Cloud Computing

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 this provisioning takes time, often forcing organizations to

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’s long-running agentic workloads often have

ReadyOn, an AWS Partner delivering an intelligent Labor Orchestration Platform for Fortune 100 enterprises, runs a multi-tenant platform on Amazon Elastic Kubernetes Service (Amazon EKS) that handles some of the most sensitive data in enterprise IT. Kubernetes is central to that platform, and it is also one of the most complex surfaces to secure. A

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 product SKU numbers. To build truly robust search and AI applications, you may need the

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-Sanders noted that knowledge workers carve out

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 security. In this article, we discuss new AI-native agentic methods that we’ve developed

This week we kicked off the DevFest season in North America at Google Hudson Square in New York City with 80 engineers packed into the room. Typical technical workshops hand you a finished repo, tell you to blindly paste blocks of code into your terminal, and hope nothing crashes. You walk away with green checkmarks,

State and local governments are driven by a shared mission to provide responsive, equitable, and accessible services. However, achieving this goal is often hindered by legacy technical debt, disconnected data, and heavy administrative burdens that slow down mission delivery. This systemic fragmentation creates costly operational bottlenecks across the public sector, including: Legacy data silos: Crucial