Inside GitLab’s IPO filing

While the technology and business world worked towards the weekend, developer operations (DevOps) firm GitLab filed to go public. Before we get into our time off, we need to pause, digest the company’s S-1 filing, and come to some early conclusions.

GitLab competes with GitHub, which Microsoft purchased for $7.5 billion back in 2018.

The company is notable for its long-held, remote-first stance, and for being more public with its metrics than most unicorns — for some time, GitLab had a November 18, 2020 IPO target in its public plans, to pick an example. We also knew when it crossed the $100 million recurring revenue threshold.

Considering GitLab’s more recent results, a narrowing operating loss in the last two quarters is good news for the company.

The company’s IPO has therefore been long expected. In its last primary transaction, GitLab raised $286 million at a post-money valuation of $2.75 billion, per Pitchbook data. The same information source also notes that GitLab executed a secondary transaction earlier this year worth $195 million, which gave the company a $6 billion valuation.

Let’s parse GitLab’s growth rate, its final pre-IPO scale, its SaaS metrics, and then ask if we think it can surpass its most recent private-market price. Sound good? Let’s rock.

The GitLab S-1

GitLab intends to list on the Nasdaq under the symbol “GTLB.” Its IPO filing lists a placeholder $100 million raise estimate, though that figure will change when the company sets an initial price range for its shares. Its fiscal year ends January 31, meaning that its quarters are offset from traditional calendar periods by a single month.

Let’s start with the big numbers.

In its fiscal year ended January 2020, GitLab posted revenues of $81.2 million, gross profit of $71.9 million, an operating loss of $128.4 million, and a modestly greater net loss of $130.7 million.

And in the year ended January 31, 2021, GitLab’s revenue rose roughly 87% to $152.2 million from a year earlier. The company’s gross profit rose around 86% to $133.7 million, and operating loss widened nearly 67% to $213.9 million. Its net loss totaled $192.2 million.

This paints a picture of a SaaS company growing quickly at scale, with essentially flat gross margins (88%). Growth has not been inexpensive either — GitLab spent more on sales and marketing than it generated in gross profit in the past two fiscal years.

#computing, #crowdstrike, #datadog, #ec-news-analysis, #enterprise-software, #fundings-exits, #git, #github, #gitlab, #ipo, #microsoft, #saas, #software, #software-engineering, #startups, #tc, #twilio, #version-control

Cribl raises $200M to help enterprises do more with their data

At a time when remote work, cybersecurity attacks and increased privacy and compliance requirements threaten a company’s data, more companies are collecting and storing their observability data, but are being locked in with vendors or have difficulty accessing the data.

Enter Cribl. The San Francisco-based company is developing an “open ecosystem of data” for enterprises that utilizes unified data pipelines, called “observability pipelines,” to parse and route any type of data that flows through a corporate IT system. Users can then choose their own analytics tools and storage destinations like Splunk, Datadog and Exabeam, but without becoming dependent on a vendor.

The company announced Wednesday a $200 million round of Series C funding to value Cribl at $1.5 billion, according to a source close to the company. Greylock and Redpoint Ventures co-led the round and were joined by new investor IVP, existing investors Sequoia and CRV and strategic investment from Citi Ventures and CrowdStrike. The new capital infusion gives Cribl a total of $254 million in funding since the company was started in 2017, Cribl co-founder and CEO Clint Sharp told TechCrunch.

Sharp did not discuss the valuation; however, he believes that the round is “validation that the observability pipeline category is legit.” Data is growing at a compound annual growth rate of 25%, and organizations are collecting five times more data today than they did 10 years ago, he explained.

“Ultimately, they want to ask and answer questions, especially for IT and security people,” Sharp added. “When Zoom sends data on who started a phone call, that might be data I need to know so I know who is on the call from a security perspective and who they are communicating with. Also, who is sending files to whom and what machines are communicating together in case there is a malicious actor. We can also find out who is having a bad experience with the system and what resources they can access to try and troubleshoot the problem.”

Cribl also enables users to choose how they want to store their data, which is different from competitors that often lock companies into using only their products. Instead, customers can buy the best products from different categories and they will all talk to each other through Cribl, Sharp said.

Though Cribl is developing a pipeline for data, Sharp sees it more as an “observability lake,” as more companies have differing data storage needs. He explains that the lake is where all of the data will go that doesn’t need to go into an existing storage solution. The pipelines will send the data to specific tools and then collect the data, and what doesn’t fit will go back into the lake so companies have it to go back to later. Companies can keep the data for longer and more cost effectively.

Cribl said it is seven times more efficient at processing event data and boasts a customer list that includes Whole Foods, Vodafone, FINRA, Fannie Mae and Cox Automotive.

Sharp went after additional funding after seeing huge traction in its existing customer base, saying that “when you see that kind of traction, you want to keep doubling down.” His aim is to have a presence in every North American city and in Europe, to continue launching new products and growing the engineering team.

