Sold by: Datasaur
Deployed on AWS
Self-hosted, SOC 2 Type II and HIPAA-compliant NLP data labeling platform. Deploy on Kubernetes to label data 80% faster with ML-assisted automation and workforce management tools.
4.4
Overview
Datasaur Data Studio - Self-Hosted NLP Data Labeling Platform
Datasaur Data Studio is a comprehensive, self-hosted data labeling platform purpose-built for Natural Language Processing (NLP). Founded in 2019, Datasaur is an AWS Partner, and the platform is SOC 2 Type II certified and HIPAA compliant - making it a trusted choice for teams working with sensitive data in regulated industries.
Deploy Datasaur Data Studio as a container within your own AWS environment to maintain full control over your data. On average, clients have reduced time and spend on AI projects by over 70% through ML-automated labeling and intelligent workforce management.
Why Teams Choose Datasaur Data Studio
- Accelerate labeling by over 80%: ML-assisted pre-annotation suggests labels automatically, so annotators focus on edge cases rather than starting from scratch.
- Maintain data sovereignty: Self-hosted deployment means your sensitive data never leaves your environment. Ideal for healthcare, legal, financial services, and government use cases.
- Manage your labeling workforce at scale: Real-time visibility into annotator progress, quality metrics, inter-annotator agreement, and project timelines. Assign tasks, enforce multi-stage review workflows, and ensure production-quality datasets.
- Automate end-to-end with a comprehensive API: Programmatically create projects, import data, trigger labeling, and export labeled datasets directly into your ML pipeline.
- Enterprise-grade security: SOC 2 Type II and HIPAA compliance. Supports Google SSO and SAML/SCIM for user provisioning and access control.
Supported NLP Project Types
- Named entity recognition (NER)
- Document classification and text classification
- Part of speech labeling
- Coreference resolution
- Dependency parsing
- Data extraction
- Optical character recognition (OCR)
- Audio transcription
Common Use Cases
- 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, subject matter experts, and specialties are supported.
AWS Integration and Deployment Requirements
Datasaur Data Studio integrates with Amazon S3 for object storage and runs on a Kubernetes cluster within your AWS environment (e.g., Amazon EKS).
Minimum infrastructure requirements:
- Kubernetes version 1.30 or higher
- Node specification: 4 vCPU and 16GB RAM per node
- Recommended: 5-6 nodes with auto-scaling enabled
- Storage: 100GB per node
- Additional nodes may be required depending on Analytics and AI feature usage
Get Started
A free trial is available so you can evaluate the platform before committing. Schedule a personalized demo to see ML-assisted labeling, workforce management dashboards, and review workflows in action.
For detailed setup guides and API documentation, visit the Datasaur documentation at https://docs.datasaur.ai/
Highlights
- NLP-optimized labeling interface with ML-assisted pre-annotation that enables teams to label data over 80% faster than manual methods. Supports named entity recognition, document classification, text extraction, dependency parsing, OCR-based labeling, and audio transcription. SOC 2 Type II and HIPAA compliant, with self-hosted deployment on Kubernetes so sensitive data never leaves your environment.
- Built-in workforce management and multi-stage review workflows give team leads real-time visibility into annotator progress, quality metrics, and inter-annotator consistency. Assign tasks, enforce review stages, and ensure labeled datasets meet production-quality standards before reaching your ML pipeline.
- Comprehensive API automates end-to-end labeling workflows - from project creation and data import to labeled dataset export. ML intelligence pre-annotates data so annotators focus on edge cases. Integrates with Amazon S3 for object storage. Clients have reported reducing overall AI project time and spend by over 70%.
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 as a set unit rather than per-usage. The tier covers 1 workspace and up to 50 users, with a ceiling of 1 million labels. It bundles API access, ML-assisted labeling, label error detection, predictive labeling, data programming, and Datasaur Dinamic. It also includes advanced analytics, SAML and SCIM integration, enterprise-grade compliance and security, and dedicated support. Because there is a single tier, pricing does not scale across levels. Your capacity limits are the workspace, user, and label counts listed.
Top-of-mind questions for buyers
A label is a single annotation applied to your data during a labeling task. This includes span labels, text or document classifications, bounding boxes, audio labels, and conversational labels. Each annotation you apply counts once toward your 1 million total across the workspace.
The Enterprise tier sets fixed ceilings of 50 users and up to 1 million labels within one workspace. These are capacity limits, not usage meters that add overage charges. To go beyond them, contact the vendor to discuss expanding your contract.
You get ML-assisted labeling, predictive labeling, data programming, and Datasaur Dinamic for building your own models. Label error detection helps flag mistakes. API access lets you create projects, add documents, run assisted labeling, and export programmatically. These features support automated pre-labeling alongside human verification.
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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
Support
Vendor support
Datasaur provides 24/7 support for Data Studio (Self-hosted) with a 24-hour turnaround time on all inquiries.
Email Support For technical issues, product questions, or troubleshooting, contact the support team at support@datasaur.ai . All support requests receive an initial response within 24 hours.
