The Silicon Review
“We make sure your most demanding data intensive applications never have to wait for data, so you get to the answers faster.”
WekaIO, Inc. was founded on the recognition that data is rapidly becoming a key component of a long-term enterprise value and immediate access to large data sets is pivotal to the emerging artificial intelligence (AI) and machine learning revolution. AI initiatives cannot however be realized unless the data processing layer has immediate access to, and a constant supply of, data. Legacy storage systems, like NAS and SAN, cannot support deep learning and analytics workloads, because they were architected when spinning disk and slower networking technologies were the industry standard. Legacy storage technologies can’t keep pace with the I/O demands of new deep learning workloads, because the data gets bottlenecked between the storage and compute layers, leaving the applications starved for data and very expensive compute clusters idling. Furthermore, to keep pace with performance and capacity demands, conventional IT wisdom is to add NAS and SAN to the storage array, but this results in NAS sprawl and islands of storage, making data accessibility to the application difficult, all of which leads to poor AI and deep learning outcomes. WekaIO solves the data accessibility problem, making sure that the most demanding AI and machine learning workloads never have to wait for data.
Established in 2013, and with offices in San Jose, CA and Tel Aviv, Israel, WekaIO engineered the product from the ground-up to be the first low latency flash-optimized parallel file system, offering the most flexible deployment options. The solution accelerates compute-intensive applications by ensuring a constant supply of data to the applications, so that data scientists, researchers, and engineers get to the answer faster. The business benefits are shortened AI training cycles, better IT agility, and cost-savings by ensuring expensive compute resources are not idling waiting for data.
Early Successes in Machine Learning
WekaIO’s Matrix™ software is a next generation parallel and distributed file system that has been written from scratch to leverage the performance of flash and NVMe technology, ensuring the highest performance and scalability for the most demanding and unpredictable workloads—such as AI and machine learning. The software leverages standard X86 server infrastructure and NVMe storage; it virtualizes the SSDs into one logical pool of fast storage and can be coupled with inexpensive object storage data lake presenting a distributed, single namespace file system to the host applications. The fully POSIX compliant virtual file system can be deployed on dedicated storage servers (appliance model) or as a hyper-converged solution (with applications and storage integrated into the same server), on-premises or in the public cloud. Performance and capacity scale dynamically and independently, resulting in a storage system that delivers faster performance than an all-flash-array, the simplicity of file-based storage, the data durability of object storage systems, with the economics of the cloud.
The CEO of WekaIO, Liran Zvibel explained “Our first customer was a large electric vehicle manufacturer in Silicon Valley, who is also manufacturing autonomous driving vehicles (AVs). Our first installation at the customer was very successful: Customer results for WekaIO: 2x faster performance than local disk, 7.1x faster than the blade-based Flash-Array, and the GPGPUs are fully saturated all the time. This means that this AV manufacturer can run many more machine learning training Epochs in the same amount of time. We feel that the timing of the WekaIO Matrix product introduction is well timed with the rise of AI and machine learning workloads in the enterprise.”
Success Starts Within
WekaIO attributes much of their success to their people and culture. From Day One the effort has been to recruit and retain top talent in storage engineering, marketing and sales. File systems aren’t easy to build, they are complex, and it can take years to develop yet it had to be solid right out of the gate—this industry is unforgiving and has been burned by software launches that didn’t live up to the promises. The industry was, however, primed for reinvention, as legacy file systems were not architected to leverage the benefits of flash. WekaIO’s management team has extensive storage experience, gleaned from working at leading companies such as IBM, Intel, NetApp, EMC, and the founding team is from XIV, which was acquired by Intel in 2008.
Zvibel recalls, “Being part of a prior startup – XIV and later Fusik – I learned a lot about how companies succeed and frankly fail as well. Also, I realized the importance of quick decision-making, team and culture in running a business. It is not just about the technology.”
Prior to launch, the company was targeting EDA, but quickly learned that there was no urgent need for the customer to change, status quo was good enough. WekaIO quickly pivoted and started targeting markets like AI and machine learning where good enough performance and scalability from legacy storage systems isn’t frankly “good enough.” It realized that it was critical to focus on vertical niches where customers had a very intense small file I/O and metadata performance and scale pain point that only its technology can solve. This is exactly what the company found with AI, machine learning, and high-performance technical computing. And recent partnership announcements with HPE, Supermicro, and AWS are strong indicators that the global demand for hyperspeed, jumbo scale computing is heating up.
The Road Map Ahead
WekaIO anticipates that in a couple of years, it will be the preferred file system for data storage serving the AI, machine learning, analytics, and research markets—where performance and scale are the critical differentiators. WekaIO’s product roadmap will enable it to expand into broad manufacturing environments, healthcare, media and entertainment, and financial markets. Its product roadmap will focus on launching more enterprise features as well as plans to expand its cloud offering to multi-cloud environments.
The company is very focused on leveraging the channel as a go-to-market route, and its channel partners will be instrumental in its global expansion plans. Zvibel concluded, “We have a lot of innovation coming in the next year. I envision a day when HPC customers purchasing servers will insist that WekaIO be pre-installed on the server at the time of purchase.”
The Founding Triad
Liran Zvibel, Co-Founder, and CEO: As the CEO, Zvibel guides long-term vision and strategy at WekaIO. Prior to creating the opportunity at WekaIO, he ran engineering at social startup and Fortune 100 organizations. Zvibel holds a BSc. in Mathematics and Computer Science from Tel Aviv University.
Omri Palmon, Co-Founder, and COO: Dr. Palmon, a technology veteran for more than 30 years, brings his extensive background in software development, research and product management to WekaIO. Previously Dr. Palmon held various technical and product-centric positions in telecommunications and embedded software development. Dr. Palmon earned his Ph.D. in Computer Science from Stanford University and MSc. and BSc. degrees in pure mathematics from Tel Aviv University.
Maor Ben-Dayan, Co-Founder, and Chief Architect:
Maor Ben-Dayan, brings a wide range of skillset to the opportunities facing the company, skills won over nearly 20 years of deep technical experience in startup and large corporate environments. He holds Master’s degrees from Tel Aviv University and the Hebrew University of Jerusalem and a BSc. in Mathematics and Computer Science from Tel Aviv University.