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LLM Labs enables businesses to solve key business problems through AI & automation of OTS, custom, and private LLMs.
4.4
Overview
Use OTS LLMs or create your own proprietary model to automate AI solutioning to solve key business problems: improve margins and profits and accelerate delivery timelines by reducing costs, people time, and resources.
LLM Labs offers over 250 language models for robo-labeling (automated data annotation) based on OTS or custom ontologies and data sets. Models can be leveraged simultaneously for comparison, evaluation, and benchmarking of inference quality, speed, and cost as well as model recommendations. Furthermore, LLMs can be deployed in Datasaur's Data Studio for custom model development and automation.
You can explore and evaluate all of these with peace of mind, as Datasaur is SOC 2 Type 2, HIPAA, and GDPR compliant. Your data stays your data.
Highlights
- Leverage 250+ LLMs for robo-labeling (automated data annotation), model building and evaluation.
- 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
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Deployed on AWS
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Pricing
Free trial
Try this product free according to the free trial terms set by the vendor.
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/month |
|---|---|---|
Pay as you go | You will be charged based on your usage of LLM Labs when performing any of the following activities: Running prompts and applications in Playgrounds, updating embeddings in Vector Stores, and generating completions for Evaluation projects. The cost depends on the model you use for these activities. An optional subscription plan is available for users who need to use their own LLM credentials from several providers, such as Azure, OpenAI, Bedrock, and Vertex. Contact us at support@datasaur.ai to learn more. | $0.00 |
Dimension | Description | Cost/unit |
|---|---|---|
Usage fees | Each model has a different cost. | $0.01 |
Vendor refund policy
This is a pay as you go plan. You will only be charged for the amount you use. For any refund requests please contact support@datasaur.ai
Custom pricing options
Request a private offer to receive a custom quote.
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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
For further inquiries or assistance with our products, please contact us at support@datasaur.ai .
To learn more and explore our products, visit our documentation at
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
Nidhi R.
Datasaur Streamlines Data Labeling Coordination with Clear Roles and Progress Tracking
Reviewed on Aug 27, 2026
Review provided by G2
What do you like best about the product?
The strengths of Datasaur lie in its ability to deal with administrative coordination within the context of data labeling projects. It can assist in handling the distribution of responsibility for various tasks, tracking responsibilities of the reviewers, monitoring progress, and ensuring that all necessary data is properly routed through the necessary steps. All role assignments and project reports being located in the same space means that there is a predictability in coordination processes.
What do you dislike about the product?
The administrative side of the process can become more complicated in the case of a number of reviewers, complex labeling rules, or several QA checks throughout the project.
What problems is the product solving and how is that benefiting you?
First of all, it is high levels of visibility in the progress of the project. Having access to the progress reports and performance indicators allows to identify delays in the process, see how the work is being distributed, and coordinate actions with the reviewers.
Balaji S.
Well-Defined Annotation Workflow with Handy Bulk Labeling
Reviewed on Aug 27, 2026
Review provided by G2
What do you like best about the product?
Datasaur is particularly beneficial when it comes to a well-defined workflow of data project from the initial annotation stage through review all the way to the export stage. It allows structuring labeling instruction, facilitating coordination between reviewers and maintaining a single point of record for disagreements instead of relying solely on individual conversations. Bulk labeling feature becomes handy when there are recurring patterns in large datasets.
What do you dislike about the product?
Coordination efforts may be elevated in case there are large taxonomies or multiple review stages used in the project. It is crucial to have reviewers understand the logic of labeling instructions to produce consistent results, therefore, in case of changes in project instructions, communication and additional quality control will be required.
What problems is the product solving and how is that benefiting you?
Datasaur provides better visibility into which parts of labeling workflow go well and which require intervention. By analyzing inter-annotator agreement and statistics about the team, coordinators will be able to find issues or discrepancies that need to be addressed by focusing on certain parts of workflow instead of manual inspection.
Yash R.
Centralized Project Oversight for Data Labeling Workflows
Reviewed on Aug 27, 2026
Review provided by G2
What do you like best about the product?
Datasaur appears to be a useful tool to manage the operational aspects of data-labeling projects. I am able to get an idea on the progress of the project, coordinate the activity of reviewers, keep an eye on quality metrics, and make sure that all labeling processes comply with necessary structures. By bringing all project details and reviewer activity into one place, it becomes possible to identify any issues and flaws in the workflow.
What do you dislike about the product?
In order to manage the operational process of data labeling, a lot of efforts still have to be put into defining rules of labeling and reviewing the results. When dealing with projects containing large taxonomies or various kinds of annotations, managing the workflow might become more complicated due to different interpretations of the same data by different reviewers.
What problems is the product solving and how is that benefiting you?
The biggest advantage provided by this solution is the increase in visibility in the process of data labeling and review. Metrics of quality, activity of the reviewers, resolving conflicts, and project reports allow to detect all inconsistencies and pay special attention to them.
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.