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DateInvestorsAmountRound
investor investor

€0.0

round
investor investor investor

€0.0

round
investor investor investor investor

€0.0

round
*

$700m

Valuation: $700m

Acquisition
Total Funding000k

Financials

Estimates*

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Revenues, earnings & profits over time
USD20192020202120222023
Revenues00000000000000000000
% growth-92 %96 %222 %94 %
EBITDA00000000000000000000
Profit00000000000000000000
EV00000000000000000000
EV / revenue00.0x00.0x00.0x00.0x00.0x
EV / EBITDA00.0x00.0x00.0x00.0x00.0x
R&D budget00000000000000000000

Source: Dealroom estimates

Notes (0)
More about Run:AI
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Run:ai is a startup that operates in the artificial intelligence (AI) sector, providing a unified platform that simplifies and optimizes the process of training and deploying AI models. The company's platform is designed to abstract infrastructure complexities, allowing clients to focus on building, training, and deploying their models. It integrates with a variety of tools and frameworks, and uses unique scheduling and GPU optimization technologies to enhance the machine learning journey.

The company serves a broad range of clients, including those in the financial services sector. Its platform allows clients to scale their data processing pipelines to hundreds of machines using a single command, and to run their pre-processing pipelines in the same environment as their training workflows. This results in improved efficiency and easier management.

Run:ai's business model is based on providing access to its platform on a subscription basis. The platform allows clients to access multiple GPUs or a fraction of a single GPU, while keeping their data and code private and secure. It also enables clients to launch hundreds of distributed batch jobs on shared pools of GPUs without worrying about queueing, infrastructure failures, or GPU provisioning.

The company makes money by charging clients for use of its platform. It also offers a range of additional services, including the ability to deploy models anywhere, from cloud to on-premises and edge servers, and to give users secure access to models through a URL or a web UI.

Keywords: Artificial Intelligence, Machine Learning, GPU Optimization, Data Processing, Training Models, Deploying Models, Cloud Computing, On-Premises Servers, Edge Servers, Subscription-Based Model.

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