All news with #google cloud tag

🚀 Deploy the official n8n Docker image to Cloud Run in minutes to run scalable, serverless AI workflows. Cloud Run scales from zero and persists data in Cloud SQL while you only pay for active usage. The post shows how to call Gemini as the agent LLM and optionally connect workflows to Google Workspace via OAuth for Gmail, Calendar, and Drive. For production, follow the n8n docs to add Secrets Manager, Cloud SQL, and Terraform-based deployment.

🔍 AlloyDB AI launches two preview features—Auto Vector Embeddings and Auto Vector Index—that let teams convert operational databases into AI-native stores using simple SQL. Auto Vector Embeddings generates and incrementally refreshes vectors in-database, batching calls to Vertex AI and running as a background process. The Auto Vector Index (ScaNN) self-configures, self-tunes, and maintains vector indexes to accelerate filtered semantic search and reduce ETL and tuning overhead for production workloads.

🇪🇺 Google Cloud has formed a new European Advisory Board to provide strategic counsel on regulatory, product, and market priorities and to help customers navigate complex European requirements. The board unites leaders from technology, finance, retail, and public service, chaired by Jim Snabe, and includes Stefan Heidenreich, Nigel Hinshelwood, Christophe Cuvillier and Tim Radford (joining Jan 2026). The group will meet periodically to guide Europe-first product development, policy engagement, and sustainability efforts, reinforcing Google Cloud’s commitment to regional expertise and customer-focused innovation.

🔒 Enterprises continue to struggle with cloud misconfigurations that expose sensitive data, according to recent industry reporting and a Qualys study. The report cites a 28% breach rate tied to cloud or SaaS services over the past year and high misconfiguration rates across AWS (45%), GCP (63%) and Azure (70%). Experts blame permissive provider defaults, shadow IT and rapid business-driven deployments, and recommend controls such as MFA everywhere, private networking, encryption, least-privilege and infrastructure-as-code.

🚀 Google Cloud announced general availability of Ironwood, its seventh-generation TPU, alongside a new family of Arm-based Axion VMs. Ironwood is optimized for large-scale training, reinforcement learning, and high-volume, low-latency inference, with claims of 10x peak performance over TPU v5p and multi-fold efficiency gains versus TPU v6e (Trillium). The architecture supports superpods up to 9,216 chips, 9.6 Tb/s inter‑chip interconnect, up to 1.77 PB shared HBM, and Optical Circuit Switching for dynamic fabric routing. Complementary software and orchestration updates — including Cluster Director, MaxText improvements, vLLM support, and GKE Inference Gateway — aim to reduce time-to-first-token and serving costs, while Axion N4A/C4A instances provide ARM-based CPU options for cost-sensitive inference and data-prep workloads.

🔧 Google Cloud is introducing C4A metal, a bare-metal instance class powered by its Arm-based Axion processors, entering preview soon. Designed for workloads that require direct hardware access and Arm-native compatibility, C4A metal delivers 96 vCPUs, 768 GB DDR5 memory, up to 100 Gbps networking, and support for Google Cloud Hyperdisk variants. C4A metal targets Android development, automotive simulation, CI/CD, security workloads, and custom hypervisors by eliminating nested virtualization overhead and preserving Arm instruction-set parity.

🚀 Google Cloud has introduced the Axion-based N4A VM series in preview, positioned as the most cost-effective N-series to date with up to 2× better price-performance and 80% better performance-per-watt versus comparable x86 VMs. Available on Compute Engine, GKE, Dataproc and Batch, N4A supports up to 64 vCPUs, 512 GB DDR5, 50 Gbps networking, Custom Machine Types and new Hyperdisk storage profiles (Balanced, Throughput, ML). Early customers report substantial cost and performance gains.

🚀 The Ironwood TPU stack is a co‑designed hardware and software platform that scales from massive pre‑training to low‑latency inference. It combines dense MXU compute, ample HBM3E memory, and a high‑bandwidth ICI/OCS interconnect with compiler-driven optimizations in XLA and native support for JAX and PyTorch. Pallas and Mosaic enable hand‑tuned kernels for peak performance, while observability and orchestration tools address resilience and efficiency across pods and superpods.

🚀 Vertex AI Agent Builder is Google Cloud's integrated platform to build, scale, and govern production AI agents. The update expands the Agent Development Kit (ADK) and Agent Engine with configurable context layers to reduce token usage, an adaptable plugins framework, and new language SDK support including Go. Production features include observability, evaluation tools, simplified deployment via the ADK CLI, and strengthened governance with native agent identities and Model Armor protections.

🚀 Buildertrend describes migrating from Memorystore for Redis to Google Cloud’s managed Memorystore for Valkey to gain native cross‑regional replication, improved networking via Private Service Connect, and performance advantages. The team exported cache data to Google Cloud Storage and seeded Valkey instances to minimize downtime, eliminated a proxy layer, and now uses Valkey for caching, session state, job queues, pub/sub idempotency, and authentication tokens.

