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CloudBolt announces $35M Series B debt/equity investment to help manage hybrid cloud
CloudBolt, a Bethesda, Maryland startup that helps companies manage hybrid cloud environments, announced a $35 million Series B investment today. It was split between $15 million in equity investment and $20 million in debt.
Insight Partners provided the equity side of the equation, while Hercules Capital and Bridge Bank supplied the venture debt. The company has now raised more than $61 million in equity and debt, according to Crunchbase data.
CEO Jeff Kukowski says that his company helps customers with cloud and DevOps management including cost control, compliance and security. “We help [our customers] take advantage of the fact that most organizations are already hybrid cloud, multi cloud and/or multi tool. So you have all of this innovation happening in the world, and we make it easier for them to take advantage of it,” he said.
As he sees it, the move to cloud and DevOps, which was supposed to simplify everything, has actually created new complexity, and the tools his company sells are designed to help companies reduce some of that added complexity. What they do is provide a way to automate, secure and optimize their workloads, regardless of the tools or approach to infrastructure they are using.
The company closed the funding round at the end of last quarter and put it to work with a couple of acquisitions — Kumolus and SovLabs — to help accelerate and fill in the road map. Kumolus, which was founded in 2011 and raised $1.7 million, according to Crunchbase, really helps CloudBolt extend its vision from managing on premises to the public cloud.
SovLabs was an early-stage startup working on a very specific problem creating a framework for extending VMware automation.
CloudBolt currently has 170 employees. While Kukowski didn’t want to get specific about the number of additional employees he might be adding to that in the next 12 months, he says that as he does, he thinks about diversity in three ways.
“One is just pure education. So we as a company regularly meet and educate on issues around inclusion, social justice and diversity. We also recruit with those ideas in mind. And then we also have a standing committee within the company that continues to look at issues not only for discussion, but quite frankly for investment in terms of time and fundraising,” he said.
Kukowski says that going remote because of COVID has allowed the company to hire from anywhere, but he still looks forward to a time when he can meet face-to-face with his employees and customers, and sees that as always being part of his company’s culture.
CloudBolt was founded in 2012 and has around 200 customers. Kukowski says that the company is growing between 40% and 50% year over year, although he wouldn’t share specific revenue numbers.
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Deep Vision announces its low-latency AI processor for the edge
Deep Vision, a new AI startup that is building an AI inferencing chip for edge computing solutions, is coming out of stealth today. The six-year-old company’s new ARA-1 processors promise to strike the right balance between low latency, energy efficiency and compute power for use in anything from sensors to cameras and full-fledged edge servers.
Because of its strength in real-time video analysis, the company is aiming its chip at solutions around smart retail, including cashier-less stores, smart cities and Industry 4.0/robotics. The company is also working with suppliers to the automotive industry, but less around autonomous driving than monitoring in-cabin activity to ensure that drivers are paying attention to the road and aren’t distracted or sleepy.
Image Credits: Deep Vision
The company was founded by its CTO Rehan Hameed and its Chief Architect Wajahat Qadeer, who recruited Ravi Annavajjhala, who previously worked at Intel and SanDisk, as the company’s CEO. Hameed and Qadeer developed Deep Vision’s architecture as part of a PhD thesis at Stanford.
“They came up with a very compelling architecture for AI that minimizes data movement within the chip,” Annavajjhala explained. “That gives you extraordinary efficiency — both in terms of performance per dollar and performance per watt — when looking at AI workloads.”
Long before the team had working hardware, though, the company focused on building its compiler to ensure that its solution could actually address its customers’ needs. Only then did they finalize the chip design.
Image Credits: Deep Vision
As Hameed told me, Deep Vision’s focus was always on reducing latency. While its competitors often emphasize throughput, the team believes that for edge solutions, latency is the more important metric. While architectures that focus on throughput make sense in the data center, Deep Vision CTO Hameed argues that this doesn’t necessarily make them a good fit at the edge.
