TidalScale, a provider of software-defined servers – right-sizing servers on demand, has now raised $27 million in an “oversubscribed” Series B funding.
“TidalScale’s unique technology has the potential to break through the limitations of today’s scale-up and scale-out solutions with the best of both worlds,” said Kevin Krewell, Principal Analyst at TIRIAS Research. “TidalScale technology can deliver the performance of in-memory computing with the scalability of traditional scale-out clusters, yet with the simplicity of monolithic scale-up servers.”
TidalScale’s “breakthrough” software-defined server technology would amplify the value of modern data centers by enabling organizations to build a virtual server of any size – the right size – in just minutes.
“We’re thrilled to see that investor interest in TidalScale continues unabated, with our Series B round now at $27 million,” said Gary Smerdon, President & CEO of TidalScale. “It’s gratifying that this growing group of visionary investors recognizes how TidalScale Software-Defined Servers help organizations sharpen their competitive advantage, help drive down software licensing costs and data center TCO, and improve application performance. With our groundbreaking software, TidalScale is transforming the economics of the data center.”
Additional investors, including Forte Ventures, joined the round since it was first announced Oct. 9. Forte Ventures joins a strong investment syndicate that includes Bain Capital Ventures, Hummer Winblad, Sapphire Ventures, Infosys, SK Hynix, and a “leading” server OEM, as well as other undisclosed investors.
“TidalScale’s innovative software breaks the mold for deploying in-memory workloads. We believe the cost-effectiveness, simplicity, and flexibility of software-defined servers will disrupt every IT organization’s approach to big data problems,” said Tom Hawkins, Managing Partner at Forte Ventures. “With each customer use case, TidalScale is demonstrating how beneficial its software-defined servers can be for organizations struggling to accommodate growing data sets and fluctuating workloads.”