Up next, the company is focusing on go-to-market and engineering growth. Its headcount is 150 currently, and Sharp expects to grow that to 250 by the end of the year.

Over the last fiscal year, Cribl grew its revenue 293%, and Sharp expects that same trajectory for this year. The company is now at a growth stage, and with the new investment, he believes Cribl is the “future leader in observability.”

“This is a great investment for us, and every dollar, we believe, is going to create an outsized return as we are the only commercial company in this space,” he added.

Scott Raney, managing director at Redpoint Ventures, said his firm is a big enterprise investor in software, particularly in companies that help organizations leverage data to protect themselves, a sweet spot that Cribl falls into.

He feels Sharp is leading a team, having come from Splunk, that has accomplished a lot, has a vision and a handle on the business and knows the market well. Where Splunk is capturing the machine data and using its systems to extract the data, Cribl is doing something similar in directing the data where it needs to go, while also enabling companies to utilize multiple vendors and build apps to sit on top of its infrastructure.

“Cribl is adding opportunity by enriching the data flowing through, and the benefits are going to be meaningful in cost reduction,” Raney said. “The attitude out there is to put data in cheaper places, and afford more flexibility to extract data. Step one is to make that transition, and step two is how to drive the data sitting there. Cribl is doing something that will go from being a big business to a legacy company 30 years from now.”

#citi-ventures, #clint-sharp, #cloud, #computing, #cribl, #crowdstrike, #crv, #data-security, #datadog, #developer, #enterprise, #exabeam, #funding, #greylock, #information-technology, #ivp, #recent-funding, #redpoint-ventures, #scott-raney, #sequoia, #splunk, #startups, #storage-solution, #tc

Edge Delta raises $15M Series A to take on Splunk

Seattle-based Edge Delta, a startup that is building a modern distributed monitoring stack that is competing directly with industry heavyweights like Splunk, New Relic and Datadog, today announced that it has raised a $15 million Series A funding round led by Menlo Ventures and Tim Tully, the former CTO of Splunk. Previous investors MaC Venture Capital and Amity Ventures also participated in this round, which brings the company’s total funding to date to $18 million.

“Our thesis is that there’s no way that enterprises today can continue to analyze all their data in real time,” said Edge Delta co-founder and CEO Ozan Unlu, who has worked in the observability space for about 15 years already (including at Microsoft and Sumo Logic). “The way that it was traditionally done with these primitive, centralized models — there’s just too much data. It worked 10 years ago, but gigabytes turned into terabytes and now terabytes are turning into petabytes. That whole model is breaking down.”

Image Credits: Edge Delta

He acknowledges that traditional big data warehousing works quite well for business intelligence and analytics use cases. But that’s not real-time and also involves moving a lot of data from where it’s generated to a centralized warehouse. The promise of Edge Delta is that it can offer all of the capabilities of this centralized model by allowing enterprises to start to analyze their logs, metrics, traces and other telemetry right at the source. This, in turn, also allows them to get visibility into all of the data that’s generated there, instead of many of today’s systems, which only provide insights into a small slice of this information.

While competing services tend to have agents that run on a customer’s machine, but typically only compress the data, encrypt it and then send it on to its final destination, Edge Delta’s agent starts analyzing the data right at the local level. With that, if you want to, for example, graph error rates from your Kubernetes cluster, you wouldn’t have to gather all of this data and send it off to your data warehouse where it has to be indexed before it can be analyzed and graphed.

With Edge Delta, you could instead have every single node draw its own graph, which Edge Delta can then combine later on. With this, Edge Delta argues, its agent is able to offer significant performance benefits, often by orders of magnitude. This also allows businesses to run their machine learning models at the edge, as well.

Image Credits: Edge Delta

“What I saw before I was leaving Splunk was that people were sort of being choosy about where they put workloads for a variety of reasons, including cost control,” said Menlo Ventures’ Tim Tully, who joined the firm only a couple of months ago. “So this idea that you can move some of the compute down to the edge and lower latency and do machine learning at the edge in a distributed way was incredibly fascinating to me.”

Edge Delta is able to offer a significantly cheaper service, in large part because it doesn’t have to run a lot of compute and manage huge storage pools itself since a lot of that is handled at the edge. And while the customers obviously still incur some overhead to provision this compute power, it’s still significantly less than what they would be paying for a comparable service. The company argues that it typically sees about a 90 percent improvement in total cost of ownership compared to traditional centralized services.

Image Credits: Edge Delta

Edge Delta charges based on volume and it is not shy to compare its prices with Splunk’s and does so right on its pricing calculator. Indeed, in talking to Tully and Unlu, Splunk was clearly on everybody’s mind.

