What is Platform for AI (PAI)?

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Key concepts

For foundational concepts, see What is artificial intelligence (AI)?

Platform overview

Platform for AI (PAI) is an end-to-end AI development platform from Alibaba Cloud. It covers data labeling, model development, model training, and model deployment. PAI provides these core modules:

Module

Description

Use case

Quick start

Model Gallery

Wraps EAS and DLC to let you train and deploy open-source large models without writing code.

Zero-code training and deployment of open-source models

Model Gallery quick start

Elastic Algorithm Service (EAS)

Deploys trained models as online inference services with minimal configuration.

Deploy models as service endpoints

EAS quick start

Data Science Workshop (DSW)

Provides a cloud-based interactive development environment. Notebook and VS Code users can start developing models immediately.

AI model development

DSW quick start

Distributed Learning Cluster (DLC)

Creates distributed or single-node training jobs without manual machine provisioning or environment setup. The workflow matches local training scripts.

Distributed model training

DLC quick start

Intelligent Tagging (iTAG)

Labels images, text, video, and other data types. Also offers fully managed labeling outsourcing services.

Data labeling

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Designer

Includes 140+ built-in algorithm components. Build models visually by dragging and dropping components.

Big data + AI model development

Designer quick start

Click a module name to learn more about its features.

Product strengths

Full AI development lifecycle

  • Covers data labeling, model development, training, optimization, deployment, and AI operations management as a unified platform.

  • Includes 140+ optimized built-in algorithm components.

  • Supports multiple development modes, deep integration with big data engines, multi-framework compatibility, and custom container images.

Multiple open-source frameworks

  • Flink for stream processing.

  • TensorFlow, PyTorch, Megatron, and DeepSpeed with performance optimizations on top of their open-source releases.

  • Spark, PySpark, and MapReduce.

Industry-leading AI optimization

  • High-performance sparse training framework: supports tens of billions of sparse features, hundreds of billions of samples, and incremental training across 1,000+ workers.

  • PAI Blade accelerates inference for mainstream models such as ResNet50 and Transformer+LM.

Flexible deployment options

  • Fully managed and semi-managed modes on the public cloud.

  • AI high-performance computing clusters and lightweight deployment form factors.

  • Scheduled execution through DataWorks with separate production and development environments for data isolation.

Billing

Billing method

Description

Use case

Applicable modules

Pay-as-you-go

Pay after use based on actual consumption.

Short-term or unpredictable workloads such as test environments, demand spikes, or early-stage projects.

DSW, DLC, EAS, Designer

Subscription

Prepaid monthly or yearly plans.

Long-term, steady workloads. Prepaying for a fixed period (such as one month or one year) gives you a lower unit price than pay-as-you-go.

DSW, DLC, EAS

Resource plan

Prepaid quota package for a specific resource.

Heavy use of a specific resource type. Quota packages offer volume discounts.

DSW

Savings plan

Prepaid discount commitment plan.

Commit to a fixed spending amount over a period to receive a lower pay-as-you-go rate.

DSW, EAS

Duration-based billing (Serverless)

Pay only for the time a service actively processes requests. Deployment is free. The service scales automatically based on request volume.

Variable request volume (Serverless deployment). Handles high concurrency and dynamic loads efficiently.

EAS

For detailed billing information, see Billing.

New user guide

If you're new to PAI, see Get started with PAI.

Use cases

Large model deployment and fine-tuning

AI-generated content (AIGC)

Retrieval-Augmented Generation (RAG)

AI agents

FAQ

Q: How do I claim, use, and release a free trial?

For details, see Claim, use, and release free trial resources.

Q: What do I do if a DSW instance fails to start or stop, and how do I release an instance?

For details, see DSW FAQ - Instance start and release.

Q: Why does an EAS service call fail?

For details, see EAS FAQ - Service invocation.