- Revenue
- $9.6M
- Customers
- -
- Year founded
- 2019
- Funding
- -
- Team size
- 65
- Growth
- -
Top 62 Data Labeling Software SaaS Companies in May 2026
As of May 2026, there are 62 SaaS companies in Data Labeling Software. They have combined revenues of $4.5B and employ 15.1K people. They have raised $1.9B and serve 502.5K customers combined.
Data labeling software is designed to facilitate the process of annotating data, which is crucial for the development of machine learning and artificial intelligence models. Users of this software can label various data types, including images, audio, and text, providing the necessary annotations that allow algorithms to recognize patterns and make predictions. The software streamlines workflows, enabling large datasets to be processed efficiently and ensuring data quality through collaborative tools and automated features. Typical use cases for data labeling software include applications in computer vision for object detection, natural language processing for text classification, and audio analysis for speech recognition. With common features like user-friendly interfaces, quality control mechanisms, and integration capabilities with machine learning frameworks, this software empowers data scientists, AI developers, and researchers to prepare their data sets comprehensively. The primary buyers often include tech companies, research institutions, and enterprises looking to enhance their AI solutions and analytics capabilities.
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Top Data Labeling Software Companies
Showing 10 of 7 companies ranked by annual revenue.
2
understand.ai
Karlsruhe, Germany
We believe that AI is the most powerful tool our generation has at hand. At understand. ai , we make deep learning accessible for real-world applications like self-driving cars. More Info: https://understand. ai /privacy-policy Founded in 2017 in Karlsruhe, Germany, we are the ideal annotation partner throughout...
- Revenue
- $8.9M
- Customers
- -
- Year founded
- 2017
- Funding
- -
- Team size
- 81
- Growth
- -
3
Sapien
San Francisco, California, United States
Sapien is a decentralized data foundry, turning collective human knowledge into enterprise-grade AI training data.
- Revenue
- $8.6M
- Customers
- -
- Year founded
- 2023
- Funding
- -
- Team size
- 58
- Growth
- -
4
Macgence
Noida, Uttar Pradesh, India
Macgence is a leading AI training data company at the forefront of providing exceptional human-in-the-loop solutions to make AI better. We specialize in offering fully managed AI/ML data solutions, catering to the evolving needs of businesses across industries. With a strong commitment to responsibility and sincerity, we have established ourselves as a trusted partner for organizations seeking advanced automation solutions.
- Revenue
- $8.6M
- Customers
- -
- Year founded
- 2022
- Funding
- -
- Team size
- 78
- Growth
- -
5
Datasaur
Livermore, California, United States
Datasaur builds a data labeling workforce management platform for NLP.
- Revenue
- $7.2M
- Customers
- -
- Year founded
- 2019
- Funding
- -
- Team size
- 64
- Growth
- -
6
Kili Technology
Paris, ĂŽle-de-France, France
Build high-quality datasets, fast. Enterprises trust us to streamline their data labeling ops and build the best datasets for their custom models, generative AI, and LLMs ___ Why Kili Technology? You might not know this, but: MNIST’s dataset has an error rate of 3.4% and is still cited by more than 38,000 papers. The ImageNet dataset, with its crowdsourced labels, has an error rate of 6%. This dataset arguably underpins the most popular image recognition systems developed by Google and Facebook. Systemic error in these datasets has real-world consequences. Models trained on error-containing data are forced to learn those errors, leading to false predictions or a need of retraining on ever-increasing amounts of data to “wash out” the errors. Every industry has begun to understand the transformative potential of AI and invest. But the revolution of ML transformers and relentless focus on ML model optimization is reaching the point of diminishing returns. What else is there? ______ The Company Kili began as an idea in 2018. Edouard d’Archimbaud, our co-founder and CTO, was working at BNP Paribas, where he built one of the most advanced AI Labs in Europe from scratch. François-Xavier Leduc, our co-founder and CEO, knew how to take a powerful insight and build a company around it.While all the AI hype was on the models, they focused on helping people understand what was truly important: the data. Together, they founded Kili Technology to ensure data was no longer a barrier to good AI.By July 2020, the Kili Technology platform was live and by the end of the year, the first customers had renewed their contract, and the pipeline was full. In 2021, Kili Technology raised over $30M from Serena, Headline and Balderton. Today Kili Technology continues its journey to enable businesses around the world to build trustworthy AI with high-quality data.
- Revenue
- $6.7M
- Customers
- -
- Year founded
- 2018
- Funding
- -
- Team size
- 61
- Growth
- -
- Revenue
- $5.7M
- Customers
- -
- Year founded
- 2018
- Funding
- $1.8M
- Team size
- 56
- Growth
- 59.29%
Inclusion Criteria
- Must provide tools for labeling diverse data types including images, text, and audio. - Should support both manual labeling and automated annotation processes. - Must include collaboration features for teams to work on data labeling tasks. - Must ensure quality control mechanisms to verify the accuracy of labeled data. - Not just a data management tool; must also provide data annotation capabilities.