Encord records 244,204 monthly visits with +9.4% month-over-month growth and Contact for Pricing pricing. Gitlights records 0 monthly visits with -100% growth and Free Trial, Paid pricing.
The multimodal data layer for computer vision and physical AI teams
See each developer's real contribution in the age of AI-generated code
| Dimension / Metric | ||
|---|---|---|
| Official Domain | encord.com | gitlights.com |
| Pricing Model | Contact for Pricing | Free Trial, Paid |
| Monthly Visits | 244.2K (244,204) | 0 (0) |
| MoM Traffic Delta | +21.04K (+9.4%) | -1.38K (-100%) |
| Primary Market & Share | 🇺🇸 United States (41.6%) | 🌐 Global (—) |
| Direct Visits / mo | 104.13K | 0 |
| User Rating | 4.4 | 4 |
| Supported Platforms | Website | Website |
The multimodal data layer for computer vision and physical AI teams
Native video, image, LiDAR, audio, text, and sensor-fusion labeling in one workflow, with label lineage and quality controls built for production scale.
Use embedding-based search and model-in-the-loop curation to surface rare edge cases and close distribution gaps before they hurt model performance.
Orchestrate RLHF, rubric-based evaluation, and pairwise comparison for models in production, then route failures back into training.
Access vetted domain experts for annotation and training-ready data collection, fully integrated with the Encord platform.
See each developer's real contribution in the age of AI-generated code
Analyzes commits, pull requests, reviews and discussion to show the decisions and architectural impact behind each developer's work, not just output volume.
Visual views that compare contributors across dimensions so team leads can spot strengths, gaps and shifts in how each person is contributing.
Automatically generated summaries of repository and team activity, giving engineering managers a regular narrative they can share or act on.
Install from the GitHub Marketplace, choose which repositories to analyze, and data loads within minutes with daily automatic updates.
Free / Open-sourceFaster setup and model deployment for computer vision, but less advanced enterprise data infrastructure features.
Free / Open-sourceFaster setup and model deployment for computer vision, but less advanced enterprise data infrastructure features.
Stronger enterprise workflow automation and agentic labeling, but less specialized in complex physical AI video pipelines.
Stronger enterprise workflow automation and agentic labeling, but less specialized in complex physical AI video pipelines.
Similar multimodal annotation capabilities, but features more integrated project management and dataset curation tools.
Similar multimodal annotation capabilities, but features more integrated project management and dataset curation tools.
SpecialistVideo data infrastructure for physical AI and robot training
SpecialistVideo data infrastructure for physical AI and robot training
Free / Open-sourceCompletely open-source and highly customizable for self-hosted labeling, but lacks built-in enterprise multimodal infrastructure.
Free / Open-sourceCompletely open-source and highly customizable for self-hosted labeling, but lacks built-in enterprise multimodal infrastructure.
Free / Open-sourceSuperior visual dataset search and quality evaluation capabilities, but relies more on programmatic workflows.
Free / Open-sourceSuperior visual dataset search and quality evaluation capabilities, but relies more on programmatic workflows.
Free / Open-sourceFree real-time analytics for open source projects on GitHub
Free / Open-sourceFree real-time analytics for open source projects on GitHub
Context-aware AI code reviews and a model router for coding agents
Context-aware AI code reviews and a model router for coding agents
SpecialistThe open-source data layer for robotics and physical AI
GitHub-native analytics for developer experience and team performance
GitHub-native analytics for developer experience and team performance
Provides end-to-end model training alongside annotation, but with a narrower focus than Encord's physical AI platform.
Provides end-to-end model training alongside annotation, but with a narrower focus than Encord's physical AI platform.
Strong managed labeling services and smart tools, but less advanced tooling for multimodal video and physical AI.
Strong managed labeling services and smart tools, but less advanced tooling for multimodal video and physical AI.
