All news with #google cloud tag

🚀 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.

📚 Google Cloud has consolidated all technical documentation onto a new, dedicated platform to improve discoverability and support AI-driven experiences. By centralizing content on a unified site and integrating Gemini into authoring tools, Google aims to accelerate content creation and deliver context-aware assistance. The site offers faster performance, AI-powered translation across 12 languages, and preserves existing URL patterns to minimize disruption.

🚀 Giles AI migrated its healthcare-focused platform to Google Cloud to reduce latency, improve scalability, and accelerate developer velocity. Using Google Kubernetes Engine, Cloud Run, and Compute Engine, the company orchestrates complex clinical data flows and routes prompts through Vertex AI and Model Garden to remain model-agnostic. Data storage and extraction are handled with Cloud SQL, Cloud Storage, and Document AI, while Cloud Armor and Security Command Center bolster security and compliance. Early customer results include dramatic reductions in research time and improvements in response accuracy.

🚀 Google Cloud is previewing managed DRANET on GKE, enabling Kubernetes to treat high-performance RDMA network interfaces as schedulable resources. The integration aligns NICs and GPUs by NUMA topology to reduce latency and increase throughput, while abstracting away operational complexity. It launches with the new A4X Max instances to deliver topology-aware networking for large multi-GPU AI workloads. Developers can request specific network interfaces in pod specs and rely on GKE to co-schedule NICs and accelerators, improving utilization and simplifying operations.

🚀 Google Cloud is expanding its NVIDIA collaboration with the new A4X Max instances powered by NVIDIA GB300 NVL72, delivering 72 GPUs with high‑bandwidth NVLink and shared memory for demanding multimodal reasoning. GKE now supports DRANET for topology‑aware RDMA scheduling and integrates NVIDIA NeMo Guardrails into GKE Inference Gateway, while Vertex AI Model Garden will host NVIDIA Nemotron models. Vertex AI Training adds NeMo and NeMo‑RL recipes and a managed Slurm environment to accelerate large‑scale training and deployment.

🔁 This Google Cloud post explains how enterprises can integrate Oracle Database with cloud-native analytics and AI by moving transactional data into BigQuery. It recommends ingestion patterns such as low-latency Change Data Capture via Datastream, batch staging to Cloud Storage, and notes ODBC/JDBC for interactive queries but not continuous replication. Once data resides in BigQuery, organizations can leverage Gemini-powered features, BigQuery ML, and AI agents (via the Agent Developer Kit) for natural-language exploration, assisted coding, multimodal analysis, and automated workflows across retail and education use cases.

🔐 Google Cloud outlines a pragmatic framework to secure the emerging agentic web while preserving smooth user experiences. The post details how reCAPTCHA and Google Cloud combine agent and user identity, continuous behavior analysis, and AI-resistant mitigations such as mobile-device attestations. It highlights enabling safe agentic commerce via protocols like AP2 and tighter integration with cloud AI services.

🚀 At Partner Connect during the Google Public Sector Summit, Google announced expanded investments to deepen collaboration and accelerate AI adoption across the public sector partner ecosystem. Highlights include increased Rapid Innovation Team funding, doubled capacity for Partner Development Sprints, and boosted Deal Acceleration Funds to shorten sales cycles. Google also launched three new Public Sector Partner Expertise badges for Google Distributed Cloud, Infrastructure Modernization, and Gemini for Government, a standardized Services Subcontractor Program, and an expanded ISV ATO Accelerator offering up to $250,000 in GCP credits plus $500,000 in services reimbursements to speed FedRAMP/Impact Level readiness. Partner Demo Portal improvements, enhanced analytics, new labs, and bootcamps round out efforts to simplify co-selling and accelerate partner time-to-market.

🛒 PayPal and Google Cloud announced a joint agentic commerce offering that integrates Google Cloud’s Conversational Commerce agent with payments powered by PayPal. The solution leverages the open Agent2Agent (A2A) Protocol and the Agent Payments Protocol (AP2)—which extends A2A and the Model Context Protocol—to enable agent-to-agent payment flows secured by Verifiable Digital Credentials. Merchants can deploy Google’s out‑of‑the‑box conversational agent or build custom agents with the Agent Development Kit (ADK), retaining control over tone, branding, and the customer relationship while benefiting from integrated payment and fraud controls.

🔔 Google Cloud previewed Bigtable tiered storage, which automatically moves less-frequently accessed data from high-performance SSD storage to an infrequent access tier while exposing the same Bigtable API. The fully managed feature integrates with Bigtable autoscaling so applications can read and write across hot and cold tiers via a single interface. Google says the infrequent access tier can be up to 85% less expensive than SSD and that a tiered-storage node offers substantially more usable capacity, making it suited for large time-series and telemetry datasets that require long-term retention for analytics or compliance.