Run:ai Raises $75M in Series C Round to Accelerate AI Adoption Worldwide

TEL AVIV, Israel, March 15, 2022 /PRNewswire/ -- Run:ai, the company simplifying AI infrastructure orchestration and management, today announced that it has raised $75M in Series C round led by Tiger Global Management and Insight Partners, who led the previous Series B round. The round includes the participation of additional existing investors, TLV Partners and S Capital VC, bringing the total funding raised to date to $118M.

Run:ai has grown sharply, with a 9x increase in Annual Recurring Revenue in the last year, while the company's staff more than tripled over the same period. The company plans to use the investment to further grow its global teams and will also be considering strategic acquisitions as it develops and enhances the company's Atlas software platform.

Omri Geller, Run:ai CEO and co-founder, said, "It may sound dramatic, but AI is really the next phase of humanity's development. When we founded Run:ai, our vision was to build the de-facto foundational layer for running any AI workload. Our growth has been phenomenal, and this investment is a vote of confidence in our path. Run:ai is enabling organizations to orchestrate all stages of their AI work at scale, so companies can begin their AI journey and innovate faster."

Research firm IDC predicts global AI spending in 2022 will reach $433bn, nearly a 20% annual increase. However, AI infrastructure is complex to manage and scale. Companies see low hardware utilization, scheduling clashes and a delayed pace of innovation — if they can build functioning AI infrastructure at all.

Run:ai's Atlas platform provides a 'Foundation for AI Clouds', whether on premises, across public clouds, or at the edge, allowing organizations to have their AI resources on a single, unified platform that supports AI at all stages of development, from building and training models to running inference in production. Customers include Fortune 500 companies as well as cutting-edge AI startups from multiple verticals like finance, automotive, healthcare, and gaming, as well as leading academic AI research centers.

"We do for AI hardware what VMware and virtualization did for traditional computing — more efficiency, simpler management, greater user productivity. Traditional CPU computing has a rich software stack with many development tools for running applications at scale. AI, however, runs on dedicated hardware accelerators such as GPUs which have few tools to help with their implementation and scaling. With Run:ai Atlas, we've built a cloud-native software layer that abstracts AI hardware away from data scientists and ML engineers, letting Ops and IT simplify the delivery of compute resources for any AI workload and any AI project." said Ronen Dar, Run:ai CTO and co-founder.

"As enterprises in every industry reimagine themselves to become learning systems powered by AI and human talent, there has been a global surge in demand for AI hardware chipsets such as GPUs," said Lonne Jaffe, Managing Director at Insight Partners. "As the Forrester Wave AI Infrastructure report recently highlighted, Run:ai creates extraordinary value by bringing advanced virtualization and orchestration capabilities to AI chipsets, making training and inference systems run both much faster and more cost-effectively. Because of explosive demand since 2020, Run:ai has almost quadrupled its customer base, and we couldn't be more excited to double down on our partnership with Omri and the incredible Run:AI team as they lean into their momentum and Scale Up."

About Run:ai

Run:ai helps organizations accelerate their AI journey - from building initial models to scaling AI in production. Using Run:ai's Atlas software platform, companies streamline the development, management and scaling of AI applications across any infrastructure (on-premises, edge, cloud). Researchers gain on-demand access to pooled resources for any AI workload. An innovative, cloud-native operating-system helps IT manage everything from fractions of GPUs to large-scale distributed training. Learn more at www.run.ai.

SOURCE Run:ai