Las empresas están estandarizando cada vez más Google Kubernetes Engine (GKE) para ejecutar sus cargas de trabajo más críticas impulsadas por IA. Desde Cloud Storage FUSE para acceso a datos de alto rendimiento hasta clases de computación personalizadas (CCC) y segmentación avanzada de GPU, GKE proporciona la escala y la eficiencia necesarias para las tecnologías modernas.

Google Cloud se ha convertido en la plataforma elegida por las nuevas empresas que crean inteligencia artificial. Nuestra pila excepcionalmente completa, que incluye una selección de modelos y computación propios y de terceros; nuestra plataforma para crear y gestionar agentes; y nuestros productos para proteger las cargas de trabajo de IA, se ha convertido en el más importante

Las pequeñas y medianas empresas de América Latina son el corazón de la economía de la región y representan más del 60% del empleo total en la región, según estimaciones de las Naciones Unidas. Y al igual que sus pares empresariales, dondequiera que se mire, los equipos ambiciosos se están moviendo rápidamente para adoptar A

Tras nuestro anuncio de Gemini 3.8 Live y Gemini 3.8 Live Extended Thinking la semana pasada, estamos encantados de compartir que Gemini 3.8 Live con Live Avatar ahora está disponible de forma general en Gemini Enterprise. Vista previa por primera vez en Google Cloud Next 2026, la tecnología ahora está oficialmente lista para la empresa.

Se han dedicado carreras enteras de ingeniería de bases de datos a una sola pregunta: ¿Cómo se escala una carga de trabajo OLTP sin comprometer el sistema de registro propietario de los datos? Exadata respondió a la pregunta descargando consultas en un nivel de almacenamiento escalable debajo de la base de datos, eliminando la red como

La disponibilidad de recursos para cargas de trabajo de IA puede ser un desafío en toda la industria, especialmente en los aceleradores. Esto puede ralentizar la implementación de su carga de trabajo de IA si se basa en un tipo específico de acelerador. El concepto de computación fluida le permite diseñar su implementación de IA con varias opciones.

How cloud infrastructure, global payment APIs, and borderless crowdfunding bridge geographic distances to support personal debt elimination and financial recovery.

Earlier this year, we announced native support for Google Kubernetes Engine (GKE) custom metrics. This milestone allowed you to scrap external adapters and instead collect autoscaling metrics directly from your pods. By routing these metrics straight to the Horizontal Pod Autoscaler (HPA), we cut me

True elasticity has long been the holy grail of cloud-native engineering. And while Kubernetes has revolutionized resource management, workloads that run sporadically (e.g., batch processors, event-driven workers, and development environments) still consume compute resources while they wait for work

Amazon CloudWatch Omni is the next evolution of CloudWatch — unified observability that brings your applications and AI agents into one reimagined experience, with auto-discovered topology, natural language queries, and AI-guided investigation powered by AWS DevOps Agent.

🚀 GSP070 – App Engine: Qwik Start - Go Learn how to build and deploy a Go web application with Google App Engine Standard Environment. ☁️ App Engine 💻 Go / Golang 🚀 Serverless Deployment 🛠️ Google Cloud CLI ⚡ App Engine Standard 🌐 Horizon: Orbit of Ops:

🚀 GSP069 – App Engine: Qwik Start - PHP Learn how to deploy a PHP web application using Google App Engine Standard Environment. 🐘 PHP ☁️ App Engine 🚀 Serverless Deployment 🛠️ Google Cloud CLI ⚡ App Engine Standard 🌐 Horizon: Orbit of Ops:

🚀 GSP067 – App Engine: Qwik Start - Python Learn how to test, modify, and deploy a Python web application using Google App Engine Standard Environment. ☁️ Python Web App ⚡ App Engine 🚀 Serverless Deployment 🛠️ Google Cloud CLI 🌐 Horizon: Orbit of Ops: orbitofops.com

When running modern AI workloads, there’s often a conflict between performance and cost. Workloads like large language models (LLMs) load massive files, and may serve thousands of AI agents that need to execute code instantly. If each component is starting “cold” with a full data-load process, all t

The surge in AI development has created unprecedented demand for compute capacity around the globe. This can have negative implications for data processing and pipelines with Apache Spark. Whether you are managing your own Spark infrastructure or using a managed service, you can face availability co

Demand for AI infrastructure is at an all-time high. Global accelerator shortages mean engineering teams can rarely get all the compute they need from just one data center — capacity comes a cluster here, a cluster there, often an ocean apart. At the same time, workloads are getting hungrier: Today’

Living in the Netherlands, I spend a fair amount of time on trains, and that is usually where I catch up on what the builder community is writing. Until now, that meant opening a laptop or squinting at a browser tab on my phone. This week I found myself scrolling through trending articles and checki

🚀 Explore GSP761 and learn how to build and deploy a REST API with Go and Cloud Run. 🐹 Go / Golang 🌐 REST API ☁️ Cloud Run 🔥 Firestore 🚀 Serverless Deployment 🌐 Horizon: 🌐 Orbit of Ops:

🚀 Explore GSP650 and learn how Cloud Run and Pub/Sub can be combined to build resilient, asynchronous serverless applications. ☁️ Cloud Run 📨 Pub/Sub 🔄 Async Messaging 📈 Scalability 🛡️ Resilient Architecture 🌐 Horizon: 🌐 Orbit of Ops:

AI is accelerating software development at an unprecedented pace. But as code generation scales, so do the challenges of securing the code, especially emerging AI-based vulnerability exploitations. To meet these challenges, the Google AI and Infrastructure team is transforming how we approach securi

Today, we are excited to announce enhancements to the borderless Lakehouse, our answer to how data engineers, data scientists, and increasingly, AI agents, can query governed data directly where it lives. To reason accurately and automate complex enterprise workflows, agents and data consumers of al

Vector search is a critical component of generative AI, retrieval-augmented generation (RAG), and data agent architectures, but sometimes vector search alone isn't enough. While vector embeddings are incredible at understanding conceptual meaning, they stumble on specific alphanumeric IDs and exact

As enterprises invest in generative AI, tech leaders keep seeing the same pattern: Developers test AI tools for a week, hit setup problems, and then drift back to the backlog. Nothing ships. The real gap is enablement. In this landmark Harvard Business Review article, Josh Bersin and Marc Zao-Sander