“There’s kind of this concept of unbundling of Splunk,” Unlu said. “You have Snowflake and the data warehouse solutions coming in from one side, and they’re saying, ‘hey, if you don’t care about real time, go use us.’ And then we’re the other half of the equation, which is: actually there’s a lot of real-time operational use cases and this model is actually better for those massive stream processing datasets that you required to analyze in real time.”

But despite this competition, Edge Delta can still integrate with Splunk and similar services. Users can still take their data, ingest it through Edge Delta and then pass it on to the likes of Sumo Logic, Splunk, AWS’s S3 and other solutions.

Image Credits: Edge Delta

“If you follow the trajectory of Splunk, we had this whole idea of building this business around IoT and Splunk at the Edge — and we never really quite got there,” Tully said. “I think what we’re winding up seeing collectively is the edge actually means something a little bit different. […] The advances in distributed computing and sophistication of hardware at the edge allows these types of problems to be solved at a lower cost and lower latency.”

The Edge Delta team plans to use the new funding to expand its team and support all of the new customers that have shown interest in the product. For that, it is building out its go-to-market and marketing teams, as well as its customer success and support teams.

 

#aws, #big-data, #business-intelligence, #cloud, #computing, #cto, #data-security, #data-warehouse, #datadog, #enterprise, #information-technology, #mac-venture-capital, #machine-learning, #menlo-ventures, #microsoft, #new-relic, #real-time, #recent-funding, #seattle, #splunk, #startups, #sumo-logic, #system-administration, #tc, #technology

Vantage raises $4M to help businesses understand their AWS costs

Vantage, a service that helps businesses analyze and reduce their AWS costs, today announced that it has raised a $4 million seed round led by Andreessen Horowitz. A number of angel investors, including Brianne Kimmel, Julia Lipton, Stephanie Friedman, Calvin French Owen, Ben and Moisey Uretsky, Mitch Wainer and Justin Gage, also participated in this round

Vantage started out with a focus on making the AWS console a bit easier to use — and help businesses figure out what they are spending their cloud infrastructure budgets on in the process. But as Vantage co-founder and CEO Ben Schaechter told me, it was the cost transparency features that really caught on with users.

“We were advertising ourselves as being an alternative AWS console with a focus on developer experience and cost transparency,” he said.”What was interesting is — even in the early days of early access before the formal GA launch in January — I would say more than 95% of the feedback that we were getting from customers was entirely around the cost features that we had in Vantage.”

Image Credits: Vantage

Like any good startup, the Vantage team looked at this and decided to double down on these features and highlight them in its marketing, though it kept the existing AWS Console-related tools as well. The reason the other tools didn’t quite take off, Schaechter believes, is because more and more, AWS users have become accustomed to infrastructure-as-code to do their own automatic provisioning. And with that, they spend a lot less time in the AWS Console anyway.

“But one consistent thing — across the board — was that people were having a really, really hard time twelve times a year, where they would get a shock AWS bill and had to figure out what happened. What Vantage is doing today is providing a lot of value on the transparency front there,” he said.

Over the course of the last few months, the team added a number of new features to its cost transparency tools, including machine learning-driven predictions (both on the overall account level and service level) and the ability to share reports across teams.

Image Credits: Vantage

While Vantage expects to add support for other clouds in the future, likely starting with Azure and then GCP, that’s actually not what the team is focused on right now. Instead, Schaechter noted, the team plans to add support for bringing in data from third-party cloud services instead.

“The number one line item for companies tends to be AWS, GCP, Azure,” he said. “But then, after that, it’s Datadog Cloudflare Sumo Logic, things along those lines. Right now, there’s no way to see, P&L or an ROI from a cloud usage-based perspective. Vantage can be the tool where that’s showing you essentially, all of your cloud costs in one space.”

That is likely the vision the investors bought in as well and even though Vantage is now going up against enterprise tools like Apptio’s Cloudability and VMware’s CloudHealth, Schaechter doesn’t seem to be all that worried about the competition. He argues that these are tools that were born in a time when AWS had only a handful of services and only a few ways of interacting with those. He believes that Vantage, as a modern self-service platform, will have quite a few advantages over these older services.

“You can get up and running in a few clicks. You don’t have to talk to a sales team. We’re helping a large number of startups at this stage all the way up to the enterprise, whereas Cloudability and Cloud Health are, in my mind, kind of antiquated enterprise offerings. No startup is choosing to use those at this point, as far as I know,” he said.

The team, which until now mostly consisted of Schaechter and his co-founder and CTO Brooke McKim, bootstrapped to company up to this point. Now they plan to use the new capital to build out its team (and the company is actively hiring right now), both on the development and go-to-market side.

The company offers a free starter plan for businesses that track up to $2,500 in monthly AWS cost, with paid plans starting at $30 per month for those who need to track larger accounts.

