Datasaur vs Prodigy

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.

Prodigy is a downloadable developer tool for creating training and evaluation data for machine learning models. Its browser-based app supports annotation of text, images, audio, video, relations, and multiple choice. Computer vision workflows include image classification, segmentation, and object detection; built-in recipes cover named entity recognition, text classification, dependency parsing, transcription, speaker diarization, training, and evaluation. Active learning models are included for named entity recognition, text classification, part-of-speech tagging, and dependency parsing. Users can create workflows with Python recipes and build interfaces using HTML, CSS, and JavaScript. Prodigy supports spaCy, has plugins for Hugging Face models, and integrates with major LLM API providers. Data is accessible through Python and the command line, and can be stored in SQLite, MySQL, or PostgreSQL, or exported to JSON. It runs on the user's own machines, including air-gapped machines, and its Python web server can be deployed in the cloud or on-premise. Personal costs $390, paid once, excluding tax; Company costs $490, paid once per seat in packs of five, excluding tax. Both include 12 months of free upgrades. Prodigy assumes basic Python and command-line familiarity; trials are not offered to individuals.