Container security startup Aqua lands $62M Series C

By Ron Miller

Aqua Security, a startup that helps customers launch containers securely, announced a $62 million Series C investment today led by Insight Partners.

Existing investors Lightspeed Venture Partners, M12 (Microsoft’s venture fund), TLV Partners and Shlomo Kramer also participated. With today’s investment, the startup’s investments since inception now total over $100 million, according to the company.

Early investors took a chance on the company when it was founded in 2015. Containers were barely a thing back then, but the founders had a vision of what was coming down the pike and their bet has paid off in a big way as the company now has first-mover advantage. As more companies turn to Kubernetes and containers, the need for a security product built from the ground up to secure this kind of environment is essential.

While co-founder and CEO Dror Davidoff says the company has 60 Fortune 500 customers, he’s unable to share names, but he can provide some clues like five of the world’s top banks. As companies like that turn to new technology like containers, they aren’t going to go whole hog without a solid security option. Aqua gives them that.

“Our customers are all taking very dramatic steps towards adoption of those new technologies, and they know that existing security tools that they have in place will not solve the problems,” Davidoff told TechCrunch. He said that most customers have started small, but then have expanded as container adoption increases.

You may think that an ephemeral concept like a container would be less of a security threat, but Davidoff says that the open nature of containerization actually leaves them vulnerable to tampering. “Container lives long enough to be dangerous,” he said. He added, “They are structured in an open way, making it simple to hack, and once in, to do lateral movement. If the container holds sensitive info, it’s easy to have access to that information.”

Aqua scans container images for malware and makes sure only certified images can run, making it difficult for a bad actor to insert an insecure image, but the ephemeral nature of containers also helps if something slips through. DevOp can simply take down the faulty container and put a newly certified clean one quickly.

The company has 150 employees with offices in the Boston area and R&D in Tel Aviv in Israel. With the new influx of cash, the company plans to expand quickly, growing sales and marketing, customer support and expanding the platform into areas to cover emerging areas like serverless computing. Davidoff says the company could double in size in the next 12-18 months and he’s expecting 3x to 4x customer growth.

All of that money should provide fuel to grow the company as containerization spreads and companies look for a security solution to keep containers in production safe.

Source. Techcrunch, Ron Miller, April 3, 2019

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Run.AI raises $13M for its distributed machine learning platform

By Frederic Lardinois

Aviv’s Run.AI, a startup that is building a new virtualization and acceleration platform for deep learning, is coming out of stealth today. As a part of this announcement, the company also announced that it has now raised a total of $13 million. This includes a $3 million seed round from TLV Partners and a $10 million Series A round led by Haim Sadger’s S Capital and TLV Partners.

It’s no secret that building deep learning models take a hefty amount of GPU power or access to specialized AI chips. Run.AI argues that the virtualization layers that worked so well for in the past don’t quite cut it for training today’s AI models.

“We believe that we’re only scratching the surface of the full potential of deep learning,” Run.AI CEO and co-founder Omri Geller told me. “But the computational infrastructure needs of deep learning are a totally different ballgame. […] The rise of deep learning is triggering a new era of compute.”

Traditionally, Geller argues, virtualization was all about being generous and sharing the resources of a single machine for workloads that typically only run for a short time or use a small amount of resources. Deep learning workloads, however, are very different and are essentially selfish in that they want to take over all the available compute resources of a given machine. These training sessions also typically run for hours or days. At its core, what Run.AI offers is a new virtualization layer for distributed machine learning tasks that can across a large number of machines.

“We built a compute abstraction layer that bridges the gap between the new form of workloads and the new hardware that is evolving,” said Geller. “By using this abstraction layer, we can achieve 100x faster compute using distributed computing. We can double the resource utilization of the hardware and we can bring to companies the control over time and cost regarding deep learning.” That’s 100x faster than using a single resource, though that’s a bit aspirational as Geller also tells me that the team is seeing about a 10x speedup in production right now, though he’s confident that the team will get to 100x over time. Either way, though, the promise here is that the service will allow you to optimize the utilization of your deep learning workloads.

That’s only one part of the company’s solution, though. In addition, the company’s tools also analyze the model in order to break it down into smaller models that can then run in parallel across these servers. With that, the service can understand how many resources a workload would need and what machines to best send the given workloads to. In doing this, the system takes into account everything from available compute resources to network bandwidth, as well as the data pipeline and size.

The company also argues that this allows it to train large models that are bigger than the individual GPU memory capacity of a single machine.

There’s a financial aspect to this, too, because users can determine whether they want the service to prioritize cost savings over training speed, for example. The platform supports both private and public cloud deployments. In private clouds, cost savings are obviously less of a factor but the premise of increased utlization of the existing hardware investment will likely be a draw for many of these users.

The company, which was founded by Geller, Dr. Ronen Dar and Prof. Meir Feder, was founded in 2018. While still in stealth, it signed a number of early customers and opened a U.S. office. 

Source. Techcrunch, By Frederic Lardinois, April 2, 2019

Note. This post was brought to you by Woewoda Communications, your partner in the private equity and startup markets; offering strategic communications, public relations & investor relation services to VCs, PEs, Angels, Endowments/Trusts, Family Offices, and Startups involved in ICT, IoT, blockchain, life sciences, healthcare, agribusiness, clean energy, fintech, AI and robotics.