Segments.ai
Multi-sensor data labeling for robotics and autonomous-vehicle teams
At a glance
- Editor score8.0 / 10
- PricingPricing on request · 14-day trial
- Best forTeams labeling 3D and multi-sensor data
- Free planNo
- Model-assisted labelingYes
- Founded2020
- Facts checked20 Sep 2026
Where it wins
- Supports image, sequence, point-cloud, and sensor-fusion annotation
- Model-assisted labeling, review workflows, and collaborator roles
- Python SDK, APIs, webhooks, and machine-learning integrations
Where it doesn't
- Commercial pricing requires a sales contact
- No general free plan is provided
- Advanced security and custom workflows require Enterprise
Our verdict on Segments.ai
Segments.ai is a cloud-based training-data platform for computer-vision engineers and labeling teams. It supports image, image-sequence, 3D point-cloud, and multi-sensor annotation, making it a strong fit for robotics and autonomous-vehicle projects. Annotation options include segmentation masks, bounding boxes, polygons, polylines, keypoints, and point-cloud cuboids. Teams can also manage collaborators, apply review workflows, use model-assisted labeling, and release datasets for downstream machine-learning work.
The published plans are organized as Core, Fusion, and Enterprise, with commercial pricing handled through custom quotes. Core includes image, point-cloud, and sequence interfaces, ML-powered labeling tools, cloud bucket integrations, unlimited seats and projects, plus unlimited API and SDK access. Fusion adds sensor-fusion interfaces, webhooks, priority support, a metrics dashboard, and support through mail, Slack, and Discord. Enterprise adds custom interfaces, pipelines and workflows, custom security solutions, SSO and MFA, and a dedicated solutions ML engineer. A 14-day free trial is available, while the product does not offer a general free plan.
Its ecosystem fit is a central strength. Segments.ai provides a Python SDK, API keys, webhooks, and cloud-provider connections, with dataset export for JSON, PyTorch, TensorFlow, and Hugging Face workflows. That combination suits teams moving labeled data into model-training pipelines rather than teams seeking a simple image-only annotation tool. Choose Segments.ai when 3D geometry, sensor fusion, model assistance, or programmatic integration are important. Teams that need predictable self-serve pricing, a permanent free tier, or only basic image labeling should consider a simpler alternative.
Segments.ai pricing
All 3 Segments.ai plans and prices →
Segments.ai fact sheet
| Free plan | No |
|---|---|
| Paid from | Not verified |
| Annotation types | segmentation, bounding box, polygon, polyline, keypoint, cuboid |
| Image formats | jpeg, png, bmp |
| Model-assisted labeling | Yes |
| Dataset export | JSON, PyTorch, TensorFlow, Hugging Face |
| Team collaboration | Yes |
| API access | Yes |
| Free trial | 14 days |
| Deployment | Cloud |
| Platforms | Web |
| Compliance & security | ISO 27001, GDPR |
| Support | Email, Community, Docs |
| Built for | Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 6 integrations: Python SDK, webhooks, cloud providers, PyTorch, TensorFlow, Hugging Face See all → |
| Pricing | Pricing on request · 14-day trial (source) |
| Website | segments.ai |
| Facts checked | 20 Sep 2026 |
Segments.ai integrations
Segments.ai lists 6 integrations on its own site.
- Python SDK
- webhooks
- cloud providers
- PyTorch
- TensorFlow
- Hugging Face
See all Segments.ai integrations →
Alternatives to Segments.ai
- PixanoA flexible open-source choice for teams annotating 2D and 3D vision datasets.—
- CVATA broad CV workflow spanning manual, AI-assisted, collaborative, and programmatic annotation.9.0
- Label StudioFlexible image annotation with open-source deployment, AI assistance, and broad exports.8.0
See all Segments.ai alternatives →
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Reviewed by iTechGuides Editors · Editorial team · Updated Sep 2026
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