“[Throughput architectures] require a large number of streams being processed by the accelerator at the same time to fully utilize the hardware, whether it’s through batching or pipeline execution,” he explained. “That’s the only way for them to get their big throughput. The result, of course, is high latency for individual tasks and that makes them a poor fit in our opinion for an edge use case where real-time performance is key.”
To enable this performance — and Deep Vision claims that its processor offers far lower latency than Google’s Edge TPUs and Movidius’ MyriadX, for example — the team is using an architecture that reduces data movement on the chip to a minimum. In addition, its software optimizes the overall data flow inside the architecture based on the specific workload.
Image Credits: Deep Vision
“In our design, instead of baking in a particular acceleration strategy into the hardware, we have instead built the right programmable primitives into our own processor, which allows the software to map any type of data flow or any execution flow that you might find in a neural network graph efficiently on top of the same set of basic primitives,” said Hameed.
With this, the compiler can then look at the model and figure out how to best map it on the hardware to optimize for data flow and minimize data movement. Thanks to this, the processor and compiler can also support virtually any neural network framework and optimize their models without the developers having to think about the specific hardware constraints that often make working with other chips hard.
“Every aspect of our hardware/software stack has been architected with the same two high-level goals in mind,” Hameed said. “One is to minimize the data movement to drive efficiency. And then also to keep every part of the design flexible in a way where the right execution plan can be used for every type of problem.”
Since its founding, the company has raised about $19 million and filed nine patents. The new chip has been sampling for a while, and even though the company already has a couple of customers, it chose to remain under the radar until now. The company obviously hopes that its unique architecture can give it an edge in this market, which is getting increasingly competitive. Besides the likes of Intel’s Movidius chips (and custom chips from Google and AWS for their own clouds), there are also plenty of startups in this space, including the likes of Hailo, which raised a $60 million Series B round earlier this year and recently launched its new chips, too.
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Arrikto raises $10M for its MLOps platform
Arrikto, a startup that wants to speed up the machine learning development lifecycle by allowing engineers and data scientists to treat data like code, is coming out of stealth today and announcing a $10 million Series A round. The round was led by Unusual Ventures, with Unusual’s John Vrionis joining the board.
“Our technology at Arrikto helps companies overcome the complexities of implementing and managing machine learning applications,” Arrikto CEO and co-founder Constantinos Venetsanopoulos explained. “We make it super easy to set up end-to-end machine learning pipelines. More specifically, we make it easy to build, train, deploy ML models into production using Kubernetes and intelligent intelligently manage all the data around it.”
Like so many developer-centric platforms today, Arrikto is all about “shift left.” Currently, the team argues, machine learning teams and developer teams don’t speak the same language and use different tools to build models and to put them into production.
Image Credits: Arrikto
“Much like DevOps shifted deployment left, to developers in the software development life cycle, Arrikto shifts deployment left to data scientists in the machine learning life cycle,” Venetsanopoulos explained.
Arrikto also aims to reduce the technical barriers that still make implementing machine learning so difficult for most enterprises. Venetsanopoulos noted that just like Kubernetes showed businesses what a simple and scalable infrastructure could look like, Arrikto can show them what a simpler ML production pipeline can look like — and do so in a Kubernetes-native way.
Arrikto CEO Constantinos Venetsanopoulos. Image Credits: Arrikto
At the core of Arrikto is Kubeflow, the Google -incubated open-source machine learning toolkit for Kubernetes — and in many ways, you can think of Arrikto as offering an enterprise-ready version of Kubeflow. Among other projects, the team also built MiniKF to run Kubeflow on a laptop and uses Kale, which lets engineers build Kubeflow pipelines from their JupyterLab notebooks.
As Venetsanopoulos noted, Arrikto’s technology does three things: it simplifies deploying and managing Kubeflow, allows data scientists to manage it using the tools they already know, and it creates a portable environment for data science that enables data versioning and data sharing across teams and clouds.
While Arrikto has stayed off the radar since it launched out of Athens, Greece in 2015, the founding team of Venetsanopoulos and CTO Vangelis Koukis already managed to get a number of large enterprises to adopt its platform. Arrikto currently has more than 100 customers and, while the company isn’t allowed to name any of them just yet, Venetsanopoulos said they include one of the largest oil and gas companies, for example.
