Cloud Computing

AI has accelerated startup innovation more than any technology since perhaps the internet itself, and we’ve been fortunate to have a front row seat to much of this innovation here at Google Cloud. Nine of the top ten AI labs use Google Cloud, as does nearly every AI unicorn and more than 60% of the

As enterprises increasingly adopt model context protocol (MCP) to extend capabilities of AI models to better integrate with external tools, databases, and APIs, it becomes even more important to ensure secure MCP deployment.  MCP unlocks new capabilities for AI systems; it can also introduce new risks, such as tool poisoning, prompt injection, and dynamic tool

People often think of BigQuery in the context of data warehousing and analytics, but it is a crucial part of the AI ecosystem as well. And today, we’re excited to share significant performance improvements to BigQuery that make it even easier to extract insights from your data with generative AI.  In addition to native model

Trading in capital markets demands peak compute performance, with every microsecond impacting critical decisions and market outcomes. At Google Cloud, we’re committed to providing global markets with the cutting-edge infrastructure they need to create and participate in digital exchange ecosystems. Our industry investments enable a purpose-built, cloud-native market infrastructure solution leveraging a global network that

High-quality text embeddings are the engine for modern AI applications like semantic search, classification, and retrieval-augmented generation (RAG). But when it comes to picking a model to generate these embeddings, we know one size doesn’t fit all. Some use cases demand state-of-the-art quality, while others prioritize cost, speed, or compatibility with the open-source ecosystem. To