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Elastic acquires build.security for security policy definition and enforcement
Less than a year after raising its $6 million seed funding round, Tel Aviv and Sunnyvale-based startup build.security is being acquired by Elastic. Financial terms of the deal are not being publicly disclosed at this time. The deal is expected to close in Elastic’s Q2 FY22, ending October 31, 2021.
In an email to TechCrunch, Ash Kulkarni, chief product officer at Elastic, said that once the acquisition closes, the build.security technical team will continue as a unit in the Elastic Security organization. Kulkarni added that the acquisition will also become the foundation for a growing Elastic presence in Israel, with Amit Kanfer, co-founder and CEO of build.security, set to become the site lead for the region.
Build.security is focused on security policy management for applications. A core element of the company’s technology approach is the Open Policy Agent (OPA) open-source project, which is part of the Cloud Native Computing Foundation (CNCF), which is also home to Kubernetes. OPA was originally started by startup Styra, which itself has raised $40 million in funding to help build out policy management and authorization technology. Part of OPA is the Rego query language, which is used to structure security and authorization configuration policies.
“We see policy as a fundamental cornerstone of security,” Kulkarni said. “OPA and Rego provide an open, standards-based way to define, manage and enforce policies everywhere.”
Kulkarni noted that security policy technology is complementary to Elastic’s efforts in security and observability. He added that Elastic sees potential for using OPA and the technology that build.security has built on top of OPA to power deployment time, and in the future, build-time security for cloud-native environments.
YL Venture partner John Brennan, who helped to lead the seed round of build.security, sees the acquisition as being a good fit for both companies, as they are both creating solutions for developers that are based on open-source technologies.
“This move by a market leader like Elastic validates the need for transformation in the authorization space,” Brennan said. “This partnership will accelerate build.security’s shift-left vision of efficiently embedding access protection from the start, rather than trying to bolt it on after the fact or, worse, ignoring it completely.”
Elastic is known for its Elastic Stack, which provides Elasticsearch search capability, Logstash log monitoring and Kibana data visualization. In recent years the company has expanded into the security space, acquiring Endgame Security in 2019 for $234 million. On August 3, Elastic announced its Limitless XDR capabilities, which brings together endpoint security with security information and event management (SIEM).
With its new acquisition, Kulkarni said the goal is to go even deeper into security moving toward cloud security enforcement. He explained that after the acquisition closes and as the technology is integrated, users will be able to leverage the Elastic Stack to visualize and manage compliance policies and policy decisions at scale. An initial use-case for the build.security technology will be developing a Kubernetes security and compliance product based on OPA.
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Render raises $4.5M for its DevOps platform
Render, the winner of our Disrupt SF 2019 Startup Battlefield, today announced that it has added another $4.5 million onto its existing seed funding round, bringing total investment into the company to $6.75 million.
The round was led by General Catalyst, with participation from previous investors South Park Commons Fund and a group of angels that includes Lee Fixel, Elad Gil and GitHub CTO (and former VP of Engineering at Heroku) Jason Warner.
The company, which describes itself as a “Zero DevOps alternative to AWS, Azure and Google Cloud,” originally raised a $2.25 million seed round in April 2019, but it got a lot of inbound interest after winning the Disrupt Battlefield. In the end, though, the team decided to simply raise more money from its existing investors.
Current Render users include Cypress.io, Mux, Bloomscape, Zelos, 99designs and Stripe.
“We spoke to a bunch of people after Disrupt, including Ashton Kutcher’s firm, because he was one of the judges,” Render co-founder and CEO Anurag Goel explained. “In the end, we decided that we would just raise more money from our existing investors because we like them and it helped us get a better deal from our existing investors. And they were all super interested in continuing to invest.”
What makes Render stand out is that it fulfills many of the promises of Heroku and maybe Google Cloud’s App Engine. You simply tell it what kind of service you are going to deploy and it handles the deployment and manages the infrastructure for you.
“Our customers are all people who are writing code. And they just want to deploy this code really easily without having to worry about servers, or maintenance, or depending on DevOps teams — or, in many cases, hiring DevOps teams,” Goel said. “DevOps engineers are extremely expensive to hire and extremely hard to find, especially good ones. Our goal is to eliminate all of that work that DevOps people do at every company, because it’s very similar at every company.”
Image Credits: Render
One new feature the company is launching today is preview environments. You can think of them as disposable staging or development environments that developers can spin up to test their code — and Render promises that the testing environment will look the same as your production environment (or you can specify changes, too). Developers can then test their updates collaboratively with QA or their product and sales teams in this environment.
Development teams on Render specify their infrastructure environments in a YAML file and turning on these new preview environments is as easy as setting a flag in that file.
