Prodigy vs Segments.ai
Prodigy
prodigy.aiSegments.ai
segments.aiSide by side
| Figure | Prodigy | Segments.ai |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | Yes |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | — |
| Plans published | 2 | 4 |
| Spec score · AI Image Annotation Tools | 8.4 (#6) | 8.1 (#9) |
| Founded | 2016 | 2020 |
| Integrations | Prodigy has first-class support for spaCy, plugins for Hugging Face models, and out-of-the-box integrations with major LLM API providers. | The Python SDK supports programmatic data upload, label downloads, dataset management, and webhooks; the integrations page names TensorFlow and PyTorch. |
| Intended users | Prodigy is designed as a developer tool and assumes basic familiarity with Python and the command line, while its annotation web app can be used without programming experience after setup. | The company describes the product as designed for computer vision engineers and data scientists working in autonomous robotics and driving. |
| Product | Prodigy is a downloadable developer tool and Python library for creating training and evaluation data for machine learning models. | Segments.ai is a multi-sensor data labeling platform for robotics and autonomous vehicle teams. |
| Rating on G2 | 4.1 out of 5 from 17 reviews. | 4.6 out of 5 from 22 reviews. |
| Self-hosting | Prodigy runs locally or on infrastructure chosen by the customer, including on-premise and air-gapped environments. | The product is presented as a cloud platform; the reviewed documentation provides cloud access and customer-bucket options but no self-hosted deployment package. |
| Support | The product includes documentation and access to a support forum; company licenses also include priority forum and email support. | Fusion includes priority support and mail, Slack, and Discord support. |
| What it does | Prodigy is a developer-oriented annotation tool for creating training and evaluation data for machine-learning systems. | Segments.ai is a cloud data-labeling platform for computer-vision, robotics and autonomous-vehicle teams, supporting images, videos, point clouds and multi-sensor data. |
Plans
Segments.ai
- Academic licenseFree
- CorePrice not published
- EnterprisePrice not published
- FusionPrice not published
In their own words
Prodigy
Prodigy is a downloadable developer tool and Python library for creating training and evaluation data for machine-learning models. Its built-in recipes cover named entity recognition, text classification, computer vision, audio and video annotation, transcription, and model training and evaluation. A customizable web application offers 23 interfaces for work with text, images, audio, video, relations, multiple choice, HTML and conflict resolution. Users can define workflows with Python recipes and create interfaces using HTML, CSS and JavaScript. Prodigy supports spaCy, plugins for Hugging Face models and integrations with major LLM API providers. Data can be stored in SQLite, MySQL or PostgreSQL and exported to JSON. It runs on the user's own machines without connecting to the maker's or third-party servers, including on air-gapped machines. The standard Python web server and app can be deployed in the cloud or on-premise. Personal licensing costs $390, paid once, excluding tax; Company licensing costs $490, paid once per seat in packs of five, excluding tax.
Segments.ai
Segments.ai is a data labeling platform for robotics and autonomous vehicle work, with tools for both images and 3D point clouds. Teams can mark data with segmentation, cuboids, bounding boxes, keypoints, polygons, and polylines, view images alongside point clouds, and project labels between sensors. For sequences, Batch Mode, merged point clouds, interpolation, and automated tracking support labeling across objects and frames. Image workflows include Superpixel, Autosegment, hover-and-click segmentation, and model predictions that teams can review and correct. Its Python SDK handles uploads, label downloads, dataset management, and webhooks; TensorFlow and PyTorch are named integrations. The product is available through web and API access, and datasets can be exported as JSON, PyTorch, TensorFlow, or Hugging Face formats. Core includes cloud bucket integrations. The company states it is EU based, ISO 27001 certified, and GDPR compliant. Pricing is on request, with a 14-day trial; a free academic license is available for non-commercial research at free.