Datasaur vs Doccano

Datasaur is a web-based platform for labeling data and coordinating labeling teams. Data Studio handles span, textual or row classification, document classification, OCR, bounding boxes, audio, and conversational labeling. LLM Labs adds sandbox experimentation, knowledge bases, human rating and ranking, and automated evaluation. The Models catalog contains over 200 base models and supports Amazon SageMaker JumpStart, Amazon Bedrock, Azure OpenAI, OpenAI, and Google Vertex AI. Integrations include OCR, language-processing, machine-learning, and cloud-storage services such as Amazon Textract, spaCy, Hugging Face, AWS S3, Google Cloud Storage, and Azure Blob Storage. The API supports webhook import and export, project creation and export, OAuth 2.0, and GraphQL; OAuth credentials are available only on Growth and Enterprise plans. Self-hosting is available with Kubernetes and Helm Chart or Docker. Datasaur states that it maintains SOC 2 Type 2, GDPR, and HIPAA compliance, with encryption at rest and in transit. Free includes one user, 5,000 labels per year, and 100MB storage. Paid plans start at $5,000/yr, and a 14-day trial is listed.

Doccano is a free, open-source data-labeling tool for machine-learning practitioners. Its supported annotation tasks include text classification, sequence labeling, and sequence-to-sequence annotation, with uses such as sentiment analysis, named entity recognition, and text summarization. Users can set up projects, import datasets, add collaborators, annotate examples, and export labeled data. REST APIs let scripts connect to Doccano for model-assisted labeling, and multiple people can work collaboratively. Installation options include pip, Docker, Docker Compose, source, or cloud deployment. The installation guide lists Linux, Windows, and macOS machines running Python 3.8 or later. SQLite 3 is the default database; PostgreSQL configuration is described, and MySQL is mentioned as an option. Imported datasets can be stored using Amazon S3 or Google Cloud Storage. The project also lists mobile and multi-language support, emoji support, and a dark theme. Integration guidance demonstrates Amazon Comprehend Sentiment Analysis and custom REST APIs for auto-labeling. One upgrade caveat is important: the installation guide warns that an upgrade using SQLite 3 can cause database loss.