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
aman g.
Datasaur Makes Data Labeling Easy and More Organized
Reviewed on Aug 14, 2026
Review provided by G2
What do you like best about the product?
I like that Datasaur is easy to use and helps with data labeling. It saves time and makes the data work more organized and simple.
What do you dislike about the product?
Sometimes it can be a little confusing to use, and some features could be more simple. It can also take some time to get used to.
What problems is the product solving and how is that benefiting you?
Datasaur helps us with data labeling and makes the process faster. It saves time and helps keep the data more organized and easier to manage.
Computer Software
Datasaur Makes Collaborative, ML-Assisted Labeling Fast and Flexible
Reviewed on Aug 13, 2026
Review provided by G2
What do you like best about the product?
What I like best about Datasaur is how it makes data labeling less painful and way more collaborative.
My top 3 things about Datasaur:
1. Collaboration is smooth
Multiple annotators can work on the same dataset, with disagreements tracked and resolved. No more messy spreadsheets or "which version is final" drama. It’s built for teams.
2. ML-assisted labeling
It uses models to suggest labels while you annotate. So you label 100 examples, it learns, and starts pre-labeling the next 1000. Cuts annotation time massively.
3. Works for all kinds of data
Text, images, documents, PDF contracts, NER, classification, QA pairs — you name it. The interface adapts and you can set up custom workflows + quality checks inside it.
My top 3 things about Datasaur:
1. Collaboration is smooth
Multiple annotators can work on the same dataset, with disagreements tracked and resolved. No more messy spreadsheets or "which version is final" drama. It’s built for teams.
2. ML-assisted labeling
It uses models to suggest labels while you annotate. So you label 100 examples, it learns, and starts pre-labeling the next 1000. Cuts annotation time massively.
3. Works for all kinds of data
Text, images, documents, PDF contracts, NER, classification, QA pairs — you name it. The interface adapts and you can set up custom workflows + quality checks inside it.
What do you dislike about the product?
1. Pricing gets steep for big teams
For solo/small teams it’s okay. But once you scale to 10+ annotators + lots of documents, the cost jumps. Free tier is also pretty limited.
2. Learning curve for complex workflows
Basic labeling is easy. But if you want custom ontologies, multi-stage reviews, agreement metrics, and automation rules — setup takes time. New users often get lost in all the settings.
3. UI can feel heavy sometimes
When datasets get huge or you’re labeling 50-page PDFs, the platform can lag. And searching/filtering through thousands of labeled items isn’t as fast as I’d like.
For solo/small teams it’s okay. But once you scale to 10+ annotators + lots of documents, the cost jumps. Free tier is also pretty limited.
2. Learning curve for complex workflows
Basic labeling is easy. But if you want custom ontologies, multi-stage reviews, agreement metrics, and automation rules — setup takes time. New users often get lost in all the settings.
3. UI can feel heavy sometimes
When datasets get huge or you’re labeling 50-page PDFs, the platform can lag. And searching/filtering through thousands of labeled items isn’t as fast as I’d like.
What problems is the product solving and how is that benefiting you?
Problem: Before, labeling data for AI meant spreadsheets, Google Docs, or building your own tool. 1 person labels 200 examples/day, and quality is all over the place.
How Datasaur helps: ML-assisted labeling. You label 200, the model learns, and it pre-labels the next 2000. My speed goes up 5x-10x.
Problem: 5 people labeling same dataset = different formats, disagreements, no tracking who did what.
How Datasaur helps: Built-in collaboration + disagreement resolution + agreement scores. Project manager can assign, review, and audit everything
How Datasaur helps: ML-assisted labeling. You label 200, the model learns, and it pre-labels the next 2000. My speed goes up 5x-10x.
Problem: 5 people labeling same dataset = different formats, disagreements, no tracking who did what.
How Datasaur helps: Built-in collaboration + disagreement resolution + agreement scores. Project manager can assign, review, and audit everything
Darpan T.
Datasaur Makes Data Labeling Organized, Efficient, and Team-Friendly
Reviewed on Aug 13, 2026
Review provided by G2
What do you like best about the product?
What I like most about Datasaur is that it makes the data labeling and annotation process much more organized and efficient. The interface is straightforward, and it is easy to review, label, and manage large amounts of data without making the workflow unnecessarily complicated. I also like the collaboration features, which make it easier for teams to work consistently on annotation projects.
What do you dislike about the product?
The main thing I dislike about Datasaur is that some advanced features can take a little time to understand, especially for new users. The interface can also feel slightly overwhelming when working with complex annotation projects or large datasets. A more streamlined experience for beginners and clearer guidance for advanced features would make it easier to get started.
What problems is the product solving and how is that benefiting you?
Datasaur simplifies the process of labeling and organizing large datasets, which can otherwise be time-consuming and difficult to manage manually. It provides a structured workspace for annotation, review, and collaboration, helping reduce repetitive work and maintain consistency across projects. This makes the overall data preparation process faster and helps me work more efficiently with datasets used for AI and machine learning.
LOKESH G.
Datasaur Makes Data Labeling Simple and Efficient
Reviewed on Aug 12, 2026
Review provided by G2
What do you like best about the product?
I like Datasaur the most because it makes data labeling and annotation simple and efficient. The interface is intuitive, and the tools for managing, reviewing, and organizing datasets help streamline the process and speed up AI and machine-learning workflows.
What do you dislike about the product?
One thing I dislike about Datasaur is that some of the more advanced features can take a while to understand, especially for new users. I also think the platform could improve its customization options and make certain workflows feel more intuitive and straightforward.
What problems is the product solving and how is that benefiting you?
Datasaur helps me tackle the challenge of **managing and labeling large amounts of data for AI and machine-learning projects**. It streamlines annotation and keeps data organized, making the overall workflow more efficient. As a result, I save time, maintain better data quality, and can prepare more reliable datasets for training and evaluating AI models.
Apoorv T.
Datasaur’s Intuitive Interface and Powerful AI-Assisted Labeling
Reviewed on Aug 12, 2026
Review provided by G2
What do you like best about the product?
I personally love Datasaur’s interface, and I like that it supports LLMs and GenAI. On top of that, the AI assistance for labeling is a really helpful addition.
What do you dislike about the product?
It is expensive than its competitors, for small data sets we can use other tools.
Useful or AI team only not for others
Useful or AI team only not for others
What problems is the product solving and how is that benefiting you?
helping me to covert raw data into understanding format