All news with #google cloud tag

🚀 Rent the Runway migrated from a complex, self-managed MySQL environment to Cloud SQL, gaining managed backups, built-in observability, and simplified disaster recovery. The move removed a third-party 24/7 MySQL support vendor and cut more than $180,000 in annual operational costs. Engineers now work in IAM-controlled instances with query insights, enabling safer schema changes, CI/CD for database updates, and faster, more reliable deployments.

🚀 Cloud Trace now accepts traces via OTLP through telemetry.googleapis.com, enabling vendor-agnostic ingestion and preserving the OpenTelemetry data model. Using OTLP reduces client-side exporter complexity, improves telemetry integrity, and enables easier routing to other backends. The Trace Explorer leverages OpenTelemetry semantic conventions for improved filtering and diagnostics. Google recommends telemetry.googleapis.com as the best practice for new and high-volume trace pipelines and provides a migration guide to help users transition.

🚀 This Cloud blog post from Google Cloud Consulting presents a practical three-part framework to quantify the business value of AI initiatives. It asks teams to define success across four value-driver categories, transparently specify Total Cost of Ownership (TCO), and state an explicit ROI. A worked example — an e-commerce customer-service chatbot — shows quantified monthly benefits versus estimated managed-service costs, demonstrating rapid payback and sustained positive cash flow.

🛡️ Google Cloud's Network Connectivity Center provides a centralized, hub-and-spoke model to simplify and scale enterprise connectivity across VPCs, on-premises data centers, and other clouds. Architected with distinct management, control, and data planes, it employs a fail-static design so existing traffic continues during control-plane issues. The service supports up to 250 VPC spokes per hub and reduces operational overhead by automating full-mesh connectivity.

🔐 Google Cloud has launched the Professional Security Operations Engineer (PSOE) certification to validate hands‑on skills for detecting, investigating, and responding to cloud threats. The exam focuses on practical use of Google Security Operations, Security Command Center, and threat intelligence across domains such as detection engineering, incident response, and threat hunting. Google recommends candidates have ~three years in security with at least one year of Google Cloud security tooling experience, and provides online training, hands‑on labs, and an official exam guide to prepare.

🚀 GKE Inference Gateway and GKE Inference Quickstart are now generally available, bringing production-ready inferencing features built on AI Hypercomputer. New capabilities include prefix-aware load balancing, disaggregated serving, vLLM support on TPUs and Ironwood TPUs, and model streaming with Anywhere Cache to cut model load times. These features target faster time-to-first-token and time-per-output-token, higher throughput, and lower inference costs, while Quickstart offers data-driven accelerator and configuration recommendations.

⚡ 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.

🛒 Google Cloud announced general availability of the Conversational Commerce agent on Vertex AI, a shopping-focused conversational assistant designed to guide customers from intent to purchase. The agent uses Gemini to interpret complex queries, supports context retention across sessions and devices, and offers administrative controls to boost, bury, or restrict products. Albertsons Cos. reports increased basket size in early use. Onboarding is presented as quick with minimal development effort via the Vertex AI console.

🚀 Google is previewing two Gemini CLI extensions that bring security analysis and Cloud Run deployment directly into your terminal. The security extension introduces /security:analyze to scan local git diffs for issues such as hardcoded secrets, injection flaws, broken access control, and insecure data handling, and returns clear remediation guidance or optional fixes. The Cloud Run extension adds /deploy, a one-command flow to build, containerize, push, and configure services on Cloud Run, returning a public URL and supporting terminal, VS Code agent mode, and Cloud Shell.

🔁 Google Cloud has introduced Data Transfer Essentials, a no-cost service for EU and U.K. customers to move multicloud data between Google Cloud and other providers. Designed for in-parallel processing across multiple clouds, qualifying multicloud traffic is metered separately and billed at a zero charge while other traffic remains billed at existing Network Service Tier rates. Customers can opt in via a configuration guide to specify which traffic qualifies.

🚀 Google Cloud has released an official Rust SDK that provides idiomatic, supported access to more than 140 Google Cloud APIs. The SDK includes built-in authentication (ADC, OAuth2, API Keys, service accounts, and upcoming Workload Identity Federation), documentation, and code samples to streamline development. It targets high-performance backends, secure data processing, and real-time analytics, and the project is available on crates.io and GitHub for feedback and contributions.

