Domino Data Lab
Financials
Estimates*
USD | 2018 | 2019 | 2020 |
---|---|---|---|
Revenues | 27.2m | 39.2m | 48.8m |
% growth | - | 44 % | 24 % |
Date | Investors | Amount | Round |
---|---|---|---|
- | N/A | - | |
$3.0m | Series A | ||
N/A | $100k | Angel | |
$3.0m | Series A | ||
$10.5m | Series B | ||
$27.0m | Series C | ||
* | $40.0m | Series D | |
$43.0m | Late VC | ||
N/A | - | ||
$100m | Series F | ||
* | N/A | Series F | |
Total Funding | €206m |
Recent News about Domino Data Lab
EditDomino Data Lab is a technology startup that operates in the artificial intelligence (AI) sector. The company provides a unified platform that allows businesses to build, deploy, and manage AI models. Their platform is designed to foster collaboration, establish best practices, and track models in production to accelerate and scale AI while ensuring governance and reducing costs.
The company's primary clients are businesses that require AI solutions. Domino Data Lab's platform is particularly useful for these businesses as it provides access to a broad ecosystem of open source and commercial tools, and infrastructure, allowing for innovation without vendor lock-in.
Domino Data Lab operates on a business model that focuses on providing a central hub for AI operations and knowledge across the enterprise. This hub enables best practices, cross-functional collaboration, faster innovation, and efficiency. The company's platform also integrates workflows and automation built for enterprise processes, controls, and governance, to satisfy compliance and regulatory needs.
The company's platform can run AI workloads close to data anywhere, whether on-premises, hybrid, any cloud or multi-cloud, for lower cost, optimal performance, and compliance. This flexibility offers freedom for data scientists and control for IT departments.
Domino Data Lab makes money by charging businesses for using their platform. The platform helps businesses optimize compute utilization and cloud costs, manage AI risk, and minimize support costs with automated DevOps.
Keywords: Artificial Intelligence, AI Models, Collaboration, Governance, Open Source Tools, Commercial Tools, Enterprise Processes, Compliance, Risk Management, Automated DevOps.