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
Samarth P.
Highly Customizable Annotation Guidelines with Instructions Always at Hand
Reviewed on Nov 04, 2026
Review provided by G2
What do you like best about the product?
What I like the most about Datasaur is the opportunity to design annotation guidelines that match the specifics of each individual project. As various text datasets may call for different interpretation standards, the system allows you to configure the annotation workspace according to these very standards. In this way, you reduce the risk of imposing similar classification principles to unrelated datasets. Moreover, I really like the fact that annotation instructions are always at hand near the actual records.
What do you dislike about the product?
Changing the annotation guidelines once the project is already started may be quite difficult. With changing requirements, it could be useful to have some clear tools to indicate which instructions were modified and inform all participants about the changes.
What problems is the product solving and how is that benefiting you?
The use of Datasaur makes the continuity possible while preparing text with a number of people. As task instructions and annotated records are put into one single place, there is no need for separate reference documents, and project transfers will be easier.
Pragati V.
Versatile Text Annotation Platform for Building ML Datasets Faster
Reviewed on Nov 03, 2026
Review provided by G2
What do you like best about the product?
What I like about Datasaur is that I can work on several types of text annotations using one platform. If I need to deal with customer interactions, feedback, and business documents, I can define what information should be annotated and organize my work in this way. In such a way, I can easily create datasets that will correspond to different machine learning projects without the necessity to begin from scratch.
What do you dislike about the product?
I think that the platform could be more convenient in terms of its usage for similar projects when you have to repeat some project configurations. The process of their recreation wastes time and I would prefer to have more customization options for the workspace.
What problems is the product solving and how is that benefiting you?
Datasaur is a tool that can help to fill the gap between raw text information and data that can be used for machine learning projects. It offers a comfortable working environment for preparing training material and does not require too much manual work to organize unstructured information before development.
Arpit S.
Flexible, Easy-to-Organize Text Projects with Datasaur
Reviewed on Nov 03, 2026
Review provided by G2
What do you like best about the product?
I like how easy it is to work with Datasaur when it comes to setting up text-based projects because of its flexibility. The ability to arrange multiple datasets and set up project-specific categories makes it simpler to work separately and not to try to combine everything in one messy collection. The workspace provides a clear and fast overview of the materials you are working with.
What do you dislike about the product?
It takes some time to learn all the settings of Datasaur, particularly when you need to set up a project with requirements that differ from previous ones. Some clearer instructions for beginners would be helpful for setting up the basic project without lots of trial and error.
What problems is the product solving and how is that benefiting you?
Datasaur offers a more structured way of working with text data in large amounts. Using less scattered spreadsheets and tracking documents makes it simpler to keep project records organized and get quick access to the necessary information.
Surbhi B.
Flexible Annotation Options and a Labeling-Focused Workspace
Reviewed on Oct 31, 2026
Review provided by G2
What do you like best about the product?
I like the fact that there are various options for annotations, particularly when working with datasets which consist of conversations, documents, and unstructured texts. The workspace is tailored towards labeling rather than the spreadsheet way, and therefore it is easier to examine single examples and make sure that project guidelines are followed throughout the labeling process.
What do you dislike about the product?
Annotation projects may be difficult to handle when the same example needs several rounds of correction. It becomes necessary to keep track of why the label was changed and also ensure that the reviewers follow the same guidelines.
What problems is the product solving and how is that benefiting you?
The annotation analytics feature of Datasaur provides visibility on productivity and quality variations between contributors. This can make it easier to know areas which require additional training or revision of instructions.
Vikash K.
Fast span and row labeling workspace for curating our claims golden test suite
Reviewed on Oct 19, 2026
Review provided by G2
What do you like best about the product?
While we use Snorkel Flow to bulk-label large training datasets with Python rules, I personally use Datasaur Data Studio to curate and audit the Golden Test Suite for our insurance claims pipeline. When we ingest messy contents inventory spreadsheets, columns like 'Model#' and 'Brand or Manufacturer' are often left blank because adjusters type everything inside the raw 'Item Description' column. What I like best is how quickly I can switch between span-level entity tagging and row-level classification in the same spreadsheet-style view. Using keyboard hotkeys and LLM-assisted pre-labeling, I can highlight brands, model numbers, and dimensions buried inside item descriptions while also fixing low-confidence spans from our AssemblyAI audio transcripts. From there, I tag the item 'Condition', 'Item Age', and depreciation category so we have a clean ground-truth dataset to benchmark our Fireworks AI extraction models and Weights & Biases evaluation runs.
What do you dislike about the product?
I hit a few annoying limits when uploading large batches of raw claim files. Datasaur Data Studio caps individual uploads at 50 MB and requires every file in a batch to have the exact same file extension, so I had to write a Python preprocessing script to split and normalize our multi-sheet Excel exports and receipt PDFs before pushing them through the API. Also, in Reviewer Mode, when two team members highlight slightly different text spans or receipt bounding boxes, the red conflicted box locks so I cannot simply drag the border to resize it—I have to reject the label and redraw it from scratch. Finally, since hotkeys only map the first nine classes, scrolling through our full list of internal depreciation and regional tax codes in the dropdown gets tedious.
What problems is the product solving and how is that benefiting you?
Datasaur solved the headache of building and maintaining a reliable ground-truth benchmark across our end-to-end claims pipeline. Before I brought Datasaur into our workflow, curating our golden test suite meant passing raw Excel spreadsheets around on Slack, which broke down whenever we needed to verify tricky edge cases—like separating 'Cost to Replace Pre-Tax' from regional sales tax, catching duplicate high-quantity rows, or overriding standard age depreciation on 25-to-30-year-old vintage items bought on eBay. By managing this inside Datasaur with Reviewer Mode and consensus scoring, I can resolve conflicted rows alongside our domain experts in half the time and export clean JSON files straight into our PyTorch and LangChain pipelines.