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 human-in-the-loop methods.
4.4
Overview
Datasaur is a comprehensive data labeling platform for Natural Language Processing (NLP) - part of the data pipeline behind Datasaur's private AI deployments.
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
- Highly intuitive interface for NLP labeling hosted in the cloud or on-premise.
- Build custom AI solutions with the best-in-market LLMs and annotation tools for automation to reduce people time and costs by over 70%
- Full service workforce management and review tooling that allows teams to track and monitor progress.
Details
Sold by
Delivery method
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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 |
|---|---|---|
Growth tier | 1 workspace, 10 users, up to 250,000 labels, API access, Advanced Analytics, ML-assisted Labeling, Label Error Detection, Predictive Labeling, Data Programming, Datasaur Dinamic | $50,000.00 |
Dimensions summary
You buy the Growth tier as a single contract with a fixed set of capacity limits. This one bundle covers 1 workspace, up to 10 users, and up to 250,000 labels. Access to API, Advanced Analytics, ML-assisted Labeling, Label Error Detection, Predictive Labeling, Data Programming, and Datasaur Dinamic is included at this tier. Pricing does not scale by usage or add-ons. Instead, you commit to the tier and its capped quantities. If your team or label volume grows beyond these limits, you would move to a different arrangement.
Top-of-mind questions for buyers
A label is a single annotation you apply to a data point during a labeling task. Each tag on a piece of text counts separately. The Growth tier caps your total at 250,000 labels across your workspace. Automated and manually applied labels both count toward this figure.
The Growth tier is a fixed contract with capped quantities. It does not scale automatically or add overage charges. Once you reach 1 workspace, 10 users, or 250,000 labels, you cannot exceed those caps under this tier. You would move to a different arrangement with the vendor.
All listed capabilities come bundled in the single Growth tier contract at no extra charge. This includes API access, Advanced Analytics, ML-assisted Labeling, Label Error Detection, Predictive Labeling, Data Programming, and Datasaur Dinamic. There are no per-feature add-ons or usage-based charges layered on top.
datasaur.ai
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor 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
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.
Monika B.
Efficient Labeling with Room for UI Improvement
Reviewed on Sep 29, 2026
Review provided by G2
What do you like best about the product?
I really enjoy using the rule-based match and programmatic auto-labeling features of Datasaur. They make the annotation process much quicker, as my team can apply patterns and models automatically which significantly reduces time spent on each project. I love the VPC deployment option and enterprise compliance as it helps handle sensitive client data securely by allowing us to deploy the platform within our private cloud, ensuring that customer data stays within our security parameters. The initial setup was fast and simple, making it easy for us to configure our workspace and get started with almost no training required. This allowed us to start using the intuitive labeling interface from day one.
What do you dislike about the product?
I dislike the UI lag on multi-page dense documents. When annotators highlight long text across dense, 40-plus page contracts, the web rendering interface sometimes stutters or delays token highlighting. They could improve this by optimizing DOM canvas rendering or introducing virtualized token scrolling, so multi-page documents load and highlight smoothly without frame drops.
What problems is the product solving and how is that benefiting you?
Datasaur solves low annotation consistency and inter-annotator disagreements. It speeds up projects by over 40% with rule-based matching, auto labeling, and secure VPC deployment for handling proprietary data, enhancing my team's efficiency and security.
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.