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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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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
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.