Cloud Computing

Significant contributors to this article include Megan O’Keefe, Senior Staff Developer Advocate, and Karl Weinmeister, Director of Developer Relations. Whether you are joining us in person in Las Vegas or tuning in virtually from around the world, Google Cloud Next ’26 offers a deep look into the practical evolution of AI. With 89% of sessions […]

The AI era requires a fundamental rethink of physical cloud architecture — networking, in particular. With foundational model parameters growing exponentially, traditional general-purpose networks are reaching their breaking points. To fuel the next decade of machine learning, Google designed Virgo Network, a new megascale AI data center fabric that embraces a “campus-as-a-computer” philosophy, and that

While generative AI sparked a revolution, the true paradigm shift is the rapid evolution from standalone AI models to multi-agent autonomous systems. In this new era, the network transcends basic connectivity to become the critical integration layer for your agentic enterprise. As AI agents and services surge, your core applications remain as vital as ever.

In the agentic era, the role of enterprise data is shifting from a passive resource to a dynamic System of Action. That’s why we announced the Agentic Data Cloud — a unified architecture that integrates models, analytics, and operational databases into a single, AI-native system. This allows organizations to unlock the full potential of their

In the early days of generative AI, building safe and reliable business tools took massive engineering effort and a high tolerance for trial and error. We helped solve that with Vertex AI, our trusted AI development platform. But today, we’re managing a different level of complexity, with agents interacting across multiple systems — and often

This week at Google Cloud Next ‘26, we are sharing the evolution of Google Kubernetes Engine (GKE), delivering leading performance, efficiency, security, and scale for your most demanding and complex workloads, and the next generation of AI and agentic applications. Why it matters:  Kubernetes has rapidly become the operating system for the AI era, with

Traditional data catalogs were built as manual inventories for technical users, focusing on table structures rather than the deep context that AI agents need. When agents lack business semantics and data relationships, this triggers hallucinations, high latency, and stale insights.  To address this problem, we are evolving Dataplex into a dynamic, always-on Knowledge Catalog that

At Google, our TPU design philosophy has always been centered on three pillars: scalability, reliability, and efficiency. As AI models evolve from dense large language models (LLMs) to massive Mixture-of-Experts (MoEs) and reasoning-heavy architectures, the hardware must do more than just add floating point operations per second (FLOPS); it must evolve to meet the specific

Today at Google Cloud Next, we are unveiling a more proactive Gemini Cloud Assist, our AI-assisted cloud operations platform. This update shifts your Google Cloud operations from manual workflows to a proactive, intelligent experience supported by a powerful ecosystem of agents. Why it matters: A new agentic architecture enables Gemini Cloud Assist to handle the

From vibe-coded and large-scale apps to AI models and agents, Cloud Run delivers on-demand compute with zero overhead and pay-per-use pricing for all of your workloads. Last year, the number of external active developers and applications on Cloud Run doubled, with more new customers and apps coming to Cloud Run in 2025 than in its