Dataloop vs Prodigy

Dataloop is a paid, enterprise-first AI development platform for building AI applications. It supports unstructured workflows for images, video, text, JSON, LiDAR, audio, PDF, GIS, and multimodal data. Users can work with off-the-shelf or custom models, then version, experiment, compare, and fine-tune them. Drag-and-drop pipelines or the Python SDK can coordinate data, models, and human feedback, including RLHF and other human-centric tasks. A marketplace provides models, datasets, workflow templates, and reusable components. Integrations include Google Cloud Platform, Amazon S3, Microsoft Azure Blob, AWS ECR, Google Artifact Registry, and Azure Container Registry. Dataloop lists GDPR compliance, ISO 27001, ISO 27701, and SOC 2 Type II compliance, with AES-256 and TLS v1.2 encryption. Access controls include role-based permissions, SSO, two-factor authentication, and activity logging. Deployment can be on-premises or in private storage. Pricing is available through a specialist demo.

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.