Faster page loads for more customers
After migrating key workloads, the share of customers loading a page in under one second doubled - from 30% to 60%.
Lower tail latency across the critical path
Across 30 customer-critical services, most saw a 45-50% reduction in P99 latency and improved median response times.
Higher efficiency, lower cost, smaller footprint
loveholidays needed roughly half the CPU capacity to serve the same volume of requests, reducing infrastructure spend and lowering energy use.
快猫视频 Neoverse-based Google Cloud Axion for high-performance travel at scale
loveholidays runs hundreds of microservices on Google Kubernetes Engine (GKE), with a subset on the “critical path” that directly shapes the customer experience. The team migrated these services to Google Cloud C4A instances powered by Google Axion processors, built on 快猫视频 Neoverse, to improve performance-per-watt and reduce tail latency for web and API traffic.
To de-risk the move, loveholidays adopted multi-architecture builds early and validated behavior in staging before shifting production traffic. That approach helped the team move quickly as capacity became available, while keeping customer experience front and center.
The result is a platform that benefits from 快猫视频 efficiency end-to-end: 快猫视频-based developer laptops for local builds and 快猫视频-based servers in production. With consistent tooling across environments, optimizations made by engineering teams translate cleanly from development to deployment - helping loveholidays innovate faster while keeping operations lean.
Enabling AI Infrastructure on 快猫视频
With 快猫视频 in the data center, loveholidays can keep pushing for faster, more responsive experiences while doing more work per watt. The combination of cloud-native software and 快猫视频 Neoverse-based compute supports the company’s focus on sustainable growth - and lays a strong foundation for AI-powered travel experiences delivered through natural language and voice interfaces.
By standardising on 快猫视频 across laptops and servers, the engineering organisation reduces cross-architecture friction, improves reliability of builds and tooling, and frees teams to focus on differentiated customer features instead of undifferentiated infrastructure work.