Looking for Labelbox Alternative?
Lightly vs.

Labelbox

Deploy AI models faster to production and at the fraction of the cost with Lightly.

Automate Data Curation at Scale.

LightlyOne is a data curation platform for computer vision that helps you find and label the most valuable samples.

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Optimize AI Data Collection at the Edge.

LightlyEdge is a smart data selection SDK for edge devices. It collects high-value data in real time, reducing transfer and storage needs.

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Pretrain Your Vision Models, No Labels Needed.

LightlyTrain is the world’s first computer vision pretraining framework tailored for industrial applications.

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Automate Data Curation at Scale.

LightlyEdge is a data curation platform for computer vision that helps you find and label the most valuable samples.

Learn More

Optimize AI Data Collection at the Edge.

LightlyEdge is a smart data selection SDK for edge devices. It collects high-value data in real time, reducing transfer and storage needs.

Learn More

Pretrain your vision models

LightlyTrain is the world’s first computer vision pretraining framework tailored for industrial applications.

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Trusted by enterprises, researchers and startups.

Comparison

Compare Lightly vs. Labelbox Features

End-to-End Vision Workflow vs. Multimodal Data Factory

Primary ML Lifecycle Focus

Integrated data loop: curation, inspection, label fixes, QA, and pretraining/fine-tuning in one platform
Labeling-centric data factory for training data production, with curation and evaluation layered on top - no native model training

Data Curation & Selection

Embeddings-driven active learning, multimodal search, and smart subset selection at scale (to 10M+ images)
Catalog: vector + traditional search, metadata filtering, CLIP/custom embeddings, Smart Select, Cluster View, similarity search, and foundation-model-powered data enrichment; slices enable automated curation based on model feedback

Model Training

Built-in LightlyTrain: SSL pretraining, distillation from foundation models (DINOv2/v3), and fine-tuning across YOLO, RT-DETR, and custom architectures
No native training; Model product handles diagnostics, evaluation, and predictions import; Foundry integrates foundation models for pre-labeling and inference, but production model training happens externally

Annotation Capabilities

Native inline editor + QA tightly coupled to curation; auto-labeling via LightlyTrain pseudo-labels
Bounding boxes, polygons, segmentation masks; foundation-model-assisted labeling, configurable review workflows, on-demand workforce

Scalability

Scales to 10M+ images with embeddings-driven indexing, smart subset selection, and flexible local or cloud storage
Cloud-managed platform that scales to petabytes of data across cloud storage (AWS S3, GCP, Azure); usage based LBU pricing; Enterprise API rate limits up to 20K requests per minute; no native on prem deployment

Vision-Language & Video

Native video support with embeddings-driven multimodal search (image-to-image, image-to-video, text-to-image) inside a unified curation + training workflow
Video annotation tool with frame-by-frame editing; multimodal support for images, video, text, audio, and geospatial with vector and traditional search
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Testimonials

Trusted by teams shipping Computer Vision to production

What customers say after choosing Lightly

"We had millions of images but no clear way to prioritize. Manual selection was slow and full of guesswork. With Lightly, we just feed in the data and get back what’s actually worth labeling."

Carlos Alvarez
Machine Learning Engineer
3x
Faster Model Iteration Cycle

We collect millions of road surface images, but since surface imagery is highly spatially correlated, labelling every sample is redundant, and finding sets of diverse data was a challenge.

Vijay Gill Hansted
Machine Learning Engineer
2M+
Curated Images

“The pretrained models were low in performance. The color scheme is probably the reason, they just don’t transfer well to ash-RGB. This is why we decided to give LightlyTrain distillation a try.”

Ana-Maria Pelin
ML Trainee
Improved performance YOLO-based models

"Through this collaboration, SDSC and Lightly have combined their expertise to revolutionize the process of frame selection in surgical videos, making it more efficient and accurate than ever before to find the best subset of frames for labeling and model training."

Margaux Masson-Forsythe
Director of Machine Learning
2.3M
Frames Processed In 1 Month

“Lightly enabled us to improve our ML data pipeline in all regards: Selection, Efficiency, and Functionality. This allowed us to cut customer onboarding time by 50% while achieving better model performance.”

