Labelbox

Labelbox is a platform for labeling, managing, and improving training data, with the option to bring in a vetted workforce when your own team cannot cover the volume.

The platform side gives you annotation tools for images, video, text, and documents, a catalog for organizing and searching your data, and model-assisted labeling that pre-labels items so annotators correct rather than start from scratch. Reviewers approve or send work back, and the whole project stays visible so you can see throughput and quality without chasing spreadsheets.

Most teams use it in one of two ways:

• Self-serve, where your own labelers or subject-matter experts work inside the tool
• Boost, where Labelbox supplies a managed workforce to do the labeling for you

On top of that, Labelbox runs Alignerr, a network of screened experts for the harder frontier-data work: ranking model responses, writing answers, and evaluating output for language models. The acceptance rate into that network is low, which is the point when the tasks need advanced degrees or real coding depth.

That range is why Labelbox shows up on a lot of shortlists. A computer-vision team labels images in the platform with their own annotators. A company fine-tuning an LLM taps Alignerr for preference data. A team that started self-serve adds Boost when a deadline arrives and they need more hands without standing up a vendor from zero.

Pricing is a freemium model. There is a free tier to try the platform, with paid plans that scale by usage and seats, and managed labeling or expert work quoted on top.

Best for teams that want one platform across data types and the flexibility to add labelers or experts without switching vendors. Less of a fit for a team that only wants a fully hands-off managed service and never plans to touch the tooling, where a pure workforce provider is simpler.