And while you may not think of Athens as a startup hub, Venetsanopoulos argues that this is changing and there is a lot of talent there (though the company is also using the funding to build out its sales and marketing team in Silicon Valley). “There’s top-notch talent from top-notch universities that’s still untapped. It’s like we have an unfair advantage,” he said.
“We see a strong market opportunity as enterprises seek to leverage cloud-native solutions to unlock the benefits of machine learning,” Unusual’s Vrionis said. “Arrikto has taken an innovative and holistic approach to MLOps across the entire data, model and code lifecycle. Data scientists will be empowered to accelerate time to market through increased automation and collaboration without requiring engineering teams.”
Image Credits: Arrikto
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Gretel announces $12M Series A to make it easier to anonymize data
As companies work with data, one of the big obstacles they face is making sure they are not exposing personally identifiable information (PII) or other sensitive data. It usually requires a painstaking manual effort to strip out that data. Gretel, an early-stage startup, wants to change that by making it faster and easier to anonymize data sets. Today the company announced a $12 million Series A led by Greylock. The company has now raised $15.5 million.
Gretel co-founder and CEO Alex Watson says that his company was founded to make it simpler to anonymize data and unlock data sets that were previously out of reach because of privacy concerns.
“As a developer, you want to test an idea or build a new feature, and it can take weeks to get access to the data you need. Then essentially it boils down to getting approvals to get started, then snapshotting a database, and manually removing what looks like personal data and hoping that you got everything.”
Watson, who previously worked as a GM at AWS, believed that there needed to be a faster and more reliable way to anonymize the data, and that’s why he started Gretel. The first product is an open-source, synthetic machine learning library for developers that strips out personally identifiable information.
“Developers use our open source library, which trains machine learning models on their sensitive data, then as that training is happening we are enforcing something called differential privacy, which basically ensures that the model doesn’t memorize details about secrets for individual people inside of the data,” he said. The result is a new artificial data set that is anonymized and safe to share across a business.
The company was founded last year, and they have actually used this year to develop the open-source product and build an open-source community around it. “So our approach and our go-to-market here is we’ve open-sourced our underlying libraries, and we will also build a SaaS service that makes it really easy to generate synthetic data and anonymized data at scale,” he said.
As the founders build the company, they are looking at how to build a diverse and inclusive organization, something that they discuss at their regular founders’ meetings, especially as they look to take these investment dollars and begin to hire additional senior people.
“We make a conscious effort to have diverse candidates apply, and to really make sure we reach out to them and have a conversation, and that’s paid off, or is in the process of paying off I would say, with the candidates in our pipeline right now. So we’re excited. It’s tremendously important that we avoid group think that happens so often,” he said.
The company doesn’t have paying customers, but the plan is to build off the relationships it has with design partners and begin taking in revenue next year. Sridhar Ramaswamy, the partner at Greylock who is leading the investment, says that his firm is placing a bet on a pre-revenue company because he sees great potential for a service like this.
“We think Gretel will democratize safe and controlled access to data for the whole world the way GitHub democratized source code access and control,” Ramaswamy said.
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Mirantis brings extensions to its Lens Kubernetes IDE, launches a new Kubernetes distro
Earlier this year, Mirantis, the company that now owns Docker’s enterprise business, acquired Lens, a desktop application that provides developers with something akin to an IDE for managing their Kubernetes clusters. At the time, Mirantis CEO Adrian Ionel told me that the company wants to offer enterprises the tools to quickly build modern applications. Today, it’s taking another step in that direction with the launch of an extensions API for Lens that will take the tool far beyond its original capabilities.
In addition to this update to Lens, Mirantis also today announced a new open-source project: k0s. The company describes it as “a modern, 100% upstream vanilla Kubernetes distro that is designed and packaged without compromise.”
It’s a single optimized binary without any OS dependencies (besides the kernel). Based on upstream Kubernetes, k0s supports Intel and Arm architectures and can run on any Linux host or Windows Server 2019 worker nodes. Given these requirements, the team argues that k0s should work for virtually any use case, ranging from local development clusters to private data centers, telco clusters and hybrid cloud solutions.
