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 HITL methods.
4.4
Overview
Datasaur is a comprehensive data labeling platform for Natural Language Processing (NLP).
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
- Optimized labeling interface for NLP labeling hosted in the cloud or on-premise
- Full-fledged workforce management and review tool that allows team leads to track and monitor their team's progress
- Built-in intelligence and a comprehensive API allows you to automate the basics away and label over 80% faster
Details
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Delivery method
Supported services
Delivery option
Datasaur Data Studio Helm Charts
Latest version
Operating system
Linux
Deployed on AWS
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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 |
|---|---|---|
Enterprise tier | 1 workspace, 50 users, up to 1 million labels, API access, Advanced Analytics, ML-assisted Labeling, Label Error Detection, Predictive Labeling, Data Programming, Datasaur Dinamic, SAML and SCIM integration, Enterprise-grade compliance and security, Dedicated support | $250,000.00 |
Dimensions summary
This listing offers one pricing option: the Enterprise tier, billed as a contract. You buy it in units, and each unit covers 1 workspace, up to 50 users, and up to 1 million labels. Pricing scales by how many units you purchase, so larger teams or higher label volumes require more units. The tier includes API access, ML-assisted labeling, analytics, SAML and SCIM integration, compliance and security features, and dedicated support. Because there is a single tier, there are no lower or higher levels to choose between—you scale by adding units.
Top-of-mind questions for buyers
A label is one annotation applied to a piece of data during a labeling project. The unit allows up to 1 million such labels across your workspace. If your project needs more labels, you add units to raise the total limit.
Each unit covers 1 workspace, 50 users, and up to 1 million labels. To go beyond any of these caps, you purchase more units. There is no separate overage rate; you scale by adding whole units rather than paying per extra user or label.
Dedicated support is part of the Enterprise tier and comes with each unit you buy. You do not pay extra for it. Compliance and security features, API access, and analytics are also included in the same unit rather than sold as add-ons.
datasaur.ai
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Delivery details
Datasaur Data Studio Helm Charts
Supported services: Learn more
- Amazon EKS
Helm chart
Helm charts are Kubernetes YAML manifests combined into a single package that can be installed on Kubernetes clusters. The containerized application is deployed on a cluster by running a single Helm install command to install the seller-provided Helm chart.
Version release notes
Additional details
Usage instructions
The app will be installed using Kubernetes with Helm Chart and can be seamlessly deployed on top of EKS (Amazon Elastic Kubernetes Service). After provisioning all necessary services and setting up environment variables, simply execute the helm install command. For detailed instructions, please refer to our guide on the GitBook page: https://docs.datasaur.ai/deployment/self-hosted
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
Nidhi R.
Datasaur Streamlines Data Labeling Coordination with Clear Roles and Progress Tracking
Reviewed on Aug 27, 2026
Review provided by G2
What do you like best about the product?
The strengths of Datasaur lie in its ability to deal with administrative coordination within the context of data labeling projects. It can assist in handling the distribution of responsibility for various tasks, tracking responsibilities of the reviewers, monitoring progress, and ensuring that all necessary data is properly routed through the necessary steps. All role assignments and project reports being located in the same space means that there is a predictability in coordination processes.
What do you dislike about the product?
The administrative side of the process can become more complicated in the case of a number of reviewers, complex labeling rules, or several QA checks throughout the project.
What problems is the product solving and how is that benefiting you?
First of all, it is high levels of visibility in the progress of the project. Having access to the progress reports and performance indicators allows to identify delays in the process, see how the work is being distributed, and coordinate actions with the reviewers.
Balaji S.
Well-Defined Annotation Workflow with Handy Bulk Labeling
Reviewed on Aug 27, 2026
Review provided by G2
What do you like best about the product?
Datasaur is particularly beneficial when it comes to a well-defined workflow of data project from the initial annotation stage through review all the way to the export stage. It allows structuring labeling instruction, facilitating coordination between reviewers and maintaining a single point of record for disagreements instead of relying solely on individual conversations. Bulk labeling feature becomes handy when there are recurring patterns in large datasets.
What do you dislike about the product?
Coordination efforts may be elevated in case there are large taxonomies or multiple review stages used in the project. It is crucial to have reviewers understand the logic of labeling instructions to produce consistent results, therefore, in case of changes in project instructions, communication and additional quality control will be required.
What problems is the product solving and how is that benefiting you?
Datasaur provides better visibility into which parts of labeling workflow go well and which require intervention. By analyzing inter-annotator agreement and statistics about the team, coordinators will be able to find issues or discrepancies that need to be addressed by focusing on certain parts of workflow instead of manual inspection.
Yash R.
Centralized Project Oversight for Data Labeling Workflows
Reviewed on Aug 27, 2026
Review provided by G2
What do you like best about the product?
Datasaur appears to be a useful tool to manage the operational aspects of data-labeling projects. I am able to get an idea on the progress of the project, coordinate the activity of reviewers, keep an eye on quality metrics, and make sure that all labeling processes comply with necessary structures. By bringing all project details and reviewer activity into one place, it becomes possible to identify any issues and flaws in the workflow.
What do you dislike about the product?
In order to manage the operational process of data labeling, a lot of efforts still have to be put into defining rules of labeling and reviewing the results. When dealing with projects containing large taxonomies or various kinds of annotations, managing the workflow might become more complicated due to different interpretations of the same data by different reviewers.
What problems is the product solving and how is that benefiting you?
The biggest advantage provided by this solution is the increase in visibility in the process of data labeling and review. Metrics of quality, activity of the reviewers, resolving conflicts, and project reports allow to detect all inconsistencies and pay special attention to them.
Sushant S.
Datasaur Keeps Annotation Quality High with Clear Progress Tracking and Review Insights
Reviewed on Aug 26, 2026
Review provided by G2
What do you like best about the product?
Datasaur stands out in situations when the successful service delivery is highly dependent on maintaining a high level of data quality in several annotation projects. This tool helps me track labeling progress, identify points of disagreement between the reviewers and use overall project insights to solve any quality problems before they affect AI processing. A unified review system allows easier coordination of efforts between distributed teams as well.
What do you dislike about the product?
In complex cases, considerable coordination effort is required when taxonomies, reviewers and quality expectations do not align. I will also need to ensure that each team understands the labeling guidelines thoroughly, as the automated solutions can’t make up for the lack of clarity in project requirements.
What problems is the product solving and how is that benefiting you?
The main benefit of using this tool is increased visibility of the delivery process and data quality. Metrics like inter-annotator agreement, continuous labeler tracking, review processes and audits will reveal bottlenecks early and give service teams an opportunity to use the evidence when managing project performance.
Priyanshu R.
Datasaur Streamlines Large-Scale Labeling With Flexible, Configurable Workflows
Reviewed on Aug 26, 2026
Review provided by G2
What do you like best about the product?
Datasaur becomes very useful when operating teams manage projects that are based on large amounts of unstructured data. It helps to manage labeling efforts, distribute tasks, monitor progress, and perform quality checks without using numerous spreadsheets or tracking systems. Configurable workflows become very convenient, as they are designed to fit the diverse review processes needed for each particular project.
What do you dislike about the product?
Setting up a complex project may take some preliminary preparation, especially if several types of labels, reviewers, and approval stages are used. Some teams that have no previous experience with annotation workflows may need additional time to understand the right way of project setup.
What problems is the product solving and how is that benefiting you?
The main advantage is increased operational visibility within data preparation projects. By monitoring progress and performing quality checks at the labeler level, one can detect possible problems early enough and resolve them.