Image Credits: Render
“Once they do that, then for every pull request — because we’re integrated with GitHub and GitLab — we automatically spin up a copy of that environment. That can include anything you have in production, or things like a Redis instance, or managed Postgres database, or Elasticsearch instance, or obviously APIs and web services and static sites,” Goel said. Every time you push a change to that branch or pull request, the environment is automatically updated, too. Once the pull request is closed or merged, Render destroys the environment automatically.
The company will use the new funding to grow its team and build out its service. The plan, Goel tells me, is to raise a larger Series A round next year.
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Logz.io lands $52M to keep growing open source-based logging tools
Logz.io announced a $52 million Series D investment today. The round was led by General Catalyst.
Other investors participating in the round included OpenView Ventures, 83North, Giza Venture Capital, Vintage Investment Partners, Greenspring Associates and Next47. Today’s investment brings the total raised to nearly $100 million, according to Crunchbase data.
Logz.io is a company built on top of the open-source tools Elasticsearch, Logstash and Kibana (collectively known by the acronym ELK) and Grafana. It’s taking those tools in a typical open-source business approach, packaging them up and offering them as a service. This approach enables large organizations to take advantage of these tools without having to deal with the raw open-source projects.
The company’s solutions intelligently scan logs looking for anomalies. When it finds them, it surfaces the problem and informs IT or security, depending on the scenario, using a tool like PagerDuty. This area of the market has been dominated in recent years by vendors like Splunk and Sumo Logic, but company founder and CEO Tomer Levy saw a chance to disrupt that space by packaging a set of open-source logging tools that were rapidly increasing in popularity. They believed they could build on that growing popularity, while solving a pain point the founders had actually experienced in previous positions, which is always a good starting point for a startup idea.
Screenshot: Logz.io
“We saw that the majority of the market is actually using open source. So we said, we want to solve this problem, a problem we have faced in the past and didn’t have a solution. What we’re going to do is we’re going to provide you with an easy-to-use cloud service that is offering an open-source compatible solution,” Levy explained. In other words, they wanted to build on that open-source idea, but offer it in a form that was easier to consume.
Larry Bohn, who is leading the investment for General Catalyst, says that his firm liked the idea of a company building on top of open source because it provides a built-in community of developers to drive the startup’s growth — and it appears to be working. “The numbers here were staggering in terms of how quickly people were adopting this and how quickly it was growing. It was very clear to us that the company was enjoying great success without much of a commercial orientation,” Bohn explained.
In fact, Logz.io already has 700 customers, including large names like Schneider Electric, The Economist and British Airways. The company has 175 employees today, but Levy says they expect to grow that by 250 by the end of this year, as they use this money to accelerate their overall growth.
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Vizion.ai launches its managed Elasticsearch service
Setting up Elasticsearch, the open-source system that many companies large and small use to power their distributed search and analytics engines, isn’t the hardest thing. What is very hard, though, is to provision the right amount of resources to run the service, especially when your users’ demand comes in spikes, without overpaying for unused capacity. Vizion.ai’s new Elasticsearch Service does away with all of this by essentially offering Elasticsearch as a service and only charging its customers for the infrastructure they use.
Vizion.ai’s service automatically scales up and down as needed. It’s a managed service and delivered as a SaaS platform that can support deployments on both private and public clouds, with full API compatibility with the standard Elastic stack that typically includes tools like Kibana for visualizing data, Beats for sending data to the service and Logstash for transforming the incoming data and setting up data pipelines. Users can easily create several stacks for testing and development, too, for example.
Vizion.ai GM and VP Geoff Tudor
“When you go into the AWS Elasticsearch service, you’re going to be looking at dozens or hundreds of permutations for trying to build your own cluster,” Vision.ai’s VP and GM Geoff Tudor told me. “Which instance size? How many instances? Do I want geographical redundancy? What’s my networking? What’s my security? And if you choose wrong, then that’s going to impact the overall performance. […] We do balancing dynamically behind that infrastructure layer.” To do this, the service looks at the utilization patterns of a given user and then allocates resources to optimize for the specific use case.
What VVizion.ai hasdone here is take some of the work from its parent company Panzura, a multi-cloud storage service for enterprises that has plenty of patents around data caching, and applied it to this new Elasticsearch service.
There are obviously other companies that offer commercial Elasticsearch platforms already. Tudor acknowledges this, but argues that his company’s platform is different. With other products, he argues, you have to decide on the size of your block storage for your metadata upfront, for example, and you typically want SSDs for better performance, which can quickly get expensive. Thanks to Panzura’s IP, Vizion.ai is able to bring down the cost by caching recent data on SSDs and keeping the rest in cheaper object storage pools.
He also noted that the company is positioning the overall Vizion.ai service, with the Elasticsearch service as one of the earliest components, as a platform for running AI and ML workloads. Support for TensorFlow, PredictionIO (which plays nicely with Elasticsearch) and other tools is also in the works. “We want to make this an easy serverless ML/AI consumption in a multi-cloud fashion, where not only can you leverage the compute, but you can also have your storage of record at a very cost-effective price point.”
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