Datasaur
Reviews
Star rating
Rank in Data Labeling
Pros and cons
Pros
Cons
Traffic & LLM citations
Source: G2 review data
Winning from
Key stats
Adoption by company size
| Segment | Share of reviews |
|---|---|
| Small Business | 35.3% |
| Mid-Market | 36.5% |
| Enterprise | 28.2% |
Quarterly history
| Quarter | Reviews | Star rating |
|---|---|---|
| 2026 Q2 | 2 | 4.5 |
| 2026 Q3 | 53 | 4.4 |
Satisfaction ratings
What reviewers say
User-Friendly, But Lags with Complex Data
“I like how Datasaur is user-friendly, which makes it easy for me to navigate. I also appreciate that it saves me time compared to doing manual work. The initial setup was easy, which was a nice surprise.”
Powerful Annotation for Large Text Datasets with Flexible Guidelines
“Datasaur is especially valuable in dealing with large-scale text sets which differ slightly by meaning. It is nice that one can create sophisticated annotation guidelines and study specific samples. This way I can discern similar topics without grouping them into overly general c”
Datasaur Makes Qualitative Analysis Easy with Consistent Labels
“Datasaur is useful in giving a framework to qualitative information related to products. I like the opportunity to use specific labels for certain types of feedback and apply them consistently because it makes it much easier to search for patterns in a large text collection.”