Cloud Computing

For the third consecutive year, Google has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms. This recognition comes on the heels of Google Cloud Next 2026, where we feel we showcased a fundamental evolution in how enterprises interact with data: the shift from a reactive system

As enterprise generative AI transitions from simple, conversational chatbots to autonomous multi-agent workflows, developers face a critical bottleneck: scale. In a production environment, an enterprise agent often needs to navigate hundreds of heterogeneous data structures, dynamic business rules, and shifting API schemas. The standard blueprint relies on “Static Prompting”—pre-loading all potential JSON schemas, Pydantic classes,

Significant contributors to this article include Sneha Aradhey, Software Engineer, Google Kubernetes Engine, and Michael MacDonald, Sr Software Engineer, Google Cloud Managed Lustre. Enterprise production environments are shifting to distributed, multi-node architectures to serve long-context window lengths and agentic AI. As these workloads scale, KVCaches often outgrow local CPU RAM and host SSD cache tiers.

Businesses run on fast decisions, but the teams who hold the answers are often buried under a backlog of routine requests, leaving users waiting in line for insights they need now. Today, we are bringing Conversational Analytics in BigQuery to general availability, so both business and technical teams can query data, run multi-step analyses, and

Choosing the threshold of an alert policy can be a headache. You have to analyze historical data, aggregate it into semantically meaningful time series, and choose a threshold that matters. If the workload grows, your previously set static threshold might become too low, and your alert might fire too frequently. New workloads might require setting

Computational chemistry researchers have traditionally faced a frustrating trade-off when simulating molecular interactions: use fast classical force fields that sacrifice precision or rely on accurate quantum-mechanical methods that run too slowly on large jobs.  Machine-learned force fields (MLFFs) close that gap by training neural networks on high-fidelity quantum data. When it comes to modern drug

Great creative happens when your tools move at the speed of your ideas. To help you create rich, reliable experiences while reducing regeneration time and costs, we’re adding two new models to Gemini Enterprise Agent Platform.  First, we’re announcing the general availability of Nano Banana 2 Lite (Gemini 3.1 Flash-Lite Image). This model is the