Sold by: Datasaur
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LLM Labs enables businesses to solve key business problems through AI & automation of OTS, custom, and private LLMs.
4.5
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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Delivery method
Deployed on AWS
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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
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.
Legal
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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
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.
Ragini C.
Datasaur Makes Structured Annotation Consistent and Efficient
Reviewed on Aug 25, 2026
Review provided by G2
What do you like best about the product?
Datasaur is particularly helpful when working with structured data-review assignments that require maintaining consistency. Using Datasaur, I am able to work with structured annotation projects with well-defined labeling schemes, quickly find information within large-scale data sets, and label the same patterns across various records.
What do you dislike about the product?
Sometimes, detailed labeling projects can be complicated because of the complexity or regular changes in the classification rules. To avoid inconsistencies in treatment of similar records, a lot of attention must be paid to the project instructions.
What problems is the product solving and how is that benefiting you?
The main advantage is the ability to work in a more consistent manner at the stage of data preparation and review. The quality control feature helps identify inconsistencies between reviewers, and the automated/bulk labeling functionality allows reducing the load of repetitive manual tasks. This way, I can pay my attention to those records that require my attention.
Vivaan K.
Datasaur Streamlines ML/NLP Annotation with Flexible Schemas and Model-Assisted Labeling
Reviewed on Aug 24, 2026
Review provided by G2
What do you like best about the product?
I find Datasaur useful to prepare data sets that are going to be used for machine-learning and NLP processes. This allows me to create annotation schemas, to use various formats, and to implement standardized annotation procedures rather than using fragmented manual approaches. The ability to have some assistance from a model in my workflow becomes very useful when working with large data sets because this will not decrease quality but will simplify the labeling procedure.
What do you dislike about the product?
With more complex annotation tasks, the planning is sometimes necessary prior to implementing the labeling procedure because I need to define taxonomies and project rules, as well as review automatic label suggestions, in order to ensure the necessary quality of the training data.
What problems is the product solving and how is that benefiting you?
The main advantage of using Datasaur is that this product helps to reduce the time spent on the preparation of the machine-learning dataset since engineers will be able to automate part of the annotation procedure, control the quality, and check the consistency of the annotations.
Christy D.
Easy Onboarding and a Straightforward System
Reviewed on Aug 20, 2026
Review provided by G2
What do you like best about the product?
The onboarding process was easy. It’s a good system and straightforward to use.
What do you dislike about the product?
The pricing for the basic setup is higher than I would like.
What problems is the product solving and how is that benefiting you?
It was easy to integrate with other systems.