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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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Pricing
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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
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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
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Customer reviews
Computer Software
Datasaur Makes Collaborative, ML-Assisted Labeling Fast and Flexible
Reviewed on Aug 13, 2026
Review provided by G2
What do you like best about the product?
What I like best about Datasaur is how it makes data labeling less painful and way more collaborative.
My top 3 things about Datasaur:
1. Collaboration is smooth
Multiple annotators can work on the same dataset, with disagreements tracked and resolved. No more messy spreadsheets or "which version is final" drama. It’s built for teams.
2. ML-assisted labeling
It uses models to suggest labels while you annotate. So you label 100 examples, it learns, and starts pre-labeling the next 1000. Cuts annotation time massively.
3. Works for all kinds of data
Text, images, documents, PDF contracts, NER, classification, QA pairs — you name it. The interface adapts and you can set up custom workflows + quality checks inside it.
My top 3 things about Datasaur:
1. Collaboration is smooth
Multiple annotators can work on the same dataset, with disagreements tracked and resolved. No more messy spreadsheets or "which version is final" drama. It’s built for teams.
2. ML-assisted labeling
It uses models to suggest labels while you annotate. So you label 100 examples, it learns, and starts pre-labeling the next 1000. Cuts annotation time massively.
3. Works for all kinds of data
Text, images, documents, PDF contracts, NER, classification, QA pairs — you name it. The interface adapts and you can set up custom workflows + quality checks inside it.
What do you dislike about the product?
1. Pricing gets steep for big teams
For solo/small teams it’s okay. But once you scale to 10+ annotators + lots of documents, the cost jumps. Free tier is also pretty limited.
2. Learning curve for complex workflows
Basic labeling is easy. But if you want custom ontologies, multi-stage reviews, agreement metrics, and automation rules — setup takes time. New users often get lost in all the settings.
3. UI can feel heavy sometimes
When datasets get huge or you’re labeling 50-page PDFs, the platform can lag. And searching/filtering through thousands of labeled items isn’t as fast as I’d like.
For solo/small teams it’s okay. But once you scale to 10+ annotators + lots of documents, the cost jumps. Free tier is also pretty limited.
2. Learning curve for complex workflows
Basic labeling is easy. But if you want custom ontologies, multi-stage reviews, agreement metrics, and automation rules — setup takes time. New users often get lost in all the settings.
3. UI can feel heavy sometimes
When datasets get huge or you’re labeling 50-page PDFs, the platform can lag. And searching/filtering through thousands of labeled items isn’t as fast as I’d like.
What problems is the product solving and how is that benefiting you?
Problem: Before, labeling data for AI meant spreadsheets, Google Docs, or building your own tool. 1 person labels 200 examples/day, and quality is all over the place.
How Datasaur helps: ML-assisted labeling. You label 200, the model learns, and it pre-labels the next 2000. My speed goes up 5x-10x.
Problem: 5 people labeling same dataset = different formats, disagreements, no tracking who did what.
How Datasaur helps: Built-in collaboration + disagreement resolution + agreement scores. Project manager can assign, review, and audit everything
How Datasaur helps: ML-assisted labeling. You label 200, the model learns, and it pre-labels the next 2000. My speed goes up 5x-10x.
Problem: 5 people labeling same dataset = different formats, disagreements, no tracking who did what.
How Datasaur helps: Built-in collaboration + disagreement resolution + agreement scores. Project manager can assign, review, and audit everything
Darpan T.
Datasaur Makes Data Labeling Organized, Efficient, and Team-Friendly
Reviewed on Aug 13, 2026
Review provided by G2
What do you like best about the product?
What I like most about Datasaur is that it makes the data labeling and annotation process much more organized and efficient. The interface is straightforward, and it is easy to review, label, and manage large amounts of data without making the workflow unnecessarily complicated. I also like the collaboration features, which make it easier for teams to work consistently on annotation projects.
What do you dislike about the product?
The main thing I dislike about Datasaur is that some advanced features can take a little time to understand, especially for new users. The interface can also feel slightly overwhelming when working with complex annotation projects or large datasets. A more streamlined experience for beginners and clearer guidance for advanced features would make it easier to get started.
What problems is the product solving and how is that benefiting you?
Datasaur simplifies the process of labeling and organizing large datasets, which can otherwise be time-consuming and difficult to manage manually. It provides a structured workspace for annotation, review, and collaboration, helping reduce repetitive work and maintain consistency across projects. This makes the overall data preparation process faster and helps me work more efficiently with datasets used for AI and machine learning.
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