🔧 Workload Manager automates FinOps governance by codifying cost-control policies and enforcing them across Google Cloud environments. It supports both predefined checks (for example, bigquery-missing-labels) and custom rules written in Open Policy Agent (OPA) Rego, allowing organization-, folder-, or project-level scans. Scheduled evaluations can export results to BigQuery, trigger notifications (email, Slack, PagerDuty), and feed Looker Studio dashboards for reporting and trend analysis. New pricing reduces scan costs by up to 95% and includes a small free tier to accelerate adoption.

🔗 Networking is a critical enabler for AI on Google Cloud, connecting models, storage, and inference endpoints while preserving security and performance. The post outlines seven capabilities—from private API access and RDMA-backed GPU interconnects to hybrid Cross-Cloud links—that reduce latency, prevent data exfiltration, and simplify model serving. It also highlights options for exposing inference (managed services, GKE, load balancing) and previews AI-driven network operations using Gemini.

🚀 Google Cloud and Anyscale have enhanced the Ray experience on Cloud TPUs with GKE to reduce setup complexity and improve performance. The new ray.util.tpu library and a SlicePlacementGroup with a label_selector API automatically reserve co-located TPU slices and preserve SPMD topology to avoid resource fragmentation. Ray Train and Ray Serve gain expanded TPU support including alpha JAX training, while TPU metrics and libtpu logs appear in the Ray Dashboard for faster troubleshooting and migration between GPUs and TPUs.

🔬 Google Cloud presents how researchers can accelerate scientific workflows by combining Gemini Enterprise with integrated HPC infrastructure. It showcases AI agents—like the Deep Research agent for literature synthesis and the Idea Generation agent for proposing and ranking hypotheses—alongside developer tooling such as Gemini Code Assist and Gemini CLI for code, debugging, and workflow automation. The platform pairs these capabilities with purpose-built VMs (H4D, A4, A4X) and Google Cloud Managed Lustre to scale simulations and analysis.

🔔 Google Cloud has made Cost Anomaly Detection generally available to provide an automatic safety net for unexpected cloud spend. Alerts are enabled by default for all projects and delivered to Billing Administrators, with preferences managed in the billing console and direct links to an Anomaly dashboard that shows suspected root causes. The GA release introduces AI-generated thresholds that learn from historical spending, a percentage-deviation filter to keep alerts relevant across project sizes, and cold-start handling so new accounts receive protection immediately. The feature is free and integrates with Cloud Budgets as part of Google Cloud’s FinOps capabilities.

🚀 Google Cloud and Anyscale describe tighter integration between Ray and Kubernetes to improve distributed AI scheduling and autoscaling on GKE. The release introduces a Ray Label Selector API (Ray v2.49) to align task, actor and placement-group placement with Kubernetes labels and GKE custom compute classes, enabling targeted placement and fallback strategies for GPUs and markets. It also adds Dynamic Resource Allocation for A4X/GB200 racks, writable cgroups for Ray resource isolation on GKE v1.34+, TPU/JAX training support via a JAXTrainer in Ray v2.49, and in-place pod resizing (Kubernetes v1.33) for vertical autoscaling and higher efficiency.

🚀 This post explains how an external KV Cache backed by Google Cloud Managed Lustre can accelerate transformer inference and lower costs by offloading expensive prefill compute to I/O. In experiments with a 50K token context and ~75% cache-hit, Managed Lustre increased inference throughput by 75% and cut mean time-to-first-token by 44%. The analysis projects a 35% TCO reduction and up to ~43% fewer GPUs for the same workload, and the article summarizes practical steps: provision Managed Lustre in the same zone, deploy an inference server that supports external caching (for example vLLM), enable o_direct, and tune I/O parallelism.

🔍 The Log Analytics query builder in Google Cloud Console provides a UI-driven way to build and preview SQL-based log queries without hand-coding. It helps DevOps engineers, SREs, and application developers search across fields, infer JSON schemas, select nested values, and apply aggregations via an intuitive interface. Real-time SQL preview and one-click visualizations let users switch to the editor to fine-tune queries and save dashboards.

☁️ Global Payments partnered with Google Cloud to design a multi-region, highly available database architecture using Cloud SQL Enterprise Plus. The deployment spans three regions with zonal replication, read replicas, cascading replication, and Cloud SQL Auth Proxy integration to support low-latency reads and rapid failover. This configuration yields near-zero planned downtime, sub-minute RTO and zero RPO for Tier 1 workloads, while meeting PCI DSS, GDPR, and NIST requirements.

🔒 Google Cloud has been migrating its infrastructure toward post-quantum cryptography for nearly a decade to mitigate Store Now, Decrypt Later (SNDL) risks. The company has deployed the standards-based ML-KEM (FIPS 203) for key exchange across internal traffic and the Google Cloud networking stack, and introduced ML-KEM capabilities in Cloud KMS (preview) for key generation, encapsulation, and decapsulation. It also added native support for ML-DSA and SLH-DSA in Cloud KMS to protect long-lived digital signatures, and is phasing quantum-safe certificate support into Certificate Authority Service to enable future PQC-ready PKI. Administrators will receive tooling to opt in, audit cryptographic assets, and manage transitions to hybrid or pure PQC deployments as standards mature.