Personal blog of Yzmir Ramirez

Taking on Amazon S3 in the cloud storage game would seem to be a fool-hearty proposition, but Wasabi has found a way to build storage cheaply and pass the savings onto customers. Today the Boston-based startup announced a $112 million Series C investment on a $700 million valuation.

Fidelity Management & Research Company led the round with participation from previous investors. It reports that it has now raised $219 million in equity so far, along with additional debt financing, but it takes a lot of money to build a storage business.

CEO David Friend says that business is booming and he needed the money to keep it going. “The business has just been exploding. We achieved a roughly $700 million valuation on this round, so  you can imagine that business is doing well. We’ve tripled in each of the last three years and we’re ahead of plan for this year,” Friend told me.

He says that demand continues to grow and he’s been getting requests internationally. That was one of the primary reasons he went looking for more capital. What’s more, data sovereignty laws require that certain types of sensitive data like financial and healthcare be stored in-country, so the company needs to build more capacity where it’s needed.

He says they have nailed down the process of building storage, typically inside co-location facilities, and during the pandemic they actually became more efficient as they hired a firm to put together the hardware for them onsite. They also put channel partners like managed service providers (MSPs) and value added resellers (VARs) to work by incentivizing them to sell Wasabi to their customers.

Wasabi storage starts at $5.99 per terabyte per month. That’s a heck of a lot cheaper than Amazon S3, which starts at 0.23 per gigabyte for the first 50 terabytes or $23.00 a terabyte, considerably more than Wasabi’s offering.

But Friend admits that Wasabi still faces headwinds as a startup. No matter how cheap it is, companies want to be sure it’s going to be there for the long haul and a round this size from an investor with the pedigree of Fidelity will give the company more credibility with large enterprise buyers without the same demands of venture capital firms.

“Fidelity to me was the ideal investor. […] They don’t want a board seat. They don’t want to come in and tell us how to run the company. They are obviously looking toward an IPO or something like that, and they are just interested in being an investor in this business because cloud storage is a virtually unlimited market opportunity,” he said.

He sees his company as the typical kind of market irritant. He says that his company has run away from competitors in his part of the market and the hyperscalers are out there not paying attention because his business remains a fraction of theirs for the time being. While an IPO is far off, he took on an institutional investor this early because he believes it’s possible eventually.

“I think this is a big enough market we’re in, and we were lucky to get in at just the right time with the right kind of technology. There’s no doubt in my mind that Wasabi could grow to be a fairly substantial public company doing cloud infrastructure. I think we have a nice niche cut out for ourselves, and I don’t see any reason why we can’t continue to grow,” he said.

Nearly three years after it was first launched, Amazon Web Services’ SageMaker platform has gotten a significant upgrade in the form of new features, making it easier for developers to automate and scale each step of the process to build new automation and machine learning capabilities, the company said.

As machine learning moves into the mainstream, business units across organizations will find applications for automation, and AWS is trying to make the development of those bespoke applications easier for its customers.

“One of the best parts of having such a widely adopted service like SageMaker is that we get lots of customer suggestions which fuel our next set of deliverables,” said AWS vice president of machine learning, Swami Sivasubramanian. “Today, we are announcing a set of tools for Amazon SageMaker that makes it much easier for developers to build end-to-end machine learning pipelines to prepare, build, train, explain, inspect, monitor, debug and run custom machine learning models with greater visibility, explainability and automation at scale.”

Already companies like 3M, ADP, AstraZeneca, Avis, Bayer, Capital One, Cerner, Domino’s Pizza, Fidelity Investments, Lenovo, Lyft, T-Mobile and Thomson Reuters are using SageMaker tools in their own operations, according to AWS.

The company’s new products include Amazon SageMaker Data Wrangler, which the company said was providing a way to normalize data from disparate sources so the data is consistently easy to use. Data Wrangler can also ease the process of grouping disparate data sources into features to highlight certain types of data. The Data Wrangler tool contains more than 300 built-in data transformers that can help customers normalize, transform and combine features without having to write any code.

Amazon also unveiled the Feature Store, which allows customers to create repositories that make it easier to store, update, retrieve and share machine learning features for training and inference.

