WekaIO Quadruples Business in 2018 With Rapid Customer Adoption and Global Expansion

Customer base grows more than 400% year-over-year driven by adoption of NVME-native parallel file system for AI and Technical Compute workloads

SAN JOSE, Calif. – Jan 17, 2019 – WekaIO, the innovation leader in high-performance, scalable file storage for data intensive applications, today announced year-over-year customer growth in the US and EMEA, driven by the continued widespread adoption of the company’s NVME-native parallel file system for deep learning, analytics, and high performance computing (HPC) workloads.

Today, many companies are struggling to keep their computer infrastructure fully utilized—a pain felt most acutely by the large GPU farms being deployed for Machine Learning (ML) where large amounts of data must be shared across application servers. At scale, the compute environments become IO-bound, starving applications and wasting expensive GPU or CPU resources and resulting in long application run times. Due to the high costs and inefficiencies associated with managing these workloads on legacy storage systems, organizations are choosing to adopt WekaIO’s Matrix high-performance, massively parallel, shared file system.

“Implementing WekaIO Matrix made a high impact to our business—improving both time-to-market and opening up new lines of business for us,” said Alessandro Menegaz, IT Manager at TRE ALTAMIRA. “We were looking for a more modern solution that could provide improved performance and keep up with our fast-growing resource constraints.”

“We were looking for a solution that could simplify data management operations, given our limited resources,” said Sam Reid, Head of Technology at Untold Studios. “The scalability of WekaIO Matrix allowed us to start small and will grow as our business demands grow. The ease of use in addition to the performance, are further proof of the value of the WekaIO solution.”

Over the last year, revenue from the US and EMEA sales have grown more than 400%, with strong growth in verticals such as automotive AI, life sciences, media and entertainment, and HPC research. To support the increased demand, WekaIO has grown its channel partners by over 200% globally.

WekaIO has moreover doubled its employee headcount globally, finishing the year with the announcement of a Midwest office expansion. Additionally, significant new hires were brought on in sales, marketing, and engineering to help meet the expanding geographical demand for its solutions, while positioning the company for continued growth in the coming quarters.

“The rapid adoption of our solutions underscores the value of WekaIO’s technology and our strong relationship with our partners,” said Liran Zvibel, co-founder and CEO of WekaIO. “We have strong quarter-over-quarter customer and revenue growth, exceeding our expectations, and we have a healthy pipeline for sustained growth. It’s full speed ahead, as we expand in to new markets, open additional offices around the globe, and launch our channel program.”

In addition to opening new offices in the UK and Detroit, Mich., WekaIO was lauded with a steady flow of industry accolades that including Gartner Cool Vendor status in Storage Technologies, a place in Network World’s 10 Hot Hybrid-Cloud Startups to Watch, a spot in Silicon Review’s 10 Fastest Growing Storage Companies, and the AIConics Award. WekaIO Matrix delivers unmatched HPC class storage with enterprise class features and services, all in one solution, that provides IT with a robust platform to support data-intensive applications.

About WekaIO

WekaIO helps companies manage, scale and futureproof their data center so they can solve real problems that impact the world. WekaIO Matrix™, the world’s fastest shared parallel file system and WekaIO’s flagship product, leapfrogs legacy storage infrastructures by delivering simplicity, scale, and the best performance density per U, for a fraction of the cost. In the cloud or on-premises, WekaIO’s NVMe-native high-performance software-defined storage solution removes the barriers between the data and the compute layer, thus accelerating artificial intelligence, machine learning, genomics, research, and analytics workloads.

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