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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
LOKESH G.
Datasaur Makes Data Labeling Simple and Efficient
Reviewed on Aug 12, 2026
Review provided by G2
What do you like best about the product?
I like Datasaur the most because it makes data labeling and annotation simple and efficient. The interface is intuitive, and the tools for managing, reviewing, and organizing datasets help streamline the process and speed up AI and machine-learning workflows.
What do you dislike about the product?
One thing I dislike about Datasaur is that some of the more advanced features can take a while to understand, especially for new users. I also think the platform could improve its customization options and make certain workflows feel more intuitive and straightforward.
What problems is the product solving and how is that benefiting you?
Datasaur helps me tackle the challenge of **managing and labeling large amounts of data for AI and machine-learning projects**. It streamlines annotation and keeps data organized, making the overall workflow more efficient. As a result, I save time, maintain better data quality, and can prepare more reliable datasets for training and evaluating AI models.
Apoorv T.
Datasaur’s Intuitive Interface and Powerful AI-Assisted Labeling
Reviewed on Aug 12, 2026
Review provided by G2
What do you like best about the product?
I personally love Datasaur’s interface, and I like that it supports LLMs and GenAI. On top of that, the AI assistance for labeling is a really helpful addition.
What do you dislike about the product?
It is expensive than its competitors, for small data sets we can use other tools.
Useful or AI team only not for others
Useful or AI team only not for others
What problems is the product solving and how is that benefiting you?
helping me to covert raw data into understanding format
KUNAL J.
Kunal Jaipuriar’s Review
Reviewed on Aug 12, 2026
Review provided by G2
What do you like best about the product?
It has a strong focus on NLP and LLM data annotations. Also, the user interface is quite intuitively
What do you dislike about the product?
It is primarily optimized for text, NLP, and GenAI annotation projects hence it is less comprehensive in comparison to others.learning graph is also a bit complicated
What problems is the product solving and how is that benefiting you?
Majorly in creating high quality labeled data which need heavy training, fine tuning and evaluating genAI model.it has significantly reduced time and effort for manual data labeling and improved annotation consistency across team
Puneet M.
A Practical Platform for NLP Data Annotation
Reviewed on Aug 11, 2026
Review provided by G2
What do you like best about the product?
I like Datasaur’s intuitive annotation interface and smooth workflow, which made it easy to get started and work efficiently with NLP datasets. The AI-assisted labeling features reduced repetitive manual work, while the annotation and review tools helped maintain consistent data quality. I also found its integration capabilities useful for fitting annotation into my existing data workflow, and the responsive platform and straightforward onboarding made it easy to adopt. Overall, the time saved during dataset preparation made the platform valuable from a productivity and ROI perspective.
What do you dislike about the product?
The annotation workflow is generally smooth, but some advanced features can take time to learn, and setting up more complex integrations or workflows may require additional configuration. I also found that AI-assisted labeling still needs human review for accuracy, especially with domain-specific NLP data, so the productivity gains are not completely automatic.
What problems is the product solving and how is that benefiting you?
Datasaur helps solve the time-consuming and inconsistent process of preparing labeled NLP data. Its annotation, review, and AI-assisted labeling workflows reduce repetitive manual work, make labeling more consistent, and help me prepare higher-quality datasets faster for NLP and machine learning projects.
Mayank C.
Datasaur Makes Data Labeling Simple and Team-Friendly
Reviewed on Aug 10, 2026
Review provided by G2
What do you like best about the product?
I like Datasaur because it makes the data labeling process simple and easy to manage. The interface is clean and user-friendly, and it helps teams organize and annotate large datasets without making the workflow feel complicated. I also like that it supports collaboration, which makes it useful when working with a team.
What do you dislike about the product?
One thing I dislike about Datasaur is that it can take some time to get familiar with all the features, especially for new users. Some parts of the interface could also be a little more intuitive. Apart from that, the overall experience has been pretty good.
What problems is the product solving and how is that benefiting you?
Datasaur helps solve the problem of organizing and labeling large amounts of data efficiently. It makes data annotation easier to manage and helps reduce the time spent on manual labeling. For the business, this improves the quality and consistency of training data, makes team collaboration easier, and helps speed up AI and machine learning projects.