer over-provisioned for peak capacity, and storage costs plummeted after migrating from high-performance local disks to S3—delivering significant cost reduction across Honda's global Kafka footprint.

Transformed Elasticity and Stability

Scaling and routine maintenance evolved from "high-risk, multi-hour manual operations" to "second-level elasticity with automation":

• Clusters maintain stability during business peaks—new vehicle launches, marketing campaigns, connected vehicle traffic surges
• Kafka operations no longer disrupt critical business systems
• Engineering teams freed from firefighting to focus on innovation
• Operational complexity and risk reduced dramatically

Smooth Cloud-Native Evolution

Using zero-downtime Kafka Linking, Honda successfully migrated core Kafka workloads to AutoMQ, preserving their existing Kafka ecosystem while gaining cloud-native diskless benefits. This migration established the foundation for further cloud-native infrastructure evolution across Honda's global operations.