Harishma Dayanidhi
Co-Founder/ VP of Engineering
10%
Model Accuracy Improvement

"It took far less work than expected to plug DINO into our SSL system - the LightlySSL code was clean and easy to adapt"

Suraj Pai
Research Associate
3D
SSL Training Pipeline with DINOv2

“By integrating Lightly into our existing workflow, we achieved a 90% reduction in dataset size and doubled the efficiency of our deployment process. The tool’s seamless implementation significantly enhanced our data pipeline.”

Usman Khan
Sr. Data Scientist
2x
Model Deployment Efficiency Gains

“Lightly gave us transparency to a part of the ML development that is a black box, data. Furthermore, Lightly enabled us to do Active Learning at scale and helped us improve recall and F1-score of our object detector by 32% and 10% compared to our previous data selection method. We finally saw the light in our data using Lightly.”

Gonzalo Urquieta
Project Leader
36%
Model Accuracy Improvement

"Lightly is hyper-focused on finding thousands of relevant images from millions of video frames to improve deep learning models. The Lightly platform enabled us to build models and deploy features more than 2x faster and unlock completely new development workflows."

Isura Ranatunga
Co-Founder and CTO
50%
Reduced Retraining Process Time

"I was truly amazed once we received the results of Lightly. We knew we had a lot of similar images due to our video feed but the results showed us how we can work more efficiently by selecting the right data"

Alejandro Garcia
CEO
80%
Reduction in Annotation Costs

"We had millions of images but no clear way to prioritize. Manual selection was slow and full of guesswork. With Lightly, we just feed in the data and get back what’s actually worth labeling."

Carlos Alvarez
Machine Learning Engineer
3x
Faster Model Iteration Cycle

We collect millions of road surface images, but since surface imagery is highly spatially correlated, labelling every sample is redundant, and finding sets of diverse data was a challenge.

Vijay Gill Hansted
Machine Learning Engineer
2M+
Curated Images

“The pretrained models were low in performance. The color scheme is probably the reason, they just don’t transfer well to ash-RGB. This is why we decided to give LightlyTrain distillation a try.”

Ana-Maria Pelin
ML Trainee
Improved performance YOLO-based models

"Through this collaboration, SDSC and Lightly have combined their expertise to revolutionize the process of frame selection in surgical videos, making it more efficient and accurate than ever before to find the best subset of frames for labeling and model training."

Margaux Masson-Forsythe
Director of Machine Learning
2.3M
Frames Processed In 1 Month

“Lightly enabled us to improve our ML data pipeline in all regards: Selection, Efficiency, and Functionality. This allowed us to cut customer onboarding time by 50% while achieving better model performance.”

Harishma Dayanidhi
Co-Founder/ VP of Engineering
10%
Model Accuracy Improvement

"It took far less work than expected to plug DINO into our SSL system - the LightlySSL code was clean and easy to adapt"

Suraj Pai
Research Associate
3D
SSL Training Pipeline with DINOv2

“By integrating Lightly into our existing workflow, we achieved a 90% reduction in dataset size and doubled the efficiency of our deployment process. The tool’s seamless implementation significantly enhanced our data pipeline.”

Usman Khan
Sr. Data Scientist
2x
Model Deployment Efficiency Gains

“Lightly gave us transparency to a part of the ML development that is a black box, data. Furthermore, Lightly enabled us to do Active Learning at scale and helped us improve recall and F1-score of our object detector by 32% and 10% compared to our previous data selection method. We finally saw the light in our data using Lightly.”

Gonzalo Urquieta
Project Leader
36%
Model Accuracy Improvement

"Lightly is hyper-focused on finding thousands of relevant images from millions of video frames to improve deep learning models. The Lightly platform enabled us to build models and deploy features more than 2x faster and unlock completely new development workflows."

Isura Ranatunga
Co-Founder and CTO
50%
Reduced Retraining Process Time

"I was truly amazed once we received the results of Lightly. We knew we had a lot of similar images due to our video feed but the results showed us how we can work more efficiently by selecting the right data"

Alejandro Garcia
CEO
80%
Reduction in Annotation Costs

Explore Lightly Products

LightlyStudio

Data Curation & Labeling

Curate, label and manage your data
in one place

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LightlyTrain

Self-Supervised Pretraining

Leverage self-supervised learning to pretrain models

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LightlyServices

AI Training Data for LLMs & CV

Expert training data services for LLMs, AI Agents and vision

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Ready to Get Started?

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