“We wanted to create a modern, robust and versatile base layer for various use cases where Kubernetes is in play. Something that leverages vanilla upstream Kubernetes and is versatile enough to cover use cases ranging from typical cloud based deployments to various edge/IoT type of cases,” said Jussi Nummelin, senior principal engineer at Mirantis and founder of k0s. “Leveraging our previous experiences, we really did not want to start maintaining the setup and packaging for various OS distros. Hence the packaging model of a single binary to allow us to focus more on the core problem rather than different flavors of packaging such as debs, rpms and what-nots.”
Mirantis, of course, has a bit of experience in the distro game. In its earliest iteration, back in 2013, the company offered one of the first major OpenStack distributions, after all.
Image Credits: Mirantis
As for Lens, the new API, which will go live next week to coincide with KubeCon, will enable developers to extend the service with support for other Kubernetes-integrated components and services.
“Extensions API will unlock collaboration with technology vendors and transform Lens into a fully featured cloud native development IDE that we can extend and enhance without limits,” said Miska Kaipiainen, the co-founder of the Lens open-source project and senior director of engineering at Mirantis. “If you are a vendor, Lens will provide the best channel to reach tens of thousands of active Kubernetes developers and gain distribution to your technology in a way that did not exist before. At the same time, the users of Lens enjoy quality features, technologies and integrations easier than ever.”
The company has already lined up a number of popular CNCF projects and vendors in the cloud-native ecosystem to build integrations. These include Kubernetes security vendors Aqua and Carbonetes, API gateway maker Ambassador Labs and AIOps company Carbon Relay. Venafi, nCipher, Tigera, Kong and StackRox are also currently working on their extensions.
“Introducing an extensions API to Lens is a game-changer for Kubernetes operators and developers, because it will foster an ecosystem of cloud-native tools that can be used in context with the full power of Kubernetes controls, at the user’s fingertips,” said Viswajith Venugopal, StackRox software engineer and developer of KubeLinter. “We look forward to integrating KubeLinter with Lens for a more seamless user experience.”
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Solo.io announces service mesh platform aimed at enterprise customers
Solo.io, a Cambridge, Massachusetts service mesh startup, announced some big changes to its approach today with a full-stack platform of services aimed squarely at the enterprise. The culmination of this will be Gloo Mesh Enterprise, a new product that will be available in beta by the end of the year.
Service meshes are part of a cloud native, containerized approach to development that enable micro services to communicate with one another.
Idit Levine, founder and CEO at Solo, says that she began by creating individual components since launching the company in 2017 because she knew that it was early for service meshes. Today’s announcement is about bringing all of these components the company has created into a more coherent and connected enterprise product.
While she was worried at first that the pandemic would have a negative impact on business, she says that her company has been busier than ever and today’s announcement is really about giving customers what they have been asking for throughout this tumultuous year.
Most of Solo’s customers are running Kubernetes and they needed some missing pieces that Solo was happy to provide for them. The first problem is the primary reason the company started, which was to manage service meshes, and Gloo Mesh, which is based on the open-source Istio service mesh, helps developers manage their service mesh clusters.
Another problem involved running containers at the edge, which required an API gateway. To that end, the company announced Gloo Edge, an API gateway built on the Envoy Proxy, an edge service proxy. Running applications at the edge means they get the resources they need to improve performance and save bandwidth.
The third piece is called Gloo Portal. This provides a centralized, self-service catalog of services that developers can tap into as they are building their applications. The final piece is Gloo Extensions, which provides a way for developers to access or build extensions called web assembly modules.
All of these pieces are available as open source, but companies that want additional functionality and support and a way to connect all of these pieces will need to buy the enterprise product. Among the additional features in the enterprise version is the ability to apply roles to the APIs in Gloo Edge to control who has access. Gloo Mesh users get production Istio support including updates and patches. It also includes a dashboard for managing clusters and developer tools for building web assembly pieces in Gloo Extension.
