Alibaba Cloud Full Stack (11): PAI — The ML Platform

Fri, 08 May 2026 09:00:00 +0000

Training a model on a single GPU is fun. Deploying it to handle 1,000 requests per second without failing is what separates experiments from products. PAI handles both.

PAI (Platform for AI) is Alibaba Cloud’s managed ML platform. It’s not just one product; it’s five products in a trench coat, sharing a console. These include a notebook environment for exploration, a distributed training service for scale, a model serving platform for production, a visual pipeline designer for those who prefer dragging boxes, and a model gallery for one-click deployment of open-source models. After eighteen months of running real LLM workloads on it, I can say that the individual components range from excellent (EAS) to good enough (Designer). The whole platform is genuinely greater than the sum of its parts once you understand how they connect.

Aliyun PAI (1): Platform Overview and the Product Family Map

Thu, 05 Mar 2026 09:00:00 +0000

If your team trains or serves models on Alibaba Cloud, you’ll eventually use the PAI console. PAI is the umbrella; underneath it are the actual workhorses — a notebook product, a distributed training service, a model-serving service, and a few GUI/quick-deploy layers. After about eighteen months of running real LLM workloads on it for an AI marketing platform, this series is the field guide I wish I had before deploying my first endpoint.

EAS on Chen Kai Blog

Alibaba Cloud Full Stack (11): PAI — The ML Platform

Aliyun PAI (1): Platform Overview and the Product Family Map