All news with #google cloud tag

🚀 AlloyDB now offers PostgreSQL for agents (preview), enabling sandboxed, serverless PostgreSQL instances that provide up-to-the-second read-only access to production data while isolating agent workloads. These instances spin up in seconds, leverage Colossus-backed unified storage for sub-millisecond I/O and massive scan throughput, and scale down to zero to control costs. The engine remains fully PostgreSQL-compatible with vector, full-text, and spatial search plus strong Google Cloud security integrations.

🧭 This post outlines AlloyDB’s new agentic database architecture designed for autonomous agent workloads, arguing that traditional and emerging architectures trade off scale, latency, or isolation. It defines three tenets—Isolation, Latency, and Scale—and explains how AlloyDB, built on Google’s Colossus storage and microVM-based ephemeral compute, satisfies all three simultaneously. The article contrasts existing paradigms and presents test results showing shared-storage approaches fail under agentic load.

🔍 This article explains how Google Cloud networking accommodates fluid compute for AI workloads, with guidance for choosing GPUs or TPUs and their distinct backend networking needs. It outlines resource obtainment options—such as Flex-start VMs, calendar reservations, future and flex reservations, ComputeClasses, and Spot VMs—and describes four networking configurations: standard networking, accelerated GPU networking (TCPX/TCPXO and RoCEv2), TPU networking, and Cloud Run. The blog highlights deployment blueprints, GKE DRANET automation, and zonal profiles for RDMA, illustrating trade-offs for multi-node training and inference.

🔔 Google Cloud announced built-in Prometheus metrics processing for GKE, enabling HPA to use PromQL queries directly via Google Managed Service for Prometheus. This removes the need for third-party adapters, reduces latency, and simplifies autoscaling configuration. The controller runs in the control plane and only deploys pods on nodes when PromQL metrics are actively requested, minimizing resource use. The feature is in preview with plans to add self-hosted Prometheus support before GA.

🎯 This article explains how AlphaEvolve pairs cloud-based Gemini code generation with local evaluation to accelerate real-time video processing. It outlines the split-loop architecture—managed generation on Google Cloud and customer-run evaluators on target hardware—and emphasizes constructing robust quality gates like SSIM to prevent benchmark gaming. The post includes practical guidance on evaluator design, multi-frame state, and measuring hardware floors versus software overhead to set realistic optimization targets.

🔧 This article explains how Google’s Managed Service for Apache Spark uses flexible VMs to mitigate capacity stockouts that can disrupt Spark pipelines. Flexible VMs let teams specify ordered machine-family preferences for masters and workers, enabling multi-family blending, mixed storage support, and comprehensive cluster coverage. The post gives tiered machine-family and storage recommendations for common shapes (n2d, n1) and highlights the role of Hyperdisk Balanced. It also covers quota, CUDs, testing, and complementary strategies like AutoZone, autoscaling, smaller shapes, and regional fallbacks.

🔒 Google Cloud announced two generally available Secure Source Manager (SSM) features to harden CI/CD pipelines: enhanced blocking of unauthorized access across version control, build, and deployment systems, and a new Code Owners system for granular per-file and per-branch approver controls. The Code Owners feature supports nested ownership, branch-specific governance, glob-style path rules, and independent approval sections, while Developer Connect and Private Network Integrations let SSM connect CI/CD and runtimes across private networks with VPC Service Controls and Private Service Connect.

📰 This post introduces native BM25 full-text search in AlloyDB and Cloud SQL for PostgreSQL 17+ via the open-source pg_textsearch extension from TigerData. The change eliminates the need for separate full-text backends, reducing data duplication and sync complexity while delivering industry-standard BM25 ranking directly in the database. AlloyDB further offers accelerated vector search (ScaNN, HNSW) and an out-of-the-box hybrid search UDF to merge vector and keyword results; Cloud SQL supports hybrid results via CTEs and coalesced RRF scoring.

🔒 Today Google Cloud announced enhancements to the borderless Lakehouse to let data engineers, analysts, and AI agents query governed data in place across clouds. The update introduces preview cross-cloud caching for BigQuery to reduce remote data transfer by caching columnar blocks locally and preview cross-cloud connections to query non-Iceberg data. The features use the Apache Iceberg REST catalog spec, Partner Cross-Cloud Interconnect, and default encryption to improve performance, security, and TCO for multi-cloud analytics.

🚀 Pine59 modernized its data orchestration by moving to Managed Service for Apache Airflow (Gen 3) running Airflow 3 on Google Cloud to support massive location-intelligence pipelines. The company observed immediate gains in processing speed, reduced queue latency, and improved stability, enabling faster runs for heavy jobs like Daily Foot Traffic. Engineers also built custom UI plugins and a compatibility shim to streamline developer workflows and operator migration.

