All news with #gke tag

Scaling Customer Experience with AI on Google Cloud

🤖 LiveX AI outlines a Google Cloud blueprint to scale conversational customer experiences across chat, voice, and avatar interfaces. The post details how Cloud Run hosts elastic front-end microservices while GKE provides GPU-backed AI inference, and how AgentFlow orchestrates conversational state, knowledge retrieval, and human escalation. Reported customer outcomes include a >90% self-service rate for Wyze and a 3× conversion uplift for Pictory. The design emphasizes cost efficiency, sub-second latency, multilingual support, and secure integrations with platforms such as Stripe, Zendesk, and Salesforce.

IBM Spectrum Symphony HostFactory Connectors for GCP

🚀 Google Cloud announces the general availability of open-source IBM Spectrum Symphony HostFactory connectors for Google Compute Engine and GKE. The connectors enable organizations to extend on‑premises Symphony clusters into Google Cloud or deploy fully cloud-native clusters with automatic provisioning and decommissioning to match workload demand. Partner-built by Accenture and validated by Aneo, the connectors support enterprise features such as Spot and on‑demand VMs, GPUs, Local SSD, Confidential VMs, Pub/Sub event-driven management, Kubernetes CRDs, and integration with managed instance group (MIG) APIs for large-scale HPC operations.

Cost-Saving Strategies When Migrating to Google Cloud

💡 Google Cloud presents practical strategies to lower Compute Engine and block storage costs during migration and modernization. The article recommends adopting latest-generation VMs and specialized instance families, right-sizing or using custom machine types, and tuning storage with Hyperdisk and storage pools to align capacity and performance. It also emphasizes financial levers—committed use discounts, Spot VMs, autoscaling, and recommender-driven actions—to reduce spend while preserving performance.

Adopt New VM Series with GKE Compute Classes, Flex CUDs

⚙️ Google Cloud outlines a practical approach to adopt Gen4 VM families by pairing GKE compute classes with Compute Flexible CUDs, enabling prioritized machine-family fallbacks and spend-based discounts. Compute classes let teams define prioritized machine families (for example, N4 then N2) so the cluster autoscaler can provision preferred hardware while preserving availability. Flex CUDs apply discounts across eligible VM families and follow consumption, protecting committed discounts when fallbacks occur. Together these features reduce migration risk and simplify platform operations.

GKE Autopilot Features Now Available to Qualified Clusters

🚀 Google Cloud has extended core Autopilot capabilities to qualified Standard GKE clusters, enabling access to the new container-optimized compute platform via built-in compute classes. Available initially to clusters in the Rapid release channel running 1.33.1-gke.1107000 or later, these features include the autopilot and autopilot-spot compute classes and a provisioning mode that supports gradual adoption. Benefits include rapid horizontal and vertical scaling, pay-for-request billing, efficient bin-packing, and support for GPUs and TPUs for AI workloads.

GCE and GKE Security Dashboards Powered by SCC Now

🔒 Google has added integrated security dashboards to GCE and GKE consoles, powered by Security Command Center. The dashboards surface top security findings, vulnerability trends, CVE prioritization, and container/workload misconfigurations informed by Google Threat Intelligence and Mandiant analysis. Teams can remediate misconfigurations, prioritize patches, and monitor threats directly in their compute and cluster consoles. Full vulnerability and threat widgets require upgrading to SCC Premium (30‑day trial available).

GKE Managed Lustre CSI Driver for AI and HPC Workloads

🚀 Managed Lustre on GKE is a managed parallel file system with a CSI driver that brings low-latency, high-throughput POSIX storage to Kubernetes for demanding AI and HPC workloads. It is recommended for training, checkpointing, and small-file patterns where GPUs/TPUs must stay utilized, while Cloud Storage is an alternative for large, higher-latency files. The article presents five operational best practices—data locality, tiering, networking, provisioning, and using Kubernetes Jobs with a shared PVC—to maximize performance and control costs.

GKE Network Interface: From kubenet to the AI backbone

📡 Over the past decade, Google Cloud evolved GKE pod networking from basic kubenet and route-based clusters to VPC-native alias IPs and the eBPF-powered Cilium Dataplane V2, improving performance, scalability, and observability. The platform now supports extreme-scale AI workloads with multi-NIC, terabit throughput, and persistent IPs for stateful functions. Looking forward, Google is exploring the Kubernetes Network Driver and the DRANET reference to expose node-level network resources via Dynamic Resource Allocation.

Google Cloud and SAP: Unified Data, AI Agents, and HANA

🚀 Google Cloud and SAP announced tighter integration to unify enterprise data and accelerate intelligent automation. SAP Business Data Cloud now connects to BigQuery via Datasphere, enabling bidirectional replication and AI-ready analytics. Procurement is simplified on the Google Cloud Marketplace with SAP BTP. New agent tooling—Agentspace, the Agent Development Kit, A2A and MCP standards—and expanded M4 memory-optimized VMs certified for SAP HANA aim to speed deployments, improve data consistency, and enable autonomous process automation.

