If your team is training a computer vision or AI model, at some point someone has to sit down and label the data — draw boxes around objects, tag frames in a video, or rank one AI response against another. Doing that by hand in a spreadsheet doesn't scale past a few hundred examples. That's the job Labelbox and Encord are built for: platforms that let a team (in-house or outsourced) label thousands of images, videos or 3D scans, track quality, and feed the result straight into a training pipeline.
They get compared constantly because they solve the same core problem, but if you look past the marketing pages, they've actually specialized in different directions. This guide breaks down where each one is genuinely stronger, so you don't have to trial both before figuring out which fits your project.
The short version
Pick Labelbox if you're a frontier AI lab or enterprise team building agents or doing RLHF (reinforcement learning from human feedback) — its Alignerr network of 2.6M+ contracted reviewers and its Horizon/Recursion infrastructure go well beyond basic labeling. Pick Encord if your data is video, LiDAR, 3D, or comes from multiple sensors at once — robotics and autonomous-systems teams are its home turf. If you just need straightforward image or text labeling without either of those specializations, both are overkill for a small project, and you may be better served by a simpler tool or a generalist like Kili Technology or SuperAnnotate (also worth a look — see the comparison table below).
Labelbox: built for scale and RLHF, not just labeling
Labelbox has quietly moved past "a tool to draw bounding boxes" into infrastructure for training frontier AI models. Two things stand out: Horizon and Recursion, its reinforcement-learning and human-feedback infrastructure, and Alignerr, a network of over 2.6 million contracted expert reviewers you can tap into instead of hiring and training your own labeling team. It also supports multimodal robotics foundation model data through a product called Terra.
Who it's for: frontier AI labs, research teams and enterprises building AI agents or robotics systems who need labeling and a reviewer workforce, not just software.
Pricing: a free tier exists, but Starter, Scale and Enterprise plans are not publicly priced — every real evaluation means a sales call.
Honest limits: the RLHF/agent-training infrastructure is genuinely overkill if you just need to label a batch of product photos. And with no public pricing, you can't budget for it without talking to sales first.
Encord: the specialist for video, 3D and multi-sensor data
Encord leans hard into formats most labeling tools handle poorly: native video annotation, LiDAR, 3D point clouds, and orchestration across multiple sensors at once (think a self-driving car's camera + LiDAR + radar feed, all needing to be labeled in sync). It also tracks full labeling lineage — who labeled what, when, and how it was reviewed — which matters for quality control and audits.
Who it's for: computer vision and robotics/autonomous-systems teams whose data isn't flat images — video timelines, 3D scans, or multi-sensor streams.
Pricing: free tier available; Starter, Scale and Enterprise plans exist but pricing beyond the free tier isn't fully public.
Honest limits: that specialization is wasted if you're only labeling simple images or text. And like Labelbox, real pricing requires a conversation with sales.
Where they actually overlap — and where they don't
Both platforms offer a free tier, both are freemium-priced with custom enterprise plans, and both list each other (plus Kili Technology and SuperAnnotate) as direct alternatives. The real difference is what each one was built around first: Labelbox around scaling human feedback for model training (RLHF, agent evaluation, a huge reviewer network), Encord around handling data types that basic annotation tools choke on (video, 3D, LiDAR, multi-sensor).
If your project is "train a vision model on video footage from a fleet of cameras," Encord's native video and multi-sensor tooling saves real engineering time. If your project is "we're building an AI agent and need structured human feedback to fine-tune it," Labelbox's RLHF infrastructure and reviewer network are the more direct fit.
| Criteria | Labelbox | Encord |
|---|---|---|
| Core strength | RLHF / human-feedback infra, reviewer network | Video, 3D/LiDAR, multi-sensor annotation |
| Best for | Frontier AI labs, agent/RLHF training | Computer vision, robotics, autonomous systems |
| Notable feature | Alignerr network (2.6M+ reviewers) | Full labeling lineage for audits |
| Free tier | Yes | Yes |
| Pricing beyond free | Not public, sales required | Not fully public, sales required |
| Main honest limit | Overkill for simple labeling needs | Specialized focus may be overkill outside video/3D |
Conclusion
Neither platform is a bad choice — they're both serious, well-funded tools built for teams with real data-labeling volume, not hobby projects. The decision mostly comes down to your data: if it's video, 3D or multi-sensor, Encord's specialization will save you integration headaches. If you're building agents or doing RLHF and want access to a large reviewer network on top of the software, Labelbox is the more direct fit. Either way, budget time for a sales call — neither publishes pricing past the free tier, and for a workflow this central to your ML pipeline, that conversation is worth having before you commit.