
Mage
Provides a collaborative workspace that streamlines the data engineering workflow, enabling rapid development of data products and AI applications.
Date | Investors | Amount | Round |
---|---|---|---|
- | investor investor investor investor investor | €0.0 | round |
N/A | €0.0 | round | |
investor investor investor investor investor investor investor | €0.0 | round | |
* | N/A | $5.5m | Early VC |
Total Funding | 000k |
USD | 2021 | 2022 | 2023 |
---|---|---|---|
Revenues | 0000 | 0000 | 0000 |
% growth | - | 255 % | 224 % |
EBITDA | 0000 | 0000 | 0000 |
Profit | 0000 | 0000 | 0000 |
EV | 0000 | 0000 | 0000 |
EV / revenue | 00.0x | 00.0x | 00.0x |
EV / EBITDA | 00.0x | 00.0x | 00.0x |
R&D budget | 0000 | 0000 | 0000 |
Source: Dealroom estimates
Related Content
Mage.ai is an innovative startup that specializes in creating tools for building and managing data pipelines. The company offers an open-source platform designed to help data teams transform and integrate data from various sources. Mage.ai is particularly focused on providing a user-friendly interface and a great developer experience, which significantly reduces the time required for development.
The primary clients of Mage.ai include data engineers, data scientists, and analytics teams who need to build, run, and monitor data pipelines efficiently. These clients often work in industries such as finance, healthcare, e-commerce, and technology, where data integration and transformation are critical.
Mage.ai operates in the data pipeline tooling market, competing with other solutions like Apache Airflow. However, Mage.ai differentiates itself by offering a modern, easy-to-use alternative that simplifies the development process. The platform supports real-time and batch data processing using popular programming languages like Python, SQL, and R.
The business model of Mage.ai is based on an open-source framework, which means the core software is freely available for anyone to use. However, the company generates revenue through premium features, enterprise support, and cloud deployment options. Clients can deploy Mage.ai on various cloud platforms such as AWS, GCP, Azure, or DigitalOcean using maintained Terraform templates, which are tools that help manage cloud infrastructure.
In summary, Mage.ai makes money by offering advanced features and support services to enterprises that need robust data pipeline solutions. The company also fosters a community of users and developers who contribute to the platform, ensuring continuous improvement and innovation.
Keywords: data pipelines, open-source, data integration, developer-friendly, real-time processing, batch processing, Python, SQL, R, cloud deployment.