Getting Started and Demos To request a product demo or discuss deployment requirements for your self-hosted environment, reach out to demo@datasaur.ai . A free trial is available for teams that want to evaluate the platform before committing.
Deployment Prerequisites Datasaur Data Studio runs on Kubernetes (version 1.30 or higher). Minimum node specification is 4 vCPU and 16GB RAM, with 100GB storage per node. We recommend provisioning 5-6 nodes, with node auto-scaling supported. Additional nodes may be required depending on Analytics and AI feature usage. Amazon S3 is required for object storage. Authentication supports Google SSO, SAML, and SCIM for user provisioning.
Documentation Product documentation is available at https://datasaurai.gitbook.io/datasaur/ and covers setup, configuration, and usage guidance.
Billing and Refunds For refund requests or billing inquiries related to your AWS Marketplace subscription, contact support@datasaur.ai with your AWS account details and order information.
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
pankaj r.
User-Friendly, But Lags with Complex Data
Reviewed on Sep 03, 2026
Review provided by G2
What do you like best about the product?
I like how Datasaur is user-friendly, which makes it easy for me to navigate. I also appreciate that it saves me time compared to doing manual work. The initial setup was easy, which was a nice surprise.
What do you dislike about the product?
I don't like that Datasaur is lagging with large datasets, especially when I'm dealing with complex annotations and queries. It slows down the process and can be frustrating.
What problems is the product solving and how is that benefiting you?
I use Datasaur to create documents from natural language, which saves me time compared to manual work.
Anirudh C.
Powerful Annotation for Large Text Datasets with Flexible Guidelines
Reviewed on Sep 03, 2026
Review provided by G2
What do you like best about the product?
Datasaur is especially valuable in dealing with large-scale text sets which differ slightly by meaning. It is nice that one can create sophisticated annotation guidelines and study specific samples. This way I can discern similar topics without grouping them into overly general categories.
What do you dislike about the product?
Checking complicated annotations may become monotonous if the dataset contains lots of similar cases. Also, it takes some time to define the right approach to labeling such cases with uncommon wording.
What problems is the product solving and how is that benefiting you?
The tool makes it simpler to convert qualitative data into structured information which will be suitable for comparative analysis. Instead of maintaining classifications separately, it will be possible to base them on the results of annotation.
Ajay P.
Datasaur Makes Qualitative Analysis Easy with Consistent Labels
Reviewed on Sep 02, 2026
Review provided by G2
What do you like best about the product?
Datasaur is useful in giving a framework to qualitative information related to products. I like the opportunity to use specific labels for certain types of feedback and apply them consistently because it makes it much easier to search for patterns in a large text collection.
What do you dislike about the product?
The quality of the resulting output depends mostly on the quality of the annotation scheme used. When there are many ideas expressed in a single comment, it is still necessary to make a lot of manual work in terms of labeling.
What problems is the product solving and how is that benefiting you?
It allows minimizing efforts needed to transform unstructured feedback into structured datasets. In other words, it makes it easier to distinguish between themes, compare different groups of responses, and organize qualitative information for future product analysis without manual keeping of huge tables of classification.
Recommendations to others considering the product:
To improve the annotation process, consider using a more detailed and structured annotation scheme. Additionally, providing training for annotators can help ensure consistency and accuracy in labeling.
Vishant J.
Datasaur Makes Categorizing Customer Feedback Easy and Systematic
Reviewed on Aug 31, 2026
Review provided by G2
What do you like best about the product?
Datasaur has managed to prove its usefulness through aiding in the categorization of large amounts of customer-related data into specific categories which can then be analyzed in a systematic fashion. The tool enables teams to structure their feedback, support, and qualitative responses, thus making it easy for them to recognize themes.
What do you dislike about the product?
The variety of language used by customers can be very wide-ranging, which might make it difficult to assign messages to only one neat category. The construction of a useful categorization scheme will need some effort, especially when dealing with weird or exceptional cases.
What problems is the product solving and how is that benefiting you?
The method helps to turn customer qualitative data into useful data. Teams will be able to spot patterns in large sets of data and utilize them by prioritizing certain recurrent problems instead of working on random customer feedback.
Swastik C.
Bringing Structure to Your Annotation Workflows Without Reducing Your Team’s Speed.
Reviewed on Aug 31, 2026
Review provided by G2
What do you like best about the product?
Datasaur comes in handy when your annotation work becomes large-scale or too complicated to perform manually. It provides labeling guidelines, allows using several people for labeling the same project and model-based suggestions to facilitate the labeling of repetitive data. The human-reviews process plays a key role in automation as it enables us to control the quality of our data.
What do you dislike about the product?
The core workflow is simple to start with, but managing lots of labels, reviewers, and quality policies gets complicated. Datasets of large sizes require time to be processed, and new members of the team require some time to learn about advanced options.
What problems is the product solving and how is that benefiting you?
It substitutes the dispersed annotation tables and manual work with the unified labeling workflow that ensures the consistency of annotations, reveals inconsistencies between reviewers and produces clean data for NLP and ML projects.