What is Alibaba Cloud Platform for AI (PAI)? - Platform For AI

Platform overview

Platform for AI (PAI) is Alibaba Cloud's one-stop AI development platform. It provides a comprehensive suite of services for the end-to-end AI development lifecycle, including data annotation, model development, model training, and model deployment. PAI consists of the following core components:

Core component	Description	Scenario	Quick Start
Data Annotation (iTAG)	Supports annotation for various data types, including images, text, and videos. It also offers fully managed data annotation outsourcing services.	Data annotation	-
Data Science Workshop (DSW)	Provides a cloud-based IDE for AI development. Developers familiar with Notebooks or VSCode can get started quickly.	AI model development	DSW Quick Start
Machine Learning Designer	Provides over 140 built-in algorithm components, allowing you to build models visually by using a low-code, drag-and-drop interface.	Big data + AI model development	Designer Quick Start
Deep Learning Containers (DLC)	Lets you quickly create distributed or single-node training jobs without manually provisioning machines and configuring environments, similar to running local training scripts.	Distributed model training	DLC Quick Start
Elastic Algorithm Service (EAS)	Deploys trained models as online inference services with simple configurations.	Deploy models as service APIs	EAS Quick Start
Model Gallery	Integrates DLC and EAS for zero-code training and deployment of open-source models.	Zero-code training and deployment of open-source models	Model Gallery Quick Start

Click the name of a component to learn more about its features.

Benefits

End-to-end AI development

Supports the full AI lifecycle, from data annotation, model development, model training, and model optimization to model deployment and AI operations and governance.
Offers over 140 optimized, built-in algorithm components.
Provides core capabilities such as development modes, deep integration with big data engines, multi-framework compatibility, and custom image support.

Support for multiple open-source frameworks

The Flink stream processing framework.
Deep learning frameworks such as TensorFlow, PyTorch, Megatron, and DeepSpeed, which are deeply optimized based on their upstream open-source versions.
Industry-standard open-source frameworks such as Spark, PySpark, and MapReduce.

Industry-leading AI optimization

A high-performance computing (HPC) training framework for sparse training scenarios, supporting tens to hundreds of billions of sparse features, hundreds of billions to trillions of samples, and distributed incremental training on up to a thousand workers.
PAI-Blade accelerates many mainstream models, including ResNet-50 and Transformer+LM.

Flexible product delivery

Provides fully managed and semi-managed options on the public cloud.
Supports deployment in various formats, including AI High-Performance Computing (HPC) clusters and lightweight offerings.
Supports periodic scheduling with DataWorks, which separates production and development environments to ensure data security and isolation.

Billing

Billing method	Description	Scenarios	Applicable components
Pay-as-you-go	A pay-as-you-go method where you are billed based on actual usage.	Ideal for short-term or unpredictable workloads, such as test environments, bursty demand, or initial project phases.	Designer, DSW, DLC, EAS
Subscription	A prepaid method where you purchase resources on a monthly or yearly basis.	Suitable for long-term, stable workloads. Committing to a fixed duration costs less than the pay-as-you-go method.	DSW, DLC, EAS
Resource plan	A prepaid method where you purchase a quota package for specific resources.	Best for scenarios that require a large volume of specific resources. Purchasing a resource quota package provides a better price.	DSW
Savings plan	A prepaid method where you purchase a specific discount plan.	Ideal for users who commit to a specific spending amount over a period to receive a lower pay-as-you-go rate.	DSW, EAS
Pay-by-inference-duration	A pay-as-you-go billing model where you are charged only for the actual inference duration during service invocation—with no cost for service deployment. Resources automatically scale elastically based on request volume.	Ideal for Serverless inference workloads with unpredictable or variable demand, effectively handling high-concurrency requests and dynamic loads.	EAS

Get started

To get started with PAI, see the Get started as a new user.

Use cases

LLM deployment and fine-tuning: Deploy an large language model (LLM) application in 5 minutes using EAS ｜ Deploy DeepSeek-V3 and DeepSeek-R1 models with one click ｜ Deploy, fine-tune, and evaluate Qwen3 series models .
Artificial Intelligence Generated Content (AIGC): AI art: Deploy SDWebUI ｜ Generate high-definition long videos using EasyAnimate ｜ AI video generation: Deploy ComfyUI .
Retrieval-augmented generation (RAG): LLM RAG chatbot (v0.3.x) ｜ Build a DeepSeek+RAG Q&A application flow for the finance and healthcare realms .

FAQ

Q: Why is my PAI DSW instance failing to start or stop, and how can I release it?

For more information, see DSW FAQ - Instance startup and release.

Q: Why is my PAI EAS service invocation failing and how can I troubleshoot it?

For more information, see EAS FAQ - Service invocation.