Labelbox vs Scale AI

Verdict

The difference between Labelbox and Scale AI is who does the labeling, not what gets labeled. Labelbox's core, Annotate, is a multimodal editor and data-curation platform (Catalog) that your own reviewers use — you're buying software and a workflow. Scale AI positions itself as a full "Data Engine": software plus access to managed human labeling workforces, so you can submit work and get labeled data back without staffing the review yourselves.

Both have expanded well beyond traditional annotation into reinforcement-learning and agent-training data for foundation-model builders — RL environments, preference-signal collection and model evaluation — which is now a meaningful part of each company's business, not a side feature.

Choose Labelbox if

• Your own team (analysts, in-house reviewers) will do the labeling, and you want a capable editor plus curation and QA tooling around them.
• You want model-assisted pre-labeling to speed up your reviewers' throughput.
• You'd rather control quality directly than delegate it to a vendor's workforce.

Choose Scale AI if

• You need labeling capacity you don't have in-house, at volume, without hiring or managing reviewers.
• Your project is RLHF, preference ranking or red-teaming for a foundation model and you want a vendor built around that work.
• You want usage-based pricing that scales with labeling volume rather than a seat-based software subscription.

The honest caveat

Neither company publishes pricing that lets you compare cost per labeled unit without a quote, and Scale AI's self-serve tier (a small free allowance of labeling units and images) only covers small pilots — anything at real volume goes through an enterprise conversation. If you're unsure which model fits, a useful test is to price out what your own reviewers' time would cost against Scale AI's self-serve pricing on a representative sample before committing either way. See choosing a data labeling tool for the broader build-vs-outsource decision.