Comparison
Scale AI vs Sama: which fits Indian language data?
A straight comparison of Scale AI and Sama for Indian-language AI data work, written from a procurement point of view rather than a marketing one.
- Scale AI
- LLM alignment and vision labelling rather than original speech collection.
- Sama
- Buyers whose primary criterion is auditable ethical labour practice.
- Shared gap
- Depth in Indian dialects and studio recruitment
- Decision driver
- Scale versus per-language depth
Side by side
| Scale AI | Sama | |
|---|---|---|
| Positioning | Annotation and model-evaluation platform with a strong vision and LLM focus. | Impact-sourcing annotation provider with East African and South Asian delivery. |
| Main strength | Engineering-grade tooling, RLHF and evaluation workflows. | Strong ethical-sourcing story and stable annotation teams. |
| Gap for Indian data | Not built around field recruitment of Indian speakers or studio speech capture. | Focus is annotation of existing data rather than original speech collection in India. |
| Best fit | LLM alignment and vision labelling rather than original speech collection. | Buyers whose primary criterion is auditable ethical labour practice. |
When Scale AI is the right call
Annotation and model-evaluation platform with a strong vision and LLM focus.
Choose them when llm alignment and vision labelling rather than original speech collection. describes your programme more accurately than deep per-language work in India does.
When Sama is the right call
Impact-sourcing annotation provider with East African and South Asian delivery.
Choose them when buyers whose primary criterion is auditable ethical labour practice. is the dominant requirement.
Where both tend to struggle in India
- Dialect quotas: an Indian language is not one cohort, and a general contributor pool will silently fill quotas with the easiest urban speakers
- Native review: transcription QA needs reviewers who speak the variety, not a generic language reviewer
- Studio access outside metros: rural and small-town speakers rarely come to a metro studio
- Consent under Indian law: DPDP-aligned consent records are a specific artefact, not a generic form
- Account layers: a single-language corpus can wait behind a global account structure
Where we fit
We are not a global platform and do not pretend to be. We run Indian-language collection through a nationwide partner studio network with native reviewers per language, designed cohorts and consent records built for Indian law.
If your programme spans twenty countries, one of the vendors above is a better answer. If the hard part is Indian dialects, speaker recruitment and transcription that survives code-mixing, that is the only thing we do.
Frequently asked
Is Scale AI or Sama better for Indian speech data?
Scale AI suits llm alignment and vision labelling rather than original speech collection.; Sama suits buyers whose primary criterion is auditable ethical labour practice.. For depth in a specific Indian language, both are usually routed through general capacity rather than dedicated Indian recruitment.
Can we use more than one vendor?
Commonly, yes. Global vendors carry breadth across markets while a specialist carries the Indian-language corpora. Keep the specification and QA standard identical across both.
How do we compare quotes fairly?
Fix the specification first — cohort design, condition mix, annotation depth, acceptance thresholds — then send the same document to everyone. Quotes that assume different specs are not comparable.
What should we ask for before deciding?
A free sample recorded to your spec, the QA report format, the consent artefact, and who exactly does native review for your language.
Related pages
Run us against your shortlist
Send the same specification you sent everyone else. You get a fixed price, a schedule and a free sample to compare on.