Head-to-head · Image Annotation Software
FiftyOne vs Dataloop
FiftyOne leads on 0 checks, Dataloop on 0, and 3 are even. Who comes out ahead on the 3 yes/no, price and count checks where we have data for both products. The editor score weighs everything else too.
Our verdict
- Highest scoreFiftyOne · 8.6/10
FiftyOne scores higher on our rubric for image annotation software: 8.6 against 8.2 out of 10; our editors rank them #5 and #9.
FiftyOne is the better fit for ML teams combining annotation and model evaluation. Dataloop is the better fit for enterprise teams with multimodal datasets.
- FiftyOne fits best
ML teams combining annotation and model evaluation
- Dataloop fits best
Enterprise teams with multimodal datasets
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Side by side
| Feature | FiftyOne 8.6/10 Visit ↗ | Dataloop 8.2/10 Visit ↗ |
|---|---|---|
| At a glance | ||
| Editor score | 8.6 | 8.2 |
| Ranking | #5 in Image Annotation Software | #9 in Image Annotation Software |
| Best for | ML teams combining annotation and model evaluation | Enterprise teams with multimodal datasets |
| Pricing model | Free | Paid |
| Starting price | Free | Pricing on request |
| Free plan | Not checked yet | Not checked yet |
| Free trial | — | — |
| Deployment | Cloud, Self-hosted | Cloud |
| Platforms | Web, Windows, macOS, Linux | Web |
| Support | Email, Community, Docs | Email, Tickets, Docs |
| Compliance | ISO 27001, SSO/SAML | SOC 2, ISO 27001, GDPR, SSO/SAML, 2FA |
| Integrations | 20 integrations | 6 integrations |
| Built for | Small business, Mid-market, Enterprise | Mid-market, Enterprise |
| Features FiftyOne 3/3 · Dataloop 3/3 | ||
| Model-assisted labeling | ✓ | ✓ |
| Team collaboration | ✓ | ✓ |
| API access | ✓ | ✓ |
| Specs | ||
| Annotation types | bounding boxes; polygons; segmentation masks; classifications; polylines; SAM2 click-to-segment instance segmentation | bounding boxes, polygons, key points, cuboids, polylines, semantic segmentation, classification, tagging, hierarchical annotation |
| Image formats | JPEG; PNG; BMP | JPG, JPEG, PNG, TIFF |
| Dataset export | COCO; image directory; image classification directory tree; FiftyOne dataset; Python/CLI export | Dataloop JSON, COCO, YOLO, VOC |
| Our review | ||
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| Cons |
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| Our verdict | FiftyOne is an image and multimodal data platform from Voxel51 for computer-vision and machine-learning teams. It combines annotation, dataset curation, model evaluation, review workflows, and dataset export in one environment. Annotation… Read the review → |
Dataloop is an enterprise-focused AI development platform for managing unstructured data, creating annotation workflows, integrating models, and orchestrating pipelines. It is designed for data engineers, data scientists, software… Read the review → |
Strengths and trade-offs
FiftyOne — where it wins
- Supports boxes, polygons, masks, classifications, polylines, and SAM2 annotation
- Combines annotation, review workflows, dataset curation, and model evaluation
- Offers APIs, self-hosting, exports, and integrations across data systems
Where it doesn't
- Enterprise capabilities are organized around contact-sales plans
- The broad feature set may exceed the needs of simple annotation projects
- Governed collaborative deployments require the Enterprise offering
Dataloop — where it wins
- Supports annotation across image, video, audio, text, PDF, LiDAR, and GIS
- Combines model-assisted labeling with active-learning workflows
- Offers SDKs, REST APIs, dataset management, and cloud integrations
Where it doesn't
- Pricing requires a direct conversation with Dataloop
- Its enterprise scope may exceed the needs of small annotation projects
- Workflow breadth can be more than teams focused only on image labeling need
- FiftyOne8.6/10 · Open source
A broad open-source platform for annotation, dataset curation, and model evaluation.
Visit FiftyOneFull verdict → - Dataloop8.2/10 · Pricing on request
A broad, API-ready annotation platform for enterprise multimodal AI teams.
Visit DataloopFull verdict →
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All image annotation software comparisons → · Full ranking →
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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