As empresas estão cada vez mais padronizando o Google Kubernetes Engine (GKE) para executar suas cargas de trabalho mais críticas e orientadas por IA. Desde o Cloud Storage FUSE para acesso a dados de alto rendimento até classes de computação personalizadas (CCC) e fatiamento avançado de GPU, o GKE oferece a escala e a eficiência necessárias para

O Google Cloud se tornou a plataforma preferida para startups que criam IA. Nossa pilha completa e exclusiva — incluindo opções de computação e modelos próprios e de terceiros; nossa plataforma para construção e gerenciamento de agentes; e nossos produtos para proteger cargas de trabalho de IA — emergiu como o mais importante

As pequenas e médias empresas da América Latina são o coração da economia da região – representando mais de 60% do emprego total na região, de acordo com estimativas das Nações Unidas. E, assim como seus pares empresariais, para onde quer que você olhe, equipes ambiciosas estão se movendo rapidamente para adotar A

Após nosso anúncio do Gemini 3.8 Live e do Gemini 3.8 Live Extended Thinking na semana passada, temos o prazer de compartilhar que o Gemini 3.8 Live com Live Avatar agora está disponível para o público geral no Gemini Enterprise. Apresentada pela primeira vez no Google Cloud Next 2026, a tecnologia agora está oficialmente pronta para empresas

Carreiras inteiras de engenharia de banco de dados foram dedicadas a uma única questão: como dimensionar uma carga de trabalho OLTP sem comprometer o sistema de registro que possui os dados? O Exadata respondeu à pergunta descarregando consultas em uma camada de armazenamento escalável abaixo do banco de dados, removendo a rede como

A disponibilidade de recursos para cargas de trabalho de IA pode ser um desafio em todo o setor, especialmente nos aceleradores. Isso pode retardar a implantação da carga de trabalho de IA se ela for construída em torno de um tipo específico de acelerador. O conceito de computação fluida permite que você projete sua implantação de IA com diversas opções básicas

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