Do any of these vendors do both synthetic data generation and labeling of real data?

Not distinctly, based on Crail's data. Mostly AI and Gretel are positioned around synthetic data generation; Scale AI, Labelbox, Snorkel AI, and Surge AI are positioned around labeling, RLHF, and curating real data. Check which problem you actually have before shortlisting a vendor from this category.

Is agent-readiness generally lower in this category than in other software categories?

Yes, on Crail's data. None of the six vendors tracked here publish an MCP server, and agent-readiness scores range from 14 to 47 — noticeably lower than most other Crail categories, where several vendors clear 70-80+. This is a category still built around human/sales workflows rather than agent-driven self-serve.

Choosing a synthetic data or labeling vendor

"Synthetic data" and "data labeling" get lumped into one category, but they solve different problems — generating data that doesn't exist yet versus annotating data you already have — and most vendors here do one, not both.

“Synthetic data and labeling” sounds like one category, but it covers two different jobs: generating realistic data that doesn’t correspond to any real record (useful when real data is scarce, sensitive, or imbalanced), and labeling or curating data you already have (useful for supervised fine-tuning, RLHF, and eval sets). Sorting vendors by which job they actually do is the first filter, before price or compliance.

Generation vs. labeling — check which one you need

Mostly AI and Gretel are both positioned around synthetic data generation — creating new, statistically representative data rather than annotating existing records. Gretel was acquired by NVIDIA in March 2025 and is now folded into NVIDIA’s NeMo stack, per Crail’s data, which is worth knowing if you’re evaluating it as a standalone vendor versus part of a larger NVIDIA commitment.

Scale AI, Labelbox, Snorkel AI, and Surge AI are all built around labeling, RLHF pipelines, and curating real data for training or evaluation — Snorkel AI specifically uses a programmatic “labeling functions” approach rather than purely manual annotation. If your problem is “I have raw data that needs ground-truth labels,” these four are the relevant set, not the synthetic-data pair.

Buying motion varies more than the feature set

Mostly AI and Scale AI both offer a free tier, per Crail’s data — useful for testing before committing. Scale AI is buyable without a sales call; Mostly AI’s free tier is self-serve but a sales call is required to move past it. Labelbox, Snorkel AI, Surge AI, and Gretel are all sales-call-only with no published pricing, which is standard for this category’s larger enterprise contracts (Surge AI in particular serves OpenAI, Anthropic, Google, and Meta directly rather than through self-serve signup).

Deployment flexibility if data can’t leave your environment

If your data can’t leave your own infrastructure, that narrows the field further: Mostly AI and Snorkel AI both offer self-hosted and on-prem deployment options alongside cloud, per Crail’s data. The other four vendors here (Scale AI, Labelbox, Surge AI, Gretel) are cloud-only.

Where to start

Full pricing, deployment options, and compliance details for all six vendors are on the Synthetic Data & Data Labeling category page, or see a direct comparison in Mostly AI vs Scale AI.