🔒 Google Cloud and its partners announced a range of partner-built AI security solutions now available in the Google Cloud Marketplace. These integrations embed Gemini and Vertex AI into partner products — including CrowdStrike, Palo Alto Networks, Fortinet, and others — to protect models, data, applications, and agents. The collaborations emphasize automated detection, incident response, DLP, identity protection, and agent monitoring to reduce mean time to detect and respond, helping customers adopt AI securely.

🚀 Google Cloud announced Dataproc multi-tenant clusters to let many data scientists share a single cluster for interactive notebook workloads while preserving per-user authorization. The feature maps individual Google identities to service accounts, externalizes mappings to a YAML file, and supports updates on running clusters. Jupyter kernels launch via the Jupyter Kernel Gateway across worker nodes, with optional Vertex AI Workbench integration and the BigQuery JupyterLab Extension. Administrators retain IAM-based least-privilege control and cluster hardening isolates credentials and OS users.

🛡️ The Agentic SOC Workshop is a complimentary, half-day event series from Google Cloud designed to help security professionals apply agentic AI and cloud-native threat intelligence to real-world operations. Attendees will participate in hands-on labs, a Capture the Flag challenge, and peer networking to learn how Gemini and Google Cloud tools can reduce alert fatigue and automate routine workflows. Sessions start in Los Angeles on Sept. 17 and Chicago on Sept. 19, with additional dates in October.

🚗 Horizon is an open-source, cloud-native software factory developed by Google and Accenture to standardize platform development for Android Automotive OS (AAOS) and related embedded vehicle software. It promotes a virtual-first workflow using high-fidelity cloud devices, VHAL, virtio, and scalable Cuttlefish instances to enable elastic testing and rapid developer feedback. Innovations such as the Android Build File System (ABFS), containerized reproducible builds, and integrated code tooling have produced 10x–50x faster feedback in early deployments, reducing build times and supporting high-frequency, higher-quality releases.

🛠️ Gemini Cloud Assist Investigations is now in public preview to help troubleshoot Dataproc and Serverless for Apache Spark workloads by automatically analyzing driver and executor logs, Spark UI metrics, configurations, and cross-product telemetry. Accessible from the Google Cloud console and via API, it produces prioritized summaries and clear remediation steps. The tool is tailored to data engineers, data scientists, SREs, and managers to reduce investigation time and accelerate fixes.

🏭 Tata Steel implemented a unified manufacturing data foundation on Google Cloud, centralizing OT and IT sources into a Manufacturing Data Engine built on BigQuery. The multi-path ingestion architecture leverages partners such as Litmus and ClearBlade to collect real-time PLC telemetry, while SAP, APIs, and in-house sensors feed batch and staging pipelines. The design emphasizes secure upstaging, partitioned storage with archival to Cloud Storage, and enables predictive maintenance, environmental KPI reporting, and reduced human presence in hazardous areas.

🔔 Google Cloud has expanded its Compute Flexible Committed Use Discounts (Flex CUDs) to cover additional VM families and serverless offerings, delivering broader savings and greater deployment flexibility. The update adds enhanced discounts for memory‑optimized M1–M4 instances and HPC‑optimized H3 and H4D families, and extends coverage to Cloud Run request-based billing and Cloud Functions. A new spend-based billing model applies discounts directly to eligible usage rather than issuing credits, and introduces changes to the Billing UI, Cloud Billing export to BigQuery schema, and Cloud Commerce Consumer Procurement APIs. Customers can opt in immediately; those who do not will be auto-transitioned to the new model on January 21, 2026, while new Billing Accounts created on or after July 15, 2025 will default to the updated model.

🚀 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.

🚀 Baseten reports major cost-performance gains for AI inference by combining Google Cloud A4 VMs powered by NVIDIA Blackwell GPUs with Google Cloud’s Dynamic Workload Scheduler. The company cites 225% better cost-performance for high-throughput inference and 25% improvement for latency-sensitive workloads. Baseten pairs cutting-edge hardware with an open, optimized software stack — including TensorRT-LLM, NVIDIA Dynamo, and vLLM — and multi-cloud resilience to deliver scalable, production-ready inference.