The company has raised more than $36 million, according to PitchBook data. The most recent deal was $23 million in September. Levine says the startup has several dozen large customers at this point, and 35 employees. She said she is actively hiring and expects to be at 50 soon.
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Mozart Data lands $4M seed to provide out-of-the-box data stack
Mozart Data founders Peter Fishman and Dan Silberman have been friends for over 20 years, working at various startups, and even launching a hot sauce company together along the way. As technologists, they saw companies building a data stack over and over. They decided to provide one for them and Mozart Data was born.
The company graduated from the Y Combinator Summer 2020 cohort in August and announced a $4 million seed round today led by Craft Ventures and Array Ventures with participation from Coelius Capital, Jigsaw VC, Signia VC, Taurus VC and various angel investors.
In spite of the detour into hot sauce, the two founders were mostly involved in data over the years and they formed strong opinions about what a data stack should look like. “We wanted to bring the same stack that we’ve been building at all these different startups, and make it available more broadly,” Fishman told TechCrunch.
They see a modern data stack as one that has different databases, SaaS tools and data sources. They pull it together, process it and make it ready for whatever business intelligence tool you use. “We do all of the parts before the BI tool. So we extract and load the data. We manage a data warehouse for you under the hood in Snowflake, and we provide a layer for you to do transformations,” he said.
The service is aimed mostly at technical people who know some SQL like data analysts, data scientists and sales and marketing operations. They founded the company earlier this year with their own money, and joined Y Combinator in June. Today, they have about a dozen customers and six employees. They expect to add 10-12 more in the next year.
Fishman says they have mostly hired from their networks, but have begun looking outward as they make their next hires with a goal of building a diverse company. In fact, they have made offers to several diverse candidates, who didn’t ultimately take the job, but he believes if you start looking at the top of the funnel, you will get good results. “I think if you spend a lot of energy in terms of top of funnel recruiting, you end up getting a good, diverse set at the bottom,” he said.
The company has been able to start from scratch in the midst of a pandemic and add employees and customers because the founders had a good network to pitch the product to, but they understand that moving forward they will have to move outside of that. They plan to use their experience as users to drive their message.
“I think talking about some of the whys and the rationale is our strategy for adding value to customers […], it’s about basically how would we set up a data stack if we were at this type of startup,” he said.
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5 UX design research mistakes you can stop making today
Jason Buhle is a professor in the online Master of Science in the Applied Psychology program at the University of Southern California and Director of UX Strategy at AnswerLab, the largest independent consultancy exclusively focused on UX research.
A recent article in Entrepreneur magazine listed “inadequate testing” as the top reason why startups fail. Inadequate testing essentially means inadequate or sub-par user research that leads to poor UX design which, not surprisingly, usually ends in failure. While working with startups and tech companies, I have also seen how even when people know how important user research is, they may not necessarily know how to conduct it in optimal ways.
Let’s look, then, at some of the biggest UX research mistakes companies make and what I wish I had known when I first started.
Conduct UX research early and throughout product development
When considering any potential product or service, it’s best to get certain questions answered as soon as possible. Is it actually going to be something useful and feasible for the target users and their organizations? Are your initial; assumptions correct? Ideas that seem good at first may not seem so great after research, and many commonly criticized failures were likely results of insufficient research. This is why it’s vital to begin user research early before product development has even begun.
While it is important to conduct foundational research early on, you also want to make sure to conduct evaluative research by continuously testing your product as you build or upgrade it. One of the reasons why Google products product like Gmail or YouTube are relatively easy to use for most people is that Google has teams continuously testing their products, making sure that their users know where to find what they’re looking for.
Don’t do all of the user research yourself
One of the mistakes I see many startups and entrepreneurs make (and that I myself made early on) is doing all of the UX research themselves. In some ways, books like “Lean Startup” have bolstered this tendency by stressing the need to “get out of the building” and get to know your users. In itself this isn’t a bad idea—it’s good to know who your users are and to build empathy for their experiences. Likewise, this isn’t to say that you should not do any research yourselves.
