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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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
ALISHETTI S.
Intuitive, Collaborative Data Annotation That Boosts Productivity
Reviewed on Aug 07, 2026
Review provided by G2
What do you like best about the product?
What I like best about Datasaur is its intuitive interface, efficient data annotation workflow and strong collaboration features. The platform handles large datasets smoothly, supports multiple annotation types and significantly improves productivity while maintaining high annotation quality and consistency.
What do you dislike about the product?
One thing is that some advanced features take time to learn, and occasional performance slowdowns can occur with very large datasets, more options and shortcuts would make better experience
What problems is the product solving and how is that benefiting you?
Datasaur simplifies complex data processing annotations by centralizing labeling, team collaboration in a single platform and it saves time, improves accuracy, reduces manual effort and helps complete AI training projects more efficiently.
Computer Software
Datasaur Streamlined Our NLP Labeling Workflow and Collaboration
Reviewed on Aug 07, 2026
Review provided by G2
What do you like best about the product?
What stands out most about Datasaur is how much it has improved the way I manage NPL data labeling projects. It has streamlined my workflow, making complex annotation tasks faster and more organized. The platform has also strengthened my ability to collaborate with international teams and handle large projects with confidence. Overall, its reliable performance and intuitive labeling tools help me achieve better results while saving valuable time.
What do you dislike about the product?
The biggest challenge I’ve had with Datasaur is the initial learning curve. Because it offers a wide range of features and customization options, new users may need extra time or proper guidance before they can use the platform efficiently. A more beginner-friendly onboarding experience, along with interactive tutorials, would make it much easier for first-time users to get started and feel confident using the tool.
What problems is the product solving and how is that benefiting you?
Datasaur solves the challenge of managing and annotating large volumes of text data accurately for NLP and machine learning projects. It offers a centralized platform that helps keep labeling consistent, improves team collaboration, and speeds up project completion. As a result, I can deliver high-quality datasets more efficiently, reduce manual effort, and focus on developing better AI models instead of spending excessive time on data preparation.
Shikhar Y.
Secure, Easy-to-Use AI Workspace for Sensitive Data
Reviewed on Aug 07, 2026
Review provided by G2
What do you like best about the product?
The feature I appreciate most about Datasaur is its strong commitment to protecting sensitive business data while still enabling AI-powered productivity. It offers a secure workspace where my team can work with internal documents and use AI tools without constantly worrying about data privacy or unauthorized access. I also find the platform easy to navigate, which makes it straightforward to integrate AI into our day-to-day workflow while keeping full confidence in the security of our confidential information.
What do you dislike about the product?
One drawback is that the pricing may be challenging for smaller organizations or teams with limited budgets. Also, integration with external applications could be smoother, since some third-party connections require additional setup steps and administrative permissions.
What problems is the product solving and how is that benefiting you?
Datasaur addresses the challenge of using AI securely within organizations that handle confidential information. It allows our team to leverage AI capabilities in a protected environment, rather than relying on public AI platforms, which helps us stay compliant with internal security policies. As a result, our efficiency has improved, concerns about data privacy have decreased, and AI has become a trusted part of our day-to-day operations.
Shubham V.
Datasaur’s Flexible Annotation Workspaces Fit Seamlessly into Our Pipeline
Reviewed on Aug 07, 2026
Review provided by G2
What do you like best about the product?
What I appreciate most about Datasaur is how flexible it is across different types of annotation work. We have used it for everything from standard NER projects to more involved document parsing and LLM fine-tuning and it adjusts well to each use case instead of making us change our workflow. Being able to tailor the workspace around the needs of a specific dataset is a huge advantage and the export formats fit neatly into our existing backend pipeline without forcing us to write a lot of extra parsing logic.
What do you dislike about the product?
For everyday NLP datasets the platform performs well, but very large datasets or lengthy documents can make the interface feel less responsive. Loading multi-megabyte files or navigating dense documents with many annotation layers sometimes introduces small delays while scrolling or interacting with the text. The interface itself is well organized, although creating complex nested entity schemas takes some time for new annotators to understand. The export process works reliably, but we still occasionally need custom post-processing scripts when preparing data for non-standard machine learning formats. I have also noticed that selecting precise character offsets for overlapping entities in complicated NER tasks can require multiple attempts. Improving export flexibility and making text selection more precise would make the overall workflow much smoother.
What problems is the product solving and how is that benefiting you?
Datasaur mainly helps us eliminate one of the biggest bottlenecks in NLP development, which is manual data labeling. Preparing quality training data often takes up a significant portion of the overall project timeline, but having everything managed in one place along with automated pre-labeling cuts that effort down considerably. As a result, our team spends far less time dealing with repetitive annotation work, scattered spreadsheets, or maintaining internal labeling tools and we can dedicate more effort to model development and deployment. That has helped us move new AI features into production much faster.
Kishan T.
Clean, Intuitive Interface That Speeds Up Labeling and Team Collaboration
Reviewed on Aug 06, 2026
Review provided by G2
What do you like best about the product?
The interface is clean and intuitive, so I can get started quickly without spending much time figuring things out. It also makes labeling and reviewing data noticeably faster. The collaboration features are especially helpful when I’m working with a team on the same project, since it’s easier to stay aligned and keep everything moving.
What do you dislike about the product?
One thing I don’t like is that some of the more advanced features take a while to learn. The platform can also feel a bit slow when I’m working with very large datasets, and in those situations I’d really like to see smoother performance along with more customization options.
What problems is the product solving and how is that benefiting you?
Datasaur helps me organize and label data more efficiently, which saves time and cuts down on manual effort. It also makes collaboration on annotation tasks easier, so projects stay better organized and the overall workflow feels smoother and more consistent.