#amazon-web-services, #andreessen-horowitz, #apptio, #aws, #brianne-kimmel, #cloud, #cloud-computing, #cloud-infrastructure, #cloud-services, #cloudability, #cloudflare, #computing, #datadog, #enterprise, #information-technology, #machine-learning, #recent-funding, #startups, #sumo-logic, #tc, #technology, #vmware

YL Ventures sells its stake in cybersecurity unicorn Axonius for $270M

YL Ventures, the Israel-focused cybersecurity seed fund, today announced that it has sold its stake cybersecurity asset management startup Axonius, which only a week ago announced a $100 million Series D funding round that now values it at around $1.2 billion.

ICONIQ Growth, Alkeon Capital Management, DTCP and Harmony Partners acquired YL Venture’s stake for $270 million. This marks YL’s first return from its third $75 million fund, which it raised in 2017, and the largest return in the firm’s history.

With this sale, the company’s third fund still has six portfolio companies remaining. It closed its fourth fund with $120 million in committed capital in the middle of 2019.

Unlike YL, which focuses on early-stage companies — though it also tends to participate in some later-stage rounds — the investors that are buying its stake specialize in later-stage companies that are often on an IPO path. ICONIQ Growth has invested in the likes of Adyen, CrowdStrike, Datadog and Zoom, for example, and has also regularly partnered with YL Ventures on its later-stage investments.

“The transition from early-stage to late-stage investors just makes sense as we drive toward IPO, and it allows each investor to focus on what they do best,” said Dean Sysman, co-founder and CEO of Axonius. “We appreciate the guidance and support the YL Ventures team has provided during the early stages of our company and we congratulate them on this successful journey.”

To put this sale into perspective for the Silicon Valley- and Tel Aviv-based YL Ventures, it’s worth noting that it currently manages about $300 million. Its current portfolio includes the likes of Orca Security, Hunters and Cycode. This sale is a huge win for the firm.

Its most headline-grabbing exit so far was Twistlock, which was acquired by Palo Alto Networks for $410 million in 2019, but it has also seen exits of its portfolio companies to Microsoft, Proofpoint, CA Technologies and Walmart, among others. The fund participated in Axonius’ $4 million seed round in 2017 up to its $58 Million Series C round a year ago.

It seems like YL Ventures is taking a very pragmatic approach here. It doesn’t specialize in late-stage firms — and until recently, Israeli startups always tended to sell long before they got to a late-stage round anyway. And it can generate a nice — and guaranteed — return for its own investors, too.

“This exit netted $270 million in cash directly to our third fund, which had $75 million total in capital commitments, and this fund still has 6 outstanding portfolio companies remaining,” Yoav Leitersdorf, YL Ventures’ founder and managing partner, told me. “Returning multiple times that fund now with a single exit, with the rest of the portfolio companies still there for the upside is the most responsible — yet highly profitable path — we could have taken for our fund at this time. And all this while diverting our energies and means more towards our seed-stage companies (where our help is more impactful), and at the same time supporting Axonius by enabling it to bring aboard such excellent late-stage investors as ICONIQ and Alkeon – a true win-win-win situation for everyone involved!”

He also noted that this sale achieved a top-decile return for the firm’s limited partners and allows it to focus its resources and attention toward the younger companies in its portfolio.

#adyen, #axonius, #ca-technologies, #companies, #crowdstrike, #datadog, #enterprise, #iconiq, #iconiq-growth, #information-technology, #leader, #management, #managing-partner, #microsoft, #palo-alto-networks, #proofpoint, #tel-aviv, #twistlock, #venture-capital, #walmart, #yl-ventures, #yoav-leitersdorf

How to overcome the challenges of switching to usage-based pricing

The usage-based pricing model almost feels like a cheat code — it enables SaaS companies to more efficiently acquire new customers, grow with those customers as they’re successful and keep those customers on the platform.

Compared to their peers, companies with usage-based pricing trade at a 50% revenue multiple premium and see 10pp better net dollar retention rates.

But the shift from pure subscription to usage-based pricing is nearly as complex as going from on-premise to SaaS. It opens up the addressable market by lowering the purchase barrier, which then necessitates finding new ways to scalably acquire users. It more closely aligns payment with a customer’s consumption, thereby impacting cash flow and revenue recognition. And it creates less revenue predictability, which can generate pushback from procurement and legal.

SaaS companies exploring a usage-based model need to plan for both go-to-market and operational challenges spanning from pricing to sales compensation to billing.

Selecting the right usage metric

There are numerous potential usage metrics that SaaS companies could use in their pricing. Datadog charges based on hosts, HubSpot uses marketing contacts, Zapier prices by tasks and Snowflake has compute resources. Picking the wrong usage metric could have disastrous consequences for long-term growth.

The best usage metric meets five key criteria: value-based, flexible, scalable, predictable and feasible.

