Sold by: Datasaur
Deployed on AWS
Data Studio is the most intuitive annotation platform on the market, enabling annotators to seamlessly label data sets at scale, through automation or manual work or human-in-the-loop methods.
4.4
Overview
Datasaur is a comprehensive data labeling platform for Natural Language Processing (NLP) - part of the data pipeline behind Datasaur's private AI deployments.
Datasaur helps machine learning teams better manage their labeling workforce and improve the quality of their training data. Our best-in-class software comes with ML-automated labeling and workforce management features, giving you the tools you need to generate higher quality data, greater visibility into your team's productivity, and significant cost and time savings. On average, our clients have reduced time and/or spend on AI projects by over 70%
Labeling projects supported:
- named entity recognition (NER)
- part of speech labeling
- coreference resolution
- dependency parsing
- document classification
- data extraction
- optical character recognition (OCR)
- transcription
Common use cases supported:
- medical note transcription
- legal document analysis
- banking document analysis
- receipt and invoice understanding
- customer service call transcripts
- business contract understanding
- misinformation detection
- direct message and forum moderation
- product review summarization
All languages, SMEs, and specialties are supported. Reach out for a demo at demo@datasaur.ai
Highlights
- Highly intuitive interface for NLP labeling hosted in the cloud or on-premise.
- Build custom AI solutions with the best-in-market LLMs and annotation tools for automation to reduce people time and costs by over 70%
- Full service workforce management and review tooling that allows teams to track and monitor progress.
Details
Sold by
Delivery method
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Growth tier | 1 workspace, 10 users, up to 250,000 labels, API access, Advanced Analytics, ML-assisted Labeling, Label Error Detection, Predictive Labeling, Data Programming, Datasaur Dinamic | $50,000.00 |
Dimensions summary
You buy the Growth tier as a single contract with a fixed set of capacity limits. This one bundle covers 1 workspace, up to 10 users, and up to 250,000 labels. Access to API, Advanced Analytics, ML-assisted Labeling, Label Error Detection, Predictive Labeling, Data Programming, and Datasaur Dinamic is included at this tier. Pricing does not scale by usage or add-ons. Instead, you commit to the tier and its capped quantities. If your team or label volume grows beyond these limits, you would move to a different arrangement.
Top-of-mind questions for buyers
A label is a single annotation you apply to a data point during a labeling task. Each tag on a piece of text counts separately. The Growth tier caps your total at 250,000 labels across your workspace. Automated and manually applied labels both count toward this figure.
The Growth tier is a fixed contract with capped quantities. It does not scale automatically or add overage charges. Once you reach 1 workspace, 10 users, or 250,000 labels, you cannot exceed those caps under this tier. You would move to a different arrangement with the vendor.
All listed capabilities come bundled in the single Growth tier contract at no extra charge. This includes API access, Advanced Analytics, ML-assisted Labeling, Label Error Detection, Predictive Labeling, Data Programming, and Datasaur Dinamic. There are no per-feature add-ons or usage-based charges layered on top.
datasaur.ai
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
24/7 support with 24 hour turnaround time support@datasaur.ai
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Customer reviews
pankaj r.
User-Friendly, But Lags with Complex Data
Reviewed on Sep 03, 2026
Review provided by G2
What do you like best about the product?
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.
What do you dislike about the product?
I don't like that Datasaur is lagging with large datasets, especially when I'm dealing with complex annotations and queries. It slows down the process and can be frustrating.
What problems is the product solving and how is that benefiting you?
I use Datasaur to create documents from natural language, which saves me time compared to manual work.
Anirudh C.
Powerful Annotation for Large Text Datasets with Flexible Guidelines
Reviewed on Sep 03, 2026
Review provided by G2
What do you like best about the product?
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 categories.
What do you dislike about the product?
Checking complicated annotations may become monotonous if the dataset contains lots of similar cases. Also, it takes some time to define the right approach to labeling such cases with uncommon wording.
What problems is the product solving and how is that benefiting you?
The tool makes it simpler to convert qualitative data into structured information which will be suitable for comparative analysis. Instead of maintaining classifications separately, it will be possible to base them on the results of annotation.
Ajay P.
Datasaur Makes Qualitative Analysis Easy with Consistent Labels
Reviewed on Sep 02, 2026
Review provided by G2
What do you like best about the product?
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.
What do you dislike about the product?
The quality of the resulting output depends mostly on the quality of the annotation scheme used. When there are many ideas expressed in a single comment, it is still necessary to make a lot of manual work in terms of labeling.
What problems is the product solving and how is that benefiting you?
It allows minimizing efforts needed to transform unstructured feedback into structured datasets. In other words, it makes it easier to distinguish between themes, compare different groups of responses, and organize qualitative information for future product analysis without manual keeping of huge tables of classification.
Recommendations to others considering the product:
To improve the annotation process, consider using a more detailed and structured annotation scheme. Additionally, providing training for annotators can help ensure consistency and accuracy in labeling.
Vishant J.
Datasaur Makes Categorizing Customer Feedback Easy and Systematic
Reviewed on Aug 31, 2026
Review provided by G2
What do you like best about the product?
Datasaur has managed to prove its usefulness through aiding in the categorization of large amounts of customer-related data into specific categories which can then be analyzed in a systematic fashion. The tool enables teams to structure their feedback, support, and qualitative responses, thus making it easy for them to recognize themes.
What do you dislike about the product?
The variety of language used by customers can be very wide-ranging, which might make it difficult to assign messages to only one neat category. The construction of a useful categorization scheme will need some effort, especially when dealing with weird or exceptional cases.
What problems is the product solving and how is that benefiting you?
The method helps to turn customer qualitative data into useful data. Teams will be able to spot patterns in large sets of data and utilize them by prioritizing certain recurrent problems instead of working on random customer feedback.
Swastik C.
Bringing Structure to Your Annotation Workflows Without Reducing Your Team’s Speed.
Reviewed on Aug 31, 2026
Review provided by G2
What do you like best about the product?
Datasaur comes in handy when your annotation work becomes large-scale or too complicated to perform manually. It provides labeling guidelines, allows using several people for labeling the same project and model-based suggestions to facilitate the labeling of repetitive data. The human-reviews process plays a key role in automation as it enables us to control the quality of our data.
What do you dislike about the product?
The core workflow is simple to start with, but managing lots of labels, reviewers, and quality policies gets complicated. Datasets of large sizes require time to be processed, and new members of the team require some time to learn about advanced options.
What problems is the product solving and how is that benefiting you?
It substitutes the dispersed annotation tables and manual work with the unified labeling workflow that ensures the consistency of annotations, reveals inconsistencies between reviewers and produces clean data for NLP and ML projects.