Tidalscale Banks $24M Funding For ‘Software-Defined Servers’

TidalScale, which is a Campbell, Calif.-based Software-Defined Server company, today in a report has announced that they have raised an amount of $24m in the funding round of Series B.

Backers included Bain Capital Ventures, Hummer Winblad, Sapphire Ventures, Infosys, SK Hynix, and a server OEM, as well as other undisclosed investors. The company as of now has a plans to use the funds to accelerate adoption of its solutions.

The company which is founded and led by Gary Smerdon, President & CEO, and Chuck Piercey, Co-Founder, TidalScale provides Software-Defined Server technology solutions that enables organizations to build a virtual server of any size—the right size—in minutes. And once it’s up and running, the company’s real-time machine learning layer continuously optimizes system performance.

Gary Smerdon, President & CEO at TidalScale told in a statement that “TidalScale helps organizations sharpen their competitive advantage by making in-memory computing accessible with data sets that exceed the capabilities of even the largest traditional servers — with linear cost. Our breakthrough Software-Defined Server technology amplifies the value of modern data centers by enabling organizations to build a virtual server of any size-the right size-in just minutes. For our customers, TidalScale Software-Defined Servers have proven to be game-changing. We're honored that so many respected investors and partners recognize the value and promise of TidalScale.”


Organizations increasingly face workloads that overwhelm even their largest servers. To address this, customers are installing excess capacity based on the projected future requirements — thus possibly overpaying for years to come. The fast, flexible, easy and cost-effective solution to this problem is TidalScale's Software-Defined Server technology. With TidalScale, customers can create a Software-Defined Server from standard commodity physical servers in minutes.

TidalScale solutions deliver in-memory performance at any scale, are self-optimizing, use standard hardware and are compatible with all applications and operating systems – with no changes to applications or operating systems.

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