  • Value-based: It should align with how customers derive value from the product and how they see success. For example, Stripe charges a 2.9% transaction fee and so directly grows as customers grow their business.
  • Flexible: Customers should be able to choose and pay for their exact scope of usage, starting small and scaling as they mature.
  • Scalable: It should grow steadily over time for the average customer once they’ve adopted the product. There’s a reason why cell phone providers now charge based on GB of data rather than talk minutes — data volumes keep going up.
  • Predictable: Customers should be able to reasonably predict their usage so they have budget predictability. (Some assistance may be required during the sales process.)
  • Feasible: It should be possible to monitor, administer and police usage. The metric needs to track with the cost of delivering the service so that customers don’t become unprofitable.

Navigating enterprise legal and procurement teams

Enterprise customers often crave price predictability for annual budgetary purposes. It can be tough for traditional legal and procurement teams to wrap their heads around a purchase with an unspecified cost. SaaS vendors must get creative with different usage-based pricing structures to give enterprise customers greater peace of mind.

tips for navigating legal and procurement teams

Image Credits: Kyle Poyar

Customer engagement software Twilio offers deeper discounts when a customer commits to usage for an extended period. AWS takes this a step further by allowing a customer to commit in advance, but still pay for their usage as it happens. Data analytics company Snowflake lets customers roll over their unused usage credits as long as their next year’s commitment is at least as large as the prior one.

Handling overages

Nobody wants to see a shock expense when they’ve unknowingly exceeded their usage limit. It’s important to design thoughtful overage policies that give customers the feeling of control over how much they’re spending.

#cloud, #column, #datadog, #ec-cloud-and-enterprise-infrastructure, #ec-column, #ec-how-to, #hubspot, #saas, #software-as-a-service, #stripe, #twilio

Datadog to acquire application security management platform Sqreen

Cloud monitoring platform Datadog has announced that it plans to acquire Sqreen, a software-as-a-service security platform. Originally founded in France, Sqreen participated in TechCrunch’s Startup Battlefield in 2016.

Sqreen is a cloud-based security product to protect your application directly. Once you install the sandboxed Sqreen agent, it analyzes your application in real time to find vulnerabilities in your code or your configuration. There’s a small CPU overhead with Sqreen enabled, but there are some upsides.

It can surface threats and you can set up your own threat detection rules. You can see the status of your application from the Sqreen dashboard, receive notifications when there’s an incident and get information about incidents.

For instance, you can see blocked SQL injections, see where the injection attempts came from and act to prevent further attempts. Sqreen also detects common attacks, such as credential stuffing attacks, cross-site scripting, etc. As your product evolves, you can enable different modules from the plugin marketplace.

Combining Datadog and Sqreen makes a lot of sense as many companies already rely on Datadog to monitor their apps. Sqreen has a good product, Datadog has a good customer base. So you can expect some improvements on the security front for Datadog.

The company raised a $2.3 million round from Alven Capital, Point Nine Capital, Kima Ventures, 50 Partners and business angels. It then participated in TechCrunch’s Startup Battlefield — it made it to the finals but didn’t win the competition. The startup attended Y Combinator a bit later.

In 2019, Sqreen raised a $14 million Series A round led by Greylock Partners with existing investors Y Combinator, Alven and Point Nine participating once again.

Datadog and Sqreen have signed a definitive acquisition agreement. Terms of the deal remain undisclosed and the acquisition should close in Q2 2021.

#cloud, #datadog, #developer, #sqreen, #startups

Fylamynt raises $6.5M for its cloud workflow automation platform

Fylamynt, a new service that helps businesses automate their cloud workflows, today announced both the official launch of its platform as well as a $6.5 million seed round. The funding round was led by Google’s AI-focused Gradient Ventures fund. Mango Capital and Point72 Ventures also participated.

At first glance, the idea behind Fylamynt may sound familiar. Workflow automation has become a pretty competitive space, after all, and the service helps developers connect their various cloud tools to create repeatable workflows. We’re not talking about your standard IFTTT- or Zapier -like integrations between SaaS products, though. The focus of Fylamynt is squarely on building infrastructure workflows. And while that may sound familiar, too, with tools like Ansible and Terraform automating a lot of that already, Fylamynt sits on top of those and integrates with them.

Image Credits: Fylamynt

“Some time ago, we used to do Bash and scripting — and then […] came Chef and Puppet in 2006, 2007. SaltStack, as well. Then Terraform and Ansible,” Fylamynt co-founder and CEO Pradeep Padala told me. “They have all done an extremely good job of making it easier to simplify infrastructure operations so you don’t have to write low-level code. You can write a slightly higher-level language. We are not replacing that. What we are doing is connecting that code.”

So if you have a Terraform template, an Ansible playbook and maybe a Python script, you can now use Fylamynt to connect those. In the end, Fylamynt becomes the orchestration engine to run all of your infrastructure code — and then allows you to connect all of that to the likes of DataDog, Splunk, PagerDuty Slack and ServiceNow.

