If you're training a computer vision or AI model, someone has to label the raw data first — draw boxes around objects in photos, tag frames in video, mark up 3D point clouds — before a model can learn from it. Encord and Labelbox are two of the bigger platforms built to manage that labeling work at scale, with review tools, team workflows and quality checks built in. They look similar on the surface but are actually built for different jobs.
The short version
The short version: pick Encord if you're labeling video, LiDAR, 3D or multi-sensor data for computer vision or robotics — that's what it's built for. Pick Labelbox if you're training or fine-tuning frontier AI models and need reinforcement-learning/human-feedback infrastructure on top of basic labeling, not just annotation.
Encord — built for video, 3D and multi-sensor labeling
Encord is a platform for labeling, organizing and quality-checking the images, video and sensor data used to train computer vision and robotics AI models. It's aimed squarely at computer vision and physical AI/robotics teams that need to label video, 3D/LiDAR and multi-sensor data — not just flat images.
Pricing: a free tier is available; Starter, Scale and Enterprise paid plans exist above that but pricing isn't fully public.
Strengths: native video, LiDAR and 3D annotation (not just flat images), multi-sensor dataset orchestration built for robotics and autonomous systems, and full labeling lineage for quality control and auditability.
Limits: the specialized focus can be overkill if you only need simple image or text labeling, pricing beyond the free tier isn't published, and any autonomous/RLHF-assisted annotation accuracy claims are worth validating on your own data before trusting them at scale.
Labelbox — labeling plus reinforcement-learning infrastructure
Labelbox is a data platform for labeling, curating and generating human feedback to train and fine-tune AI models, including reinforcement-learning workflows — going beyond basic annotation into the infrastructure frontier AI labs use to improve models after the initial training pass.
Pricing: a free tier is available; Starter, Scale and Enterprise plans exist above it but pricing is not public — every real evaluation requires a sales conversation.
Strengths: reinforcement-learning and human-feedback infrastructure (Horizon, Recursion) that goes beyond basic labeling, a large network of 2.6M+ contracted expert reviewers (Alignerr) for specialized labeling tasks, and multimodal robotics foundation model data support (Terra).
Limits: no public pricing means every evaluation starts with a sales call, the RL/agent-training infrastructure is overkill for teams that just need basic labeling done, and the platform itself is proprietary despite some open-source client SDKs.
Quick comparison
| Encord | Labelbox | |
|---|---|---|
| Best for | Video, 3D/LiDAR, multi-sensor labeling | RL/human-feedback for frontier model training |
| Free tier | Yes | Yes |
| Pricing beyond free | Not public | Not public |
| Standout feature | Native 3D/LiDAR/video annotation + lineage | RLHF infrastructure + 2.6M+ reviewer network |
| Best audience | Computer vision / robotics teams | Frontier AI labs / agent trainers |
Other options worth a look
Neither Encord nor Labelbox is the only option here. Kili Technology is built for organizations running many concurrent annotation projects across images, video, text, PDF and geospatial data at once, with SOC 2, ISO 27001 and HIPAA certifications — useful if compliance is a hard requirement. SuperAnnotate covers images, video, text and audio in one platform with built-in AWS/GCP/Snowflake/Databricks integrations, aimed more at enterprise and foundation-model builders who want everything wired into their existing data stack.
Bottom line: if your data is video, 3D or multi-sensor and your team is building computer vision or robotics models, Encord's native tooling for that data will save you real engineering time. If you're past basic labeling and into reinforcement learning or human-feedback loops for a frontier model, Labelbox's RL infrastructure is built for exactly that job — but budget time for a sales conversation either way, since neither publishes pricing beyond the free tier.