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.
4.5
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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30-day refund available, guaranteed
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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
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.
Ragini C.
Datasaur Makes Structured Annotation Consistent and Efficient
Reviewed on Aug 25, 2026
Review provided by G2
What do you like best about the product?
Datasaur is particularly helpful when working with structured data-review assignments that require maintaining consistency. Using Datasaur, I am able to work with structured annotation projects with well-defined labeling schemes, quickly find information within large-scale data sets, and label the same patterns across various records.
What do you dislike about the product?
Sometimes, detailed labeling projects can be complicated because of the complexity or regular changes in the classification rules. To avoid inconsistencies in treatment of similar records, a lot of attention must be paid to the project instructions.
What problems is the product solving and how is that benefiting you?
The main advantage is the ability to work in a more consistent manner at the stage of data preparation and review. The quality control feature helps identify inconsistencies between reviewers, and the automated/bulk labeling functionality allows reducing the load of repetitive manual tasks. This way, I can pay my attention to those records that require my attention.
Vivaan K.
Datasaur Streamlines ML/NLP Annotation with Flexible Schemas and Model-Assisted Labeling
Reviewed on Aug 24, 2026
Review provided by G2
What do you like best about the product?
I find Datasaur useful to prepare data sets that are going to be used for machine-learning and NLP processes. This allows me to create annotation schemas, to use various formats, and to implement standardized annotation procedures rather than using fragmented manual approaches. The ability to have some assistance from a model in my workflow becomes very useful when working with large data sets because this will not decrease quality but will simplify the labeling procedure.
What do you dislike about the product?
With more complex annotation tasks, the planning is sometimes necessary prior to implementing the labeling procedure because I need to define taxonomies and project rules, as well as review automatic label suggestions, in order to ensure the necessary quality of the training data.
What problems is the product solving and how is that benefiting you?
The main advantage of using Datasaur is that this product helps to reduce the time spent on the preparation of the machine-learning dataset since engineers will be able to automate part of the annotation procedure, control the quality, and check the consistency of the annotations.
Christy D.
Easy Onboarding and a Straightforward System
Reviewed on Aug 20, 2026
Review provided by G2
What do you like best about the product?
The onboarding process was easy. It’s a good system and straightforward to use.
What do you dislike about the product?
The pricing for the basic setup is higher than I would like.
What problems is the product solving and how is that benefiting you?
It was easy to integrate with other systems.