Image Credits: Fylamynt

The service currently connects to Terraform, Ansible, Datadog, Jira, Slack, Instance, CloudWatch, CloudFormation and your Kubernetes clusters. The company notes that some of the standard use cases for its service are automated remediation, governance and compliance, as well as cost and performance management.

The company is already working with a number of design partners, including Snowflake

Fylamynt CEO Padala has quite a bit of experience in the infrastructure space. He co-founded ContainerX, an early container-management platform, which later sold to Cisco. Before starting ContainerX, he was at VMWare and DOCOMO Labs. His co-founders, VP of Engineering Xiaoyun Zhu and CTO David Lee, also have deep expertise in building out cloud infrastructure and operating it.

“If you look at any company — any company building a product — let’s say a SaaS product, and they want to run their operations, infrastructure operations very efficiently,” Padala said. “But there are always challenges. You need a lot of people, it takes time. So what is the bottleneck? If you ask that question and dig deeper, you’ll find that there is one bottleneck for automation: that’s code. Someone has to write code to automate. Everything revolves around that.”

Fylamynt aims to take the effort out of that by allowing developers to either write Python and JSON to automate their workflows (think ‘infrastructure as code’ but for workflows) or to use Fylamynt’s visual no-code drag-and-drop tool. As Padala noted, this gives developers a lot of flexibility in how they want to use the service. If you never want to see the Fylamynt UI, you can go about your merry coding ways, but chances are the UI will allow you to get everything done as well.

One area the team is currently focusing on — and will use the new funding for — is building out its analytics capabilities that can help developers debug their workflows. The service already provides log and audit trails, but the plan is to expand its AI capabilities to also recommend the right workflows based on the alerts you are getting.

“The eventual goal is to help people automate any service and connect any code. That’s the holy grail. And AI is an enabler in that,” Padala said.

Gradient Ventures partner Muzzammil “MZ” Zaveri echoed this. “Fylamynt is at the intersection of applied AI and workflow automation,” he said. “We’re excited to support the Fylamynt team in this uniquely positioned product with a deep bench of integrations and a non-prescriptive builder approach. The vision of automating every part of a cloud workflow is just the beginning.”

The team, which now includes about 20 employees, plans to use the new round of funding, which closed in September, to focus on its R&D, build out its product and expand its go-to-market team. On the product side, that specifically means building more connectors.

The company offers both a free plan as well as enterprise pricing and its platform is now generally available.

#ansible, #articles, #artificial-intelligence, #business, #ceo, #chef, #cisco, #cloud, #cloud-applications, #datadog, #developer, #enterprise, #gradient-ventures, #json, #pagerduty, #partner, #point72-ventures, #python, #servicenow, #splunk, #vmware, #zapier

With $29M in funding, Isovalent launches its cloud-native networking and security platform

Isovalent, a startup that aims to bring networking into the cloud-native era, today announced that it has raised a $29 million Series A round led by Andreesen Horowitz and Google. In addition, the company today officially launched its Cilium platform (which was in stealth until now) to help enterprises connect, observe and secure their applications.

The open-source Cilium project is already seeing growing adoption, with Google choosing it for its new GKE dataplane, for example. Other users include Adobe, Capital One, Datadog and GitLab. Isovalent is following what is now the standard model for commercializing open-source projects by launching an enterprise version.

Image Credits: Cilium

The founding team of CEO Dan Wendlandt and CTO Thomas Graf has deep experience in working on the Linux kernel and building networking products. Graf spent 15 years working on the Linux kernel and created the Cilium open-source project, while Wendlandt worked on Open vSwitch at Nicira (and then VMware).

Image Credits: Isovalent

“We saw that first wave of network intelligence be moved into software, but I think we both shared the view that the first wave was about replicating the traditional network devices in software,” Wendlandt told me. “You had IPs, you still had ports, you created virtual routers, and this and that. We both had that shared vision that the next step was to go beyond what the hardware did in software — and now, in software, you can do so much more. Thomas, with his deep insight in the Linux kernel, really saw this eBPF technology as something that was just obviously going to be groundbreaking technology, in terms of where we could take Linux networking and security.”

As Graf told me, when Docker, Kubernetes and containers, in general, become popular, what he saw was that networking companies at first were simply trying to reapply what they had already done for virtualization. “Let’s just treat containers as many as miniature VMs. That was incredibly wrong,” he said. “So we looked around, and we saw eBPF and said: this is just out there and it is perfect, how can we shape it forward?”

And while Isovalent’s focus is on cloud-native networking, the added benefit of how it uses the eBPF Linux kernel technology is that it also gains deep insights into how data flows between services and hence allows it to add advanced security features as well.

As the team noted, though, users definitely don’t need to understand or program eBPF, which is essentially the next generation of Linux kernel modules, themselves.

