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
Juhi P.
Streamlined Data Annotation with Collaboration Ease
Reviewed on Aug 30, 2026
Review provided by G2
What do you like best about the product?
I like how straightforward the annotation workflow is in Datasaur. It's easy to upload a dataset, define the labels, and start working without much setup. The collaboration and review features are especially useful, allowing multiple people to work on the same project and keep the labeling consistent. It saves a lot on manual coordination. I also appreciate the review and quality control features, which make it easier to spot and correct inconsistent labels before the dataset progresses. The interface is fairly clean, making it simple for new team members to understand the workflow without much training. This has really helped in managing annotation projects as our workload increases. The initial setup was quite easy, and we could get started on a project with minimal technical effort. Once we set up the labeling guidelines and workflows, the team picked it up quickly. It's made our annotation and review process much more organized than our previous manual approach.
What do you dislike about the product?
One area that could be improved is handling very large annotation projects. As the dataset grows, managing labels, reviewing edge cases, and keeping everything organized can take some extra effort. I'd also like more flexibility in customizing workflows and quality checks, especially for projects with more complex annotation rules. The platform works well overall, but those improvements would make larger projects easier to manage.
What problems is the product solving and how is that benefiting you?
I find Datasaur organizes our large-scale data annotation work and maintains consistent labeling. It's straightforward for annotation, and collaboration features save manual coordination. I need more flexibility for complex workflows and better handling of large datasets, but it has been a useful tool for our team.
Hitesh K.
Systematic Labeling Criteria That Helps Catch Annotation Inconsistencies
Reviewed on Aug 30, 2026
Review provided by G2
What do you like best about the product?
Datasaur turns out to be a great way to facilitate the organization of human-reviewing of datasets for intelligent systems. I really like the possibility to set up labeling criteria, to see individual examples and to compare how they were reviewed by different people. It means that there is a systemized and repeatable way to catch the inconsistencies in the annotation before applying the dataset further.
What do you dislike about the product?
Technical datasets sometimes contain some examples which cannot be consistently labeled. Preparing the guidelines for annotation takes much time and even after preparing them, some examples might require discussion between reviewers because they do not fit into the current labeling scheme.
What problems is the product solving and how is that benefiting you?
It provides a way to conduct an additional quality control stage of data processing. Reviewing inconsistent annotations and controlling the consistency of the process helps to avoid using incorrect examples in a dataset which will be then used by engineering systems.
Raj K.
A Valuable Space for Building Annotation Schemes and High-Quality Labeled Data
Reviewed on Aug 30, 2026
Review provided by G2
What do you like best about the product?
Datasaur demonstrates its usefulness when the quality of labeled data is crucial for the operation of the application. It is valuable to have a designated space for working on annotation schemes and making changes in them before data becomes available for the application to use. In this way, there is a clear separation of training data preparation from the feature development that uses the data.
What do you dislike about the product?
An annotation project might get complicated if the dataset contains various categories and edge cases. The change of label definitions might require coordination with the teams that will use the data, so the well-thought-out workflow becomes necessary.
What problems is the product solving and how is that benefiting you?
Consistency of annotations allows creating more predictable features in the future. Review process is helpful to find inconsistencies and correct errors in the dataset beforehand.
Sumeet S.
Easy-to-use platform for efficient data annotation
Reviewed on Aug 29, 2026
Review provided by G2
What do you like best about the product?
What I find most helpful about Datasaur is how much it streamlines the entire data labeling and annotation process, making it both easier and faster. The interface is straightforward to work with, and the AI-assisted labeling features cut down on a lot of repetitive manual tasks.
I also appreciate that it supports different types of annotation workflows, which makes it simpler for teams to collaborate while keeping data quality consistent. Overall, the biggest upside for me is the time saved, along with having better control over the quality and organization of the data.
I also appreciate that it supports different types of annotation workflows, which makes it simpler for teams to collaborate while keeping data quality consistent. Overall, the biggest upside for me is the time saved, along with having better control over the quality and organization of the data.
What do you dislike about the product?
The main thing I find less helpful is that some of the more advanced features can take a little time to understand, especially if you're new to data annotation tools. There can also be a bit of a learning curve when setting up more complex workflows. For smaller or simpler projects, some of the advanced functionality may feel like more than what is actually needed.
Overall, though, these are relatively minor downsides compared with the time it saves on larger annotation projects.
Overall, though, these are relatively minor downsides compared with the time it saves on larger annotation projects.
What problems is the product solving and how is that benefiting you?
Datasaur helps solve the problem of managing and labeling large amounts of data efficiently. Instead of doing everything manually, it makes the annotation process more organized and helps reduce the time and effort needed to prepare high-quality training data. It also helps keep the labeling consistent across the team, which is useful when working with large datasets or AI/ML projects.
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.