However, you also want to be sure to complement that by having professional, third party UX researchers do research for you as well. When you are heavily invested in your research, as you invariably would be if it is your own product, it is difficult to conduct it in an unbiased way. And when your research participants know that you are asking them about your own project, they are not likely to provide you with good signal that can actually help you improve your product.
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AWS launches its next-gen GPU instances
AWS today announced the launch of its newest GPU-equipped instances. Dubbed P4, these new instances are launching a decade after AWS launched its first set of Cluster GPU instances. This new generation is powered by Intel Cascade Lake processors and eight of Nvidia’s A100 Tensor Core GPUs. These instances, AWS promises, offer up to 2.5x the deep learning performance of the previous generation — and training a comparable model should be about 60% cheaper with these new instances.
Image Credits: AWS
For now, there is only one size available, the p4d.12xlarge instance, in AWS slang, and the eight A100 GPUs are connected over Nvidia’s NVLink communication interface and offer support for the company’s GPUDirect interface as well.
With 320 GB of high-bandwidth GPU memory and 400 Gbps networking, this is obviously a very powerful machine. Add to that the 96 CPU cores, 1.1 TB of system memory and 8 TB of SSD storage and it’s maybe no surprise that the on-demand price is $32.77 per hour (though that price goes down to less than $20/hour for one-year reserved instances and $11.57 for three-year reserved instances.
Image Credits: AWS
On the extreme end, you can combine 4,000 or more GPUs into an EC2 UltraCluster, as AWS calls these machines, for high-performance computing workloads at what is essentially a supercomputer-scale machine. Given the price, you’re not likely to spin up one of these clusters to train your model for your toy app anytime soon, but AWS has already been working with a number of enterprise customers to test these instances and clusters, including Toyota Research Institute, GE Healthcare and Aon.
“At [Toyota Research Institute], we’re working to build a future where everyone has the freedom to move,” said Mike Garrison, Technical Lead, Infrastructure Engineering at TRI. “The previous generation P3 instances helped us reduce our time to train machine learning models from days to hours and we are looking forward to utilizing P4d instances, as the additional GPU memory and more efficient float formats will allow our machine learning team to train with more complex models at an even faster speed.”
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Twilio wraps $3.2B purchase of Segment after warp-speed courtship
It was barely a month ago we began hearing rumors that Twilio was interested in acquiring Segment. The $3.2 billion deal was officially announced three weeks ago, and this morning the communications API company announced that the deal had closed, astonishingly fast for an acquisition of this size.
While we can’t know for sure, the speed with which the deal closed could suggest that it was in the works longer than we had known, and when we began hearing rumors of the acquisition, it could have already been signed, sealed and delivered. In addition, the fact that Twilio CEO Jeff Lawson and Segment CEO Peter Reinhardt knew one another before coming to terms might have helped accelerate the process.
Regardless, the two companies are a nice fit. Both deal with the API economy, providing a set of tools to help developers easily add a particular set of functions to their applications. For Twilio, that’s a set of communications APIs, while Segment focuses on customer data.
When you pull the two sets of tooling together, and combine that with Twilio’s 2018 SendGrid acquisition, you can see the possibility to build more complete applications for interacting with customers at every level, including basic communications like video, SMS and audio from Twilio, as well as customer data from Segment and customized emails and ads based on those interactions from SendGrid.
As companies increasingly focus on digital engagement, especially in the midst of a pandemic, Twilio’s Lawson believes the biggest roadblock to this type of engagement has been that data has been locked in silos, precisely the kind of problem that Segment has been attacking.
“With the addition of Segment, Twilio’s Customer Engagement Platform now enables companies to both understand their customer and engage with them digitally — the combination is key to building great digital experiences,” Lawson said in a statement.
In a recent post looking at the reasoning behind the deal, Brent Leary, founder and principal analyst at CRM Essentials, saw it this way: “This move allows Twilio to impact the data-insight-interaction-experience transformation process by removing friction from developers using their platform,” Leary explained.
With the deal closed, Segment will become a division of Twilio. Reinhardt will continue to be CEO, and will report directly to Lawson.
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