Dataloop vs Labellerr
Dataloop
dataloop.aiLabellerr
labellerr.comSide by side
| Figure | Dataloop | Labellerr |
|---|---|---|
| Price from | — | $833.25/mo |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 1 | 3 |
| Spec score · Image Annotation Software | 7.9 (#13) | 8.3 (#7) |
| Founded | 2017 | 2018 |
| Headquarters | Tel Aviv, Israel | San Francisco, California, United States |
| Support | The contact page lists a support link and gives the address 2 Sapir st, Herzliya, Israel. | Enterprise plans include 24/7 email, chat, and call support, while Researcher and Pro list business-hours email support. |
Plans
Labellerr
- Researcher PlanFree
- Pro Plan$9,999 a year
- Enterprise PlanPriced on request
In their own words
Dataloop
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.
Labellerr
Labellerr is a data labeling and annotation platform for AI and machine-learning teams preparing training data. Projects can use images, videos, PDFs, text, and audio. Annotation options include bounding boxes, polygons, polylines, keypoints, dots, classification, segmentation, and object detection. Automated labeling includes prompt-based and model-assisted methods, plus active learning; the company also states native support for Meta’s SAM, SAM 2, and SAM 3. A Python SDK can manage datasets and workflows in ML pipelines. Annotated data can be exported in formats such as COCO JSON, YOLO, Pascal VOC XML, CSV, and custom formats. Remote storage options include AWS S3, Google Cloud Storage, and Azure Blob; integrations with Vertex AI, SageMaker, and Azure ML are add-ons for applicable plans. Stated security measures include Auth0 authentication, TLS 1.2 or higher in transit, and AES-256 encryption. The free Researcher plan includes 2,500 data credits, one seat, one workspace, and up to 10 projects. Pro costs $9,999 a year, and unused data credits do not roll into the next payment cycle.