Forward Networks raises $35M to help enterprises map, track, and predict their networks’ behavior

IT news

Security breaches and other activity that causes network surges and outages are all on the rise in the enterprise, and today, a startup called Forward Networks, which has built a clever way to help businesses monitor their network traffic to identify when things are going wrong, has raised a round of $35 million to continue expanding its business to meet that demand.

The money, a Series C, is being led by Goldman Sachs, which in this case is both a strategic and financial investor. David Erickson, the startup’s co-founder and CEO, said the investment bank started out as a customer, and Joshua Matheus, MD for technology at Goldman Sachs, was so pleased with the results that he recommended that the bank also invest in the company. Others participating in this round include Andreessen Horowitz, Threshold Ventures (previously DFJ Venture) and A. Capital, the three investors that were behind Forward Networks’ previous round of $16 million in 2017.

Erickson, along with other co-founders Nikhil Handigol, Brandon Heller and Peyman Kazemian, were all Stanford PhDs, and the company’s technology is based around work that they had done there around mathematical modelling. Here, that concept is applied to a company’s network to create essentially a replica of a company’s network architecture, which is in turn used to simulate individual processes and apps running on the network to figure out how they interact and what would represent “normal” versus “abnormal” behavior, which in turn is applied both in real time to monitor the network, and to predict what might happen on it. This is not a fixing platform per se, but in developer operations, there is a fundamental need and gap in the market for products that help engineers identify what is not working right in order to know what to try to fix.

If you are familiar with Honeycomb.io — a devops platform for running apps to determine when and where bugs or conflicts might arise (which itself recently raised funding) — this seems to be taking a similar approach but on a network scale.

Considered together, it seems that we’re starting to see a new wave of services and platforms designed to provide more granular and intelligent pictures of how apps and networks behave in our modern technology landscapes.

Erickson tells me that today, the vast majority of Forward Networks’ customers are using the product to monitor on-premises rather than cloud architectures.

“We launched a public cloud product for AWS towards the end of last year, which today is in use by customers, but the dominant use case for us is on-prem,” Erickson said, who said that while the media (ahem) loves to talk about cloud, in many cases large enterprises have actually been slower to migrate processes in cases where legacy services still work well, and they still harbour distrust of public cloud security and reliability. “We see growth towards the cloud but it’s baby steps.”

The company has been growing steadily and today its network monitoring covers some 75,000 devices. In that context, Goldman Sachs is a significant client, with some 15,000 devices in its network alone.

Looking ahead, Erickson said that the funding would be used in part for R&D and in part to continue its business development. The are a number of other solutions and services out there that have identified the opportunity of providing better network management as a route to identifying security threats and other risks, so that also presents an opportunity for M&A for Forward, although Erickson declined to comment further on that.

“We continue to see the value that Forward Networks’ platform brings to large enterprises running complex networks,” said Bill Krause, board partner at Andreessen Horowitz. “They have solved a critical business problem, which presents a real growth opportunity.”

 

Forward Networks raises $35M to help enterprises map, track, and predict their networks’ behavior Forward Networks raises $35M to help enterprises map, track, and predict their networks’ behavior XWG5aPEt700

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