🔎 Lucius AI, a tender-intelligence startup covering five continents, consolidated its relational catalog, audit logs, and vector embeddings into AlloyDB for PostgreSQL and automated routine operations via the Model Context Protocol (MCP). By migrating semantic search to a ScaNN index, query latency fell from 1.14s to 24ms, a 47x improvement. The platform ingests notices from multiple procurement sources and runs across two production regions with strict least-privilege controls.

🚀 Google Cloud has launched the M4N machine series in Compute Engine, designed for I/O-intensive, high-memory workloads and now generally available. Built on 5th Gen Intel® Xeon® Scalable processors and Google’s Titanium offload architecture, M4N delivers up to 6TB RAM, 25 GiB/s aggregate host storage throughput, and up to 1 million IOPS with Hyperdisk Extreme. The family targets mission-critical databases, generative AI data layers, healthcare ERP, and real-time analytics while promising >20% TCO reduction for Oracle workloads.

🎮 SeaVerse, a SeaArt gaming startup, built a platform for playable AI experiences and needed infrastructure that could run dynamic, multi-tenant sandboxes with low latency, strong isolation, and better observability. By adopting Google Kubernetes Engine (GKE) and GKE Agent Sandbox, SeaVerse achieved kernel-level isolation with microVMs and gVisor options, improved runtime visibility, and scaled sandbox allocations rapidly. The change reduced infrastructure costs by up to 60% while preserving a fast creator experience.

🔐 Google Cloud has rolled out a 16-hour default session length and expanded session management into a granular, Context-Aware Access (CAA) feature. Administrators can now configure session controls via Terraform, gcloud, and REST APIs for DevSecOps workflows. Policies can target Google Groups and specific applications like the Cloud Console, gcloud, and OAuth apps, and policy management is being integrated into the Google Cloud Console preview. These updates aim to reduce credential theft and account takeover risk while preserving developer productivity.

🚀 Filestore agent volumes deliver fully managed, high-performance elastic file storage tailored for large-scale agent fleets on Google Cloud. Integrated with Agent Substrate and GKE Agent Sandbox, volumes attach in milliseconds to provide isolated persistent workspaces with RWX support, POSIX semantics, and granular access controls. The feature targets non-production workloads now, with GA production access via allowlist.

🚀 Google Cloud introduces Distributed GraphFlow (DGF), an open-source Python library and framework designed to train and deploy Graph Neural Networks (GNNs) at scale for telecommunications. The post outlines an Autonomous Network Operations architecture built around a real-time network digital twin hosted in Spanner Graph, and explains how DGF integrates with that twin to enable anomaly detection, root cause analysis, predictive maintenance, and what-if simulations. DGF offers composable primitives and a high-level API to simplify GNN lifecycle management and production inference via Gemini Enterprise endpoints.

🔧 This blog summarizes Google Cloud’s recommended “Verify→Investigate→Report→Resolve→Review” workflow for handling reliability incidents and advises preparing in advance by designing for failure, ensuring observability data, maintaining playbooks, and running drills. It distinguishes how to detect incidents via Personalized Service Health, Cloud Service Health, and observability tools, and provides guidance on scoping blast radius, diagnosing causes, and when to open and escalate support cases. It also covers mitigation steps while waiting for resolution and emphasizes blameless post-mortems to improve future response.

🚀 Google Cloud announces enhancements to Dataflow to support large AI workloads, including GA of Pause/Resume for batch jobs and support for G4 VMs with NVIDIA RTX PRO 6000 Blackwell GPUs. Pause/Resume reduces wasted compute by enabling stopped batch jobs to be resumed, improving productivity and resource utilization. The new GPU support delivers larger memory and bandwidth for in-pipeline inference using models of 70B+ parameters while preserving native RunInference and autoscaling capabilities.

🚀 Google Cloud was named a Leader and scored highest in current offering in The Forrester Wave™: Public Cloud Platforms, Q3 2026, with top marks in 23 of 30 criteria including AI, databases, analytics, containers, modernization, and security. The post highlights Google Cloud’s full-stack co-design from silicon to systems, recent platform updates for agentic workloads, and advancements in the Agentic Data Cloud to unify transactional and analytical systems.

🚀 Today Google Cloud announced a new installable plugin for AI coding agents that packages related agent skills and tools to simplify working with Google Cloud. The plugin follows the open Agent Plugins specification to provide consistent manifests and interoperability across agent environments. The flagship google-cloud-developer plugin helps agents with authentication, authorization, project management, gcloud guardrails, and integrates the Developer Knowledge MCP server for up-to-date documentation grounding.