Segments.ai
Data labeling software
Image recognition software
Deep learning software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
Take the quiz to check if Segments.ai and its alternatives fit your requirements.
$9,600 per year
Free TrialFree version
Small
Medium
Large
- Arts, entertainment, and recreation
- Media and communications
- Transportation and logistics
What is Segments.ai
Segments.ai is a web-based data labeling and dataset management platform focused on computer vision. It supports creating and managing annotations for images and video, with an emphasis on segmentation workflows used in training deep learning models. The product targets ML teams that need to curate, label, review, and export vision datasets for model development and iteration. It also provides dataset visualization and collaboration features to support labeling operations and quality control.
Strong segmentation-centric workflows
The product is designed around pixel-level and region-based annotation tasks commonly required for computer vision segmentation. It supports workflows that help teams create, review, and refine segmentation labels rather than only bounding-box style labeling. This focus can reduce process friction for teams building segmentation models. It fits use cases such as autonomous systems, robotics, and industrial inspection where segmentation quality matters.
Dataset management and review
Segments.ai includes dataset organization, visualization, and review capabilities that help teams track labeling progress and inspect label quality. Collaboration features support multi-user labeling and review cycles, which is important for production datasets. These capabilities align with common needs in ML data operations beyond one-off annotation jobs. The platform is positioned for iterative dataset improvement as models and requirements change.
Model-training pipeline compatibility
The platform is built to export labeled data for downstream training in common computer vision pipelines. This helps ML engineers move from annotation to experimentation without extensive manual data reshaping. It supports workflows where datasets are repeatedly exported as labeling guidelines evolve. This is particularly useful when teams maintain multiple dataset versions for different experiments.
Limited public vendor transparency
Publicly verifiable details about the company (e.g., founding year, headquarters, and corporate status) are not consistently available across authoritative sources. This can make vendor due diligence harder for procurement teams. Buyers may need to request documentation directly (security posture, data processing terms, and support SLAs). The lack of standardized disclosures can slow enterprise adoption.
May require ML ops maturity
Teams often need established labeling guidelines, review processes, and dataset versioning practices to get consistent results from segmentation labeling. Without these processes, annotation quality can vary and rework can increase. Organizations new to computer vision data operations may need additional internal effort to define workflows. This can extend time-to-value compared with simpler, task-specific labeling setups.
Enterprise controls not fully evidenced
Information on enterprise-grade controls (e.g., SSO/SAML, audit logs, role-based access granularity, and compliance attestations) is not clearly verifiable from public materials alone. For regulated industries, this may create uncertainty during security and compliance reviews. Prospective customers may need to validate these capabilities through trials or vendor questionnaires. This can be a blocker when strict governance requirements apply.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Core | $9,600 per year (14-day free trial available) | Starts at 3,600 hours/yr of labeling usage (~$2.67/hour). Unlimited seats, unlimited projects, unlimited images & point clouds, unlimited API & SDK access. Includes image, point cloud and sequence interfaces; ML-powered labeling tools; cloud bucket integrations; images with unlimited resolution; point clouds up to 500,000 points; active learning & model-assisted labeling; QA and org management. |
| Fusion | Custom quote per year | Starting at 5,000 hours/yr of labeling usage. Unlimited seats/projects/images & point clouds; unlimited API & SDK access. Adds sensor fusion interfaces, unlimited size/resolution for point clouds/images, extensive integration support, webhooks, priority support, metrics dashboard, access to experienced labeling workforce, and Mail/Slack/Discord support. |
| Enterprise | Custom quote per year | Includes +150,000 hours/year of labeling usage. Unlimited seats/projects/images & point clouds; unlimited API & SDK access. Adds custom interfaces, custom pipelines/workflows, influence on technical roadmap, custom security & procurement solutions, SSO and MFA, dedicated solutions ML engineer, tailored metric dashboards. |
Notes: Segments.ai also offers a 100% free academic license for non-commercial academic research (request via their academic license page), and indicates startup options are available by contacting sales.
