Alibaba Cloud PAI iTAG vs doccano

Alibaba Cloud PAI iTAG is a data-labeling platform for image, text, video, audio and multimodal datasets. Built-in image tasks include classification, object detection, OCR, table recognition and semantic segmentation; official templates also cover image-text explanation, conversation ranking, visual question answering and video OCR. Teams can use official or tenant templates, or create templates for business-specific labeling. A job brings together an OSS-backed dataset and template with labels, subtasks, checks, labelers and reviewers. Manual labeling is free, and selected large-model labeling templates are currently free; OSS storage and data transfer are billed separately. Input data must be in Alibaba Cloud OSS, and the workspace and bucket must be in the same region. Dataset imports require an index file rather than a direct path to a folder of raw files. From August 1, 2026, iTAG 1.0 is allowlist-only and in maintenance mode. Existing customers with prior labeling tasks or datasets can continue using current features. New users seeking AI-powered automatic dataset labeling are directed to Multimodal Data Management. API operations include listing, creating, updating and deleting templates and labeling tasks.

doccano is a free, open-source annotation tool for labeling data for human review and machine-learning work. It supports text classification, sequence labeling and sequence-to-sequence tasks, with examples including sentiment analysis, named-entity recognition and summarisation. A project workflow lets users create projects, import datasets, add collaborators, set annotation guidelines, label data and export results. Collaboration features include multi-language and mobile support, emoji support and a dark theme. REST APIs connect scripts and machine-learning models, and supported modalities include text, image and audio. The project documents installation with pip, Docker and Docker Compose on Linux, Windows or macOS machines running Python 3.8 or newer. Deployment options include AWS, Heroku or Docker elsewhere. Imported datasets can use Amazon S3 or Google Cloud Storage. SQLite 3 is the default database, while PostgreSQL and other database systems are also documented. Celery handles long-running imports and exports, with SQLite3, RabbitMQ and Redis listed as message-broker options. The installation documentation warns that upgrading an SQLite3 installation can result in database loss. The GitHub repository reports no detected SECURITY.md policy and no published security advisories.