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Encord vs Labelbox

Side-by-side comparison of Encord and Labelbox: pricing, features, API access, and community ratings.

AI comparison summary

TL;DR: Encord emphasizes infrastructure flexibility with an API-first approach that keeps your data in your own cloud, making it ideal for enterprise teams with strict data residency requirements. Labelbox differentiates through its massive 2.6M+ expert workforce network (Alignerr) and strong focus on post-training alignment for frontier AI labs, offering both the platform and the human capital. Labelbox also offers a free tier for getting started, while Encord is paid-only. Both handle multimodal data well, but Labelbox has deeper connections to AI research communities while Encord prioritizes data sovereignty and Physical AI applications.

Encord
Encord

The data infrastructure layer for Physical AI and Enterprise teams — multimodal by design

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Labelbox
Labelbox

The data factory for AI teams — powering the world's most ambitious AI labs

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Category
Chat assistants
Chat assistants
Pricing
Paid
Freemium
Starting price
Free tier
Audience
Technical
All audiences
API available
Open source
Self-hostable
Model provider
Founded
2021
2018
Headquarters
San Francisco, USA
San Francisco, USA
Key strengths
  • ·Multimodal data annotation across video, image, LiDAR, audio, text, and geospatial
  • ·End-to-end data pipeline from collection and curation to post-training alignment
  • ·Embedding-based search and model-in-the-loop curation for edge case discovery
  • ·Native support for Physical AI use cases including robotics, AVs, and drones
  • ·Trusted by 90%+ of leading US AI labs for post-training data
  • ·Alignerr network of 2.6M+ expert knowledge workers across 40+ countries
  • ·Full-stack robotics data including video, trajectories, and multimodal annotations
  • ·Custom private AGI benchmarks and frontier model evaluations
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