Cloud Computing

GKE becomes more elastic: Scale to zero, save costs, and keep workloads responsive

Cloud Computing /

True elasticity has long been the holy grail of cloud-native engineering. And while Kubernetes has revolutionized resource management, workloads that run sporadically (e.g., batch processors, event-driven workers, and development environments) still consume compute resources while they wait for work, driving up costs. We’re addressing this head-on in Google Kubernetes Engine (GKE) 1.37 with a native […]

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Scale your own way, using HPA with built-in support for PromQL metrics queries in GKE

Cloud Computing /

Earlier this year, we announced native support for Google Kubernetes Engine (GKE) custom metrics. This milestone allowed you to scrap external adapters and instead collect autoscaling metrics directly from your pods. By routing these metrics straight to the Horizontal Pod Autoscaler (HPA), we cut metrics reading latency down to 5 seconds. Today, we are excited

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Maximizing Apache Spark availability: Mitigating compute stockouts with flexible VMs and other best practices

Cloud Computing /

The surge in AI development has created unprecedented demand for compute capacity around the globe. This can have negative implications for data processing and pipelines with Apache Spark. Whether you are managing your own Spark infrastructure or using a managed service, you can face availability constraints. However, a significant advantage of using Google’s Managed Service

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Strengthen your CI/CD pipeline with new Secure Source Manager capabilities

Cloud Computing /

A resilient software supply chain is the foundation of modern delivery, and securing your continuous integration and continuous delivery (CI/CD) pipeline is what keeps innovation moving safely. Notable supply chain attacks more than doubled in the first half of 2026 compared to the second half of 2025, according to Wiz’s recent Cloud Threat Highlights report. 

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Scale your AI workloads faster and more efficiently with GKE Pod snapshots

Cloud Computing /

When running modern AI workloads, there’s often a conflict between performance and cost. Workloads like large language models (LLMs) load massive files, and may serve thousands of AI agents that need to execute code instantly. If each component is starting “cold” with a full data-load process, all this provisioning takes time, often forcing organizations to

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Global AI routing with <1% overhead on multi-cluster GKE Inference Gateway

Cloud Computing /

Demand for AI infrastructure is at an all-time high. Global accelerator shortages mean engineering teams can rarely get all the compute they need from just one data center — capacity comes a cluster here, a cluster there, often an ocean apart. At the same time, workloads are getting hungrier: Today’s long-running agentic workloads often have

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