Image Credits: Isovalent

“I have spent my entire career in this space, and the North Star has always been to go beyond IPs + ports and build networking visibility and security at a layer that is aligned with how developers, operations and security think about their applications and data,” said Martin Casado, partner at Andreesen Horowitz (and the founder of Nicira). “Until just recently, the technology did not exist. All of that changed with Kubernetes and eBPF.  Dan and Thomas have put together the best team in the industry and given the traction around Cilium, they are well on their way to upending the world of networking yet again.”

As more companies adopt Kubernetes, they are now reaching a stage where they have the basics down but are now facing the next set of problems that come with this transition. Those, almost by default, include figuring out how to isolate workloads and get visibility into their networks — all areas where Isovalent/Cilium can help.

The team tells me its focus, now that the product is out of stealth, is about building out its go-to-market efforts and, of course, continue to build out its platform.

#andreesen-horowitz, #ceo, #cloud, #computer-science, #computing, #cto, #datadog, #enterprise, #google, #kernel, #kubernetes, #linus-torvalds, #linux, #martin-casado, #nicira, #operating-systems, #recent-funding, #security, #startups, #vms, #vmware

WhyLabs brings more transparancy to ML ops

WhyLabs, a new machine learning startup that was spun out of the Allen Institute, is coming out of stealth today. Founded by a group of former Amazon machine learning engineers, Alessya Visnjic, Sam Gracie and Andy Dang, together with Madrona Venture Group principal Maria Karaivanova, WhyLabs’ focus is on ML operations after models have been trained — not on building those models from the ground up.

The team also today announced that it has raised a $4 million seed funding round from Madrona Venture Group, Bezos Expeditions, Defy Partners and Ascend VC.

Visnjic, the company’s CEO, used to work on Amazon’s demand forecasting model.

“The team was all research scientists, and I was the only engineer who had kind of tier-one operating experience,” she told me. “So it was like, ”Okay, how bad could it be?’ I carried the pager for the retail website before it can be bad. But it was one of the first AI deployments that we’d done at Amazon at scale. The pager duty was extra fun because there were no real tools. So when things would go wrong — like we’d order way too many black socks out of the blue — it was a lot of manual effort to figure out why was this happening.”

Image Credits: WhyLabs

But while large companies like Amazon have built their own internal tools to help their data scientists and AI practitioners operate their AI systems, most enterprises continue to struggle with this — and a lot of AI projects simply fail and never make it into production. “We believe that one of the big reasons that happens is because of the operating process that remains super manual,” Visnjic said. “So at WhyLabs, we’re building the tools to address that — specifically to monitor and track data quality and alert — you can think of it as Datadog for AI applications.”

The team has brought ambitions, but to get started, it is focusing on observability. The team is building — and open-sourcing — a new tool for continuously logging what’s happening in the AI system, using a low-overhead agent. That platform-agnostic system, dubbed WhyLogs, is meant to help practitioners understand the data that moves through the AI/ML pipeline.

For a lot of businesses, Visnjic noted, the amount of data that flows through these systems is so large that it doesn’t make sense for them to keep “lots of big haystacks with possibly some needles in there for some investigation to come in the future.” So what they do instead is just discard all of this. With its data logging solution, WhyLabs aims to give these companies the tools to investigate their data and find issues right at the start of the pipeline.

Image Credits: WhyLabs

According to Karaivanova, the company doesn’t have paying customers yet, but it is working on a number of proofs of concepts. Among those users is Zulily, which is also a design partner for the company. The company is going after mid-size enterprises for the time being, but as Karaivanova noted, to hit the sweet spot for the company, a customer needs to have an established data science team with 10 to 15 ML practitioners. While the team is still figuring out its pricing model, it’ll likely be a volume-based approach, Karaivanova said.

“We love to invest in great founding teams who have built solutions at scale inside cutting-edge companies, who can then bring products to the broader market at the right time. The WhyLabs team are practitioners building for practitioners. They have intimate, first-hand knowledge of the challenges facing AI builders from their years at Amazon and are putting that experience and insight to work for their customers,” said Tim Porter, managing director at Madrona. “We couldn’t be more excited to invest in WhyLabs and partner with them to bring cross-platform model reliability and observability to this exploding category of MLOps.”

#amazon, #artificial-general-intelligence, #artificial-intelligence, #bezos-expeditions, #cybernetics, #datadog, #defy-partners, #engineer, #enterprise, #jeff-bezos, #machine-learning, #madrona-venture-group, #ml, #mlops, #performance-management, #recent-funding, #science-and-technology, #startups, #tc, #whylabs

As DevOps takes off, site reliability engineers are flying high

Each year, LinkedIn tracks the top emerging jobs and roles in the U.S.

The top four roles of 2020 — AI specialist, robotics engineer, data scientist and full-stack engineer — are all closely affiliated with driving forward technological innovation. Today, we’d like to recognize number five on the list, without which innovation in any domain would not be possible: the site reliability engineer (SRE).

