The round was also joined by investment firms, TLV Partners and S Capital VC, bringing Run:ai’s current total funding to $118 million.
Run:ai plans to use the new capital to continue growing its global teams and possibly facilitate strategic acquisitions as it develops and advances its Atlas software platform. 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.
Founded in 2018, Run:ai’s Atlas platform provides a ‘Foundation for AI Clouds’, enabling organizations to have all 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. Clients include Fortune 500 companies as well as AI-based startups centered around finance, automotive, healthcare, and gaming verticals, as well as academic AI research centers.
“It may sound dramatic, but AI is really the next phase of humanity’s development,” said Omri Geller, CEO and co-founder of Run:ai.
Research firm IDC estimates global AI spending in 2022 to reach $433 billion, representing an approximate 20% annual increase. But with the inherent complexities of managing and scaling AI infrastructure, companies often experience low hardware utilization, scheduling clashes, and lags in innovation if they can even build functioning AI infrastructure in the first place.
“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,” added Geller.
“We do for AI hardware what VMware and virtualization did for traditional computing — more efficiency, simpler management, greater user productivity,” said Ronen Dar, CTO and co-founder of Run:ai. “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 machine learning engineers, letting Ops and IT simplifies the delivery of compute resources for any AI workload and any AI project.”