Google releases XProf and Cloud Diagnostics XProf tools

🔧 Google has open-sourced XProf, an upgraded ML profiler, and published the Cloud Diagnostics XProf library to simplify profiling and optimizing models on xPUs. The release brings unified XLA-based profiling across JAX, PyTorch/XLA and TensorFlow/Keras, and supports programmatic and on-demand trace capture. The Cloud Diagnostics library packages dependencies, stores profiles in Google Cloud Storage for retention, provisions TensorBoard on VMs or GKE for faster loading, and produces shareable links for collaborative analysis with tunable machine types for performance.

Disaggregated AI Inference with NVIDIA Dynamo on GKE

⚡ This post announces a reproducible recipe to deploy NVIDIA Dynamo for disaggregated LLM inference on Google Cloud’s AI Hypercomputer using Google Kubernetes Engine, vLLM, and A3 Ultra (H200) GPUs. The recipe separates prefill and decode phases across dedicated GPU pools to reduce contention and lower latency. It includes single-node and multi-node examples and step-by-step deployment actions. The repository provides configuration guidance and future plans for broader GPU and engine support.

GKE Turns 10 Hackathon: Build Agentic AI Microservices

🚀 Join the GKE Turns 10 Hackathon to build next‑generation microservices enhanced with agentic AI. Google provides sample applications (Bank of Anthos or Online Boutique), example agents on GitHub, documentation, quickstarts and a webinar to help teams get started. Submissions must run on GKE and use Google AI models such as Gemini, with agents interacting via APIs rather than altering core application code. Participants may also use the Agent Development Kit (ADK), Model Context Protocol (MCP) and Agent2Agent (A2A) to extend functionality.

Agent Development Kit Hackathon: Winners and Highlights

🚀 The Agent Development Kit (ADK) Hackathon concluded with more than 10,400 participants from 62 countries, 477 submitted projects, and 1,500+ agents built. The competition emphasized multi-agent orchestration for automation, data analysis, customer service, and content generation, awarding SalesShortcut the Grand Prize. Regional winners included Energy Agent AI, Edu.AI, GreenOps, and Nexora-AI, and organizers pointed participants to ADK documentation and developer forums while announcing an upcoming GKE hackathon with over $50,000 in prizes.

Container-Optimized Compute Delivers Fast Autopilot Scaling

🚀 GKE Autopilot now runs on a container-optimized compute platform that rethinks autoscaling to deliver near-real-time capacity. The platform uses dynamically resizable VMs and a pool of pre-provisioned compute so nodes can be resized or allocated without disrupting workloads. Customers on GKE Autopilot 1.32+ get faster pod scheduling, improved HPA responsiveness, and support for in-place pod resize out of the box. Google recommends the general purpose compute class for small, gradually scaling services.

EuroDaT and Google Cloud: Secure Financial Data Exchange

🔒 EuroDaT, a state-owned data trustee, built safeAML with major German banks to enable controlled, pseudonymous transaction matching while preserving GDPR compliance. The cloud-native service runs on Google Cloud and Google Kubernetes Engine, using infrastructure-as-code, isolated VPCs and auditable processing so EuroDaT never accesses personal-data content. By letting banks request targeted supplementary information, safeAML accelerates suspicious-activity checks, reduces false positives and lays groundwork for wider use in ESG and health data sharing.

EuroDaT and Google Cloud: Secure Financial Data Exchange

🔐 EuroDaT describes how its safeAML platform, built on Google Cloud and Google Kubernetes Engine, enables controlled, pseudonymous exchange of sensitive transaction data between banks. Acting as a neutral data trustee, EuroDaT never accesses personal content while automating secure, auditable workflows that replace error-prone phone calls. Pilots with German banks show faster, more accurate suspicion assessments and lower false positives.

Skopeo for Google Cloud: Simplifying Container Workflows

📦 This post describes how Skopeo, a daemonless CLI for container images, can streamline image management with Artifact Registry and Google Cloud CI/CD. It outlines setup steps and five practical workflows—inspect manifests, registry-to-registry copying, listing tags, promoting images, and automated verification. The article also covers security integrations with tools like Cosign and Binary Authorization, and recommends Skopeo for faster, daemonless automation in Cloud Build and related environments.

GKE Turns Ten: New Pricing, Autopilot Enhancements

🎉 Google marks the tenth anniversary of Google Kubernetes Engine (GKE) by simplifying pricing and expanding capabilities. Starting September 2025, GKE moves to a single paid tier, GKE Standard, which includes multi-cluster features such as Fleets, Teams, Config Management, and Policy Controller at no extra cost, with additional capabilities available à la carte. Google is also making Autopilot toggleable per cluster and per workload and promoting a container-optimized compute platform designed to increase efficiency and performance for AI and large-scale services.

What’s New in Google Cloud: Releases, Previews, and News

🔔 Google Cloud published a consolidated roundup of product releases and previews from early July through Aug 22, 2025, covering GA launches, public previews, and platform enhancements. Highlights include Earth Engine in BigQuery (GA), Vertex AI embedding scaling, new GKE features for NUMA alignment and swap, expanded NodeConfig controls, and Cloud Run with GPUs. Customers should review the linked documentation, request preview access via account teams where needed, and plan upgrades or migrations accordingly.