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Generative AI inference is uniquely hard: models are tens to hundreds of gigabytes, latency requirements are measured in tokens per second, cold starts can span multiple minutes as containers and weights transfer, GPU capacity is constrained, and traditional monitoring tools expose none of the token-level signals that matter in production. Amazon SageMaker AI offers customers […]

Open-weight models are changing the economics of building and deploying AI at scale. Rapid gains in intelligence and efficiency mean companies can match each workload with the right balance of capability, speed, and cost. AWS is building for a future in which organizations can adopt open-weight innovation with the reliability and security required for production.

Organizations building multi-model agentic AI applications face growing infrastructure complexity. Managing container orchestration, scaling policies, identity, and observability for multiple model types adds operational overhead. Teams often spend more time on infrastructure than on agent logic development. Developers running agentic frameworks on self-managed infrastructure such as Amazon Elastic Container Service (Amazon ECS) with AWS Fargate

Agents are no longer experiments. They process claims, write and review code, coordinate across systems, and run for hours without supervision. As agents take on more complex, longer-running work, the infrastructure underneath them must evolve just as fast. We built Amazon Bedrock AgentCore to help developers build, connect, and optimize agents securely at scale. AgentCore

Deploying a Hugging Face model to production means making a dozen decisions: choosing the right serving container for the model’s architecture, confirming the current image tag for your AWS Region, and matching an instance type to the model’s memory footprint. Beyond infrastructure, you must wire autoscaling so you don’t burn GPU hours on an idle

We are expanding our AI & Economy team with world-class academic advisors, fellows, and core internal researchers. ​ 

Eliminate GPU waste. Reduce first-token latency by up to 82%. Install one Kubernetes-native addon with zero application changes. The problem: Naive routing wastes your most expensive resource Running large language models (LLMs) at scale on GPU clusters is expensive. The default Kubernetes load balancers are making it worse. Round-robin and least-connections algorithms have no visibility

Google worked side-by-side with designers Jane Wade and Sergio Hudson to custom-design Google Flow tools to prep for NYFW. ​ 

Google and the UN system have launched the UN System Data Commons, a new open platform making global statistics accessible and easy to search. ​ 

Hiring at scale in industries such as retail, logistics, hospitality, and others has its fair share of challenges. Recruiting teams are expected to fill hundreds of roles within tight timelines, often with limited capacity and with tools that weren’t designed to seamlessly work together. As a result of this, applications pile up, phone screens get