SAN JOSE, Calif.--(BUSINESS WIRE)--WekaIO, the innovation leader in high-performance, scalable file storage for data-intensive applications, today announced it has closed $31.7 million in a Series C funding round fueled by existing investors and new strategic investors from the storage industry, including Hewlett Packard Enterprise (HPE), Mellanox Technologies, NVIDIA, Seagate, and Western Digital Capital, the strategic investment fund of Western Digital Corp. (WDC), alongside Qualcomm which invested previously. The new funding brings the company’s total capital raised to $66.7 million.
WekaIO plans to use the investment primarily to support the continued acceleration of its market trajectory and international business expansion through increased investments in engineering, sales, and marketing, giving WekaIO Matrix™ clear market advantage in addressing data intensive applications at scale in key verticals including artificial intelligence (AI), machine learning (ML), life sciences, finance and engineering.
Matrix, the world’s fastest file system and WekaIO’s flagship product, is widely used by customers with I/O intensive workloads commonly found in AI, ML, high velocity analytics, and high-performance computing (HPC). WekaIO delivers its patented software on pre-engineered solutions with HPE, Supermicro, and Western Digital or it can be installed on any industry-standard Intel x86-based server or used on the AWS Cloud to deliver enterprise grade HPC at cloud-scale economics. The company’s capacity-based pricing model allows customers to start small and scale as their needs grow. Matrix tightly integrates object storage for best economics at scale and supports features such as snapshots, cloud backup and disaster recovery, alongside multiprotocol support including NFS and SMB. The NVMe-optimized and POSIX-compliant file system delivers the industry’s leading performance and the lowest latency, as measured by independent SPEC SFS 2014 benchmark testing and VI4IO’s 10 Node Challenge list.
“This latest round of financing sets the stage for substantial growth and allows us to continue our mission to deliver an enterprise grade HPC storage solution at cloud scale economics,” says Liran Zvibel, CEO and co-founder of WekaIO. “Modern workloads need a modern file system and legacy solutions just can’t keep pace. In under two years since our launch from stealth, we’ve been lauded with industry awards and accolades, been validated in production environments with leading enterprise organizations, and broken records on industry leading benchmark tests beating out some of the world’s largest supercomputers. This additional capital will allow us to increase our presence worldwide and continue to innovate in order to exceed the evolving needs of our customers.”
“We are seeing an explosion of artificial intelligence, machine and deep learning along with high-performance computing in the enterprise market,” said Paul Glaser, Head of Pathfinder, HPE. “By combining HPE’s industry-leading server architecture with WekaIO’s performance-leading software in an integrated, tested and validated package, we can deliver best of breed solutions to our customers. This is a great example of HPE’s Pathfinder program that seeks to partner with emerging companies that are strategically aligned with HPE to drive innovative customer solutions and benefits.”
“We have been demonstrating performance improvements for mutual customers with WekaIO since they launched from stealth in 2017. The combination of Matrix software and Mellanox Ethernet and InfiniBand adapters delivers a high-performance storage solution that can address a broad range of use cases in AI, machine learning, High Performance Computing and high velocity analytics,” commented Nimrod Gindi, Senior Vice President of M&A and Head of Investments, Mellanox Technologies. “We are enthusiastic to support WekaIO in this round of funding to encourage the development of emerging technologies, like Matrix.”
“As the world leader in accelerated computing, NVIDIA is at the forefront of AI innovation,” added Jeff Herbst, Vice President of Business Development, NVIDIA. “WekaIO has pioneered an impressive modern parallel file system that delivers important capabilities to accelerate AI and workloads at scale—with high throughput, high bandwidth and low latency data access to GPU-based servers.”
"With the increase of data-hungry and low latency AI applications in the enterprise, we anticipate that the combination of WekaIO’s cutting-edge NVMe solutions tiered to S3 data pools on Seagate’s Multi Actuator based storage products will provide unparalleled scale, performance and TCO. We are excited to partner with WekaIO, and look forward to a close relationship,” said Ravi Naik, Senior Vice President of Corporate Strategy & CIO, Seagate. “Seagate's mission is to maximize data's potential, so we are excited to support the development of NVMe-native file systems like WekaIO Matrix that were built from the ground up to maximize the efficiencies of flash to support these modern workloads.”
WekaIO was awarded Gartner Cool Vendor, a spot in Silicon Review’s 10 Fastest Growing Storage Companies list, the Artificial Intelligence Excellence Award and the AIConics Award. These achievements highlight the company’s continued industry momentum to deliver unmatched HPC-class storage with enterprise-level features and services, all in one solution, that provides IT with a robust platform to support data-intensive applications.
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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