We see the emergence of site reliability engineers not as a new trend, but one closely coupled with the theme of DevOps over the last decade. As coined, it was supposed to be something that you do and not something that you are. However, as time has passed, DevOps has found its way into roles and titles, often replacing “application production support” or “production engineering.”

What we are seeing now and predicting into the future is the rise of site reliability engineer as a title relating to the practice of DevOps and better describing the work to be done. At the time of our writing, there are more than 9,000 open roles for SREs on LinkedIn, a number that is only growing.

Software focused on helping engineers ensure reliability and uptime isn’t a new phenomenon, and the market has supported numerous billion-plus dollar exits, including companies like AppDynamics and Datadog . Nonetheless, we see an impending tipping point in tooling catering to the SRE persona across their entire workflow. We’ll discuss why the market is taking off and share our view of the landscape and the many inspired founders building technology to transform the practice of reliability — a foundational block for innovation across every industry.

Why now?

  • The service is the product: As more applications have moved to being delivered as a service, moving from the realm of IT to SaaS, the service itself has become the product. Anything delivered as a service must keep an eye toward the old, basic concept of customer service. This shift began at the application layer (e.g., Salesforce, Workday, ServiceNow) and over time has spread to infrastructure layer software (e.g., Datadog, HashiCorp) and has even impacted on-prem software. As Grant Miller, CEO at Replicated, put it further, “Traditional on-prem software vendors have transitioned away from delivering binary executables (.jar, .war, .exe, etc.) and expecting their customers to set up the necessary components manually. Now, vendors are leveraging Kubernetes as the substrate to deliver a much more automated and reliable experience to their customers, and redefining what ‘on-prem software’ traditionally meant.”

    #agile-software-development, #cloud, #column, #covid-19, #datadog, #developer, #devops, #digital-services, #e-commerce, #engineer, #firehydrant, #grafana, #pagerduty, #saas, #security, #servicenow

SaaS securitization will disrupt VC’s biggest returns this coming decade

SaaS investing has been on fire the past decade and the returns have been gushing in, with IPOs like Datadog, direct listings like Slack and acquisitions like Qualtrics (which is now being spun back out) creating billions of wealth and VC returns. Dozens more SaaS startups are on deck to head toward their exits in the same way, and many VC funds — particularly those with deep portfolios in the SaaS space — are going to perform well.

Yet, the gargantuan returns we are seeing today for SaaS portfolios are unlikely to repeat themselves.

The big threat in the short term is simply price: SaaS investing has gotten a lot more expensive. It may be hard to remember, but just a decade ago the business model of “Software as a Service” was revolutionary. Much in the way that it took years for cloud infrastructure to take hold in corporate IT departments, the idea that one didn’t license software but paid by user or by usage over time was almost heretical.

For VCs willing to make the leap into the space, prices were (relatively) cheap. Investor attention a decade ago was intensely centered on consumer web and mobile, driven by Facebook’s blockbuster IPO in May 2012 and Twitter’s IPO the following year. While every investor was chasing deals like Snap(chat), the smaller population of investors targeting enterprise SaaS (or even more exotic spaces like, gulp, fintech) got great deals on what would later become the decade’s biggest unicorns.

#alex-danco, #datadog, #extra-crunch, #fundraising, #market-analysis, #qualtrics, #saas, #shopify, #slack, #startups, #tc, #venture-capital

12 top cybersecurity VCs discuss investing, valuations and no-go zones

Cybersecurity is by far the most important area in any industry. Without it, we would be in hacker open season.

But cybersecurity is difficult to get right. One wrong move and you can leave the door open for data breaches, ransomware and nation state-backed espionage. That’s why there’s such an intense focus on cybersecurity from an investor’s point of view. How does an investor know what’s a worthwhile security solution and not snake oil? And in an already saturated security startup space, who can you trust to keep your company’s data safe?

These are just some of the questions we want answers to.

Every few months we check in with some of the leading investors in cybersecurity to gauge the heat (or chill) of the market, see what trends are making waves and understand some of the challenges in a busy startup world.

This time around, we spoke to a dozen cybersecurity VCs to hear their thoughts on what they’re most excited about, cybersecurity valuations (in the age of pandemic, no less), which companies are sparking investors’ interests and the kinds of startups that aren’t. (We also have a separate look at how cybersecurity VCs are investing during the COVID-19 pandemic, and how investors are weathering the global emergency. Be sure to check it out.)

For this survey, TechCrunch spoke to:

Here’s what they said. (Answers have been edited for clarity.)

#chief-information-security-officer, #computer-security, #cryptography, #cybercrime, #cyberwarfare, #datadog, #funding, #information-age, #internet-of-things, #iot, #machine-learning, #olivier-pomel, #scale-venture, #security, #venture-capital