Another new tool that Amazon Web Services touted was Pipelines, its workflow management and automation toolkit. The Pipelines tech is designed to provide orchestration and automation features not dissimilar from traditional programming. Using pipelines, developers can define each step of an end-to-end machine learning workflow, the company said in a statement. Developers can use the tools to re-run an end-to-end workflow from SageMaker Studio using the same settings to get the same model every time, or they can re-run the workflow with new data to update their models.

To address the longstanding issues with data bias in artificial intelligence and machine learning models, Amazon launched SageMaker Clarify. First announced today, this tool allegedly provides bias detection across the machine learning workflow, so developers can build with an eye toward better transparency on how models were set up. There are open-source tools that can do these tests, Amazon acknowledged, but the tools are manual and require a lot of lifting from developers, according to the company.

Other products designed to simplify the machine learning application development process include SageMaker Debugger, which enables developers to train models faster by monitoring system resource utilization and alerting developers to potential bottlenecks; Distributed Training, which makes it possible to train large, complex, deep learning models faster than current approaches by automatically splitting data across multiple GPUs to accelerate training times; and SageMaker Edge Manager, a machine learning model management tool for edge devices, which allows developers to optimize, secure, monitor and manage models deployed on fleets of edge devices.

Last but not least, Amazon unveiled SageMaker JumpStart, which provides developers with a searchable interface to find algorithms and sample notebooks so they can get started on their machine learning journey. The company said it would give developers new to machine learning the option to select several pre-built machine learning solutions and deploy them into SageMaker environments.

In June of this year, Chinese hackers managed to install software into the networks of a contractor for the U.S. Navy and steal information on a roughly $300 million top-secret submarine program.

Two years ago, hackers infiltrated the networks of a vendor servicing the Australian military and made off with files containing a trove of information on Australian and U.S. military hardware and plans. That hacker stole roughly 30 gigabytes of data, including information on the nearly half-a-trillion dollar F-35 Joint Strike Fighter program.

Third-party vendors, contractors and suppliers to big companies have long been the targets for cyber thieves looking for access to sensitive data, and the reason is simple. Companies don’t know how secure their suppliers really are and can’t take the time to find out.

“The Department of Defense can have the best cybersecurity on the planet, but when that moves off to a subcontractor how can the DOD know how the subcontractor is going to protect that data?” says Kelly White, the chief executive of RiskRecon, a new firm that provides audits of vendors’ security profile. 

The problem is one that the Salt Lake City-based executive knew well. White was a former security executive for Zion Bank Corporation after spending years in the cybersecurity industry with Ernst & Young and TrueSecure — a Washington, DC-based security vendor.

When White began work with Zion, around 2 percent of the company’s services were hosted by third parties; less than five years later and that number had climbed to over 50 percent. When White identified the problem in 2010, he immediately began developing a solution on his own time. RiskRecon’s chief executive estimates he spent 3,000 hours developing the service between 2010 and 2015, when he finally launched the business with seed capital from General Catalyst .

And White says the tools that companies use to ensure that those vendors have adequate security measures in place basically boiled down to an emailed checklist that the vendors would fill out themselves.

That’s why White built the RiskRecon service, which has just raised $25 million in a new round of funding led by Accel Partners with participation from Dell Technologies Capital, General Catalyst and F-Prime Capital, Fidelity Investments’ venture capital affiliate.

The company’s software looks at what White calls the “internet surface” of a vendor and maps the different ways in which that surface can be compromised. “We don’t require any insider information to get started,” says White. “The point of finding systems is to understand how well an organization is managing their risk.”

White says that the software does more than identify the weak points in a vendor’s security profile, it also tries to get a view into the type of information that could be exposed at different points on a network.

According to White, the company has more than 50 customers among the Fortune 500 that are already using his company’s services across industries like financial services, oil and gas and manufacturing.

The money from RiskRecon’s new round will be used to boost sales and marketing efforts as the company looks to expand into Europe, Asia and further into North America.

“Where there’s not transparency there’s often poor performance,” says White. “Cybersecurity has gone a long time without true transparency. You can’t have strong accountability without strong transparency.”