Personal blog of Yzmir Ramirez

Cresicor, a consumer packaged goods trade management platform startup, raised $5.6 million in seed funding to further develop its tools for more accurate data and analytics.

The company, based remotely, focuses on small to midsize CPG companies, providing them with an automated way to manage their trade promotion, a process co-founder and CEO Alexander Whatley said is done primarily manually using spreadsheets.

Here’s what happens in a trade promotion: When a company wants to run a discount on one of their slower-selling items, the company has to spend money to do this — to have displays set up in a store or have that item on a certain shelf. If it works, more people will buy the item at the lower price point. Essentially, a trade promotion is the process of spending money to get more money in the future, Whatley told TechCrunch.

Figuring out all of the trade promotions is a complicated process, Whatley explained. Companies receive data feeds on the promotions from several different places, revenue data from retailers, accounting source data to show how many units were shipped and then maybe data directly from retailers. All of that has to be matched against the promotion.

“No API is bringing this data back to brands, so our software helps to automate and track these manual processes so companies can do analytics to see how the promotions are doing,” he added. “It also helps the finance team understand expenses, including which are valid and those that are not.”

What certain companies spend on trade promotions can represent their second-largest cost behind manufacturing, and companies often end up reinvesting between 20% and 30% of their revenue into trade promotions, Whatley said. This is a big market, representing untapped growth, especially with U.S. CPG sales topping $720 billion in 2020.

“You can see how messy the whole industry is, which is why we have a bright future and huge TAM,” he added. “With this new funding, we can target other parts of the P&L like supply chain and salaries. We also provide analytics for their strategy and where they should be spending it — which store, on which supply. By allocating resources the right way, companies typically see a 10% boost in sales as a result.”

Whatley started the company in 2017 with his brother, Daniel, Stuart Kennedy and Nikki McNeil while a Harvard undergrad. Since raising the funding back in February, the company has grown 2.5x in revenue, while employee headcount grew 4x over the past 12 months to 20.

Costanoa Ventures led the investment and was joined by Torch Capital and a group of angel investors including Fivestars CTO Matt Doka and Hu’s Kitchen CEO Mark Ramadan.

John Cowgill, partner at Costanoa, said though Cresicor raised a seed round, the company was already acquiring brands and capital before releasing a product and grew to almost a Series A company without any outside capital, saying it “blew me away.”

Cresicor is the “perfect example” of a company that Costanoa would get excited about — a vertical software company using data or machine learning to augment a pain point, Cowgill added.

“The CPG industry is in the middle of a rapid change where we see all of these emerging, digital native and mission-driven brands rapidly eating share from incumbents,” he added. “For the next generation of brands to compete, they have to win in trade promotion management. Cresicor’s opportunity to go beyond trade is significant. It is just a starting point to build a company that is the core enabler of great brands.”

The new funding will be used mainly to hire more talent in the areas of engineering and customer success so the company can hit its next benchmarks, Alexander Whatley said. He also intends to use the funding to acquire new brands and on software development. Cresicor boasts a list of customers including Perfect Snacks, Oatly and Hint Water.

The retail industry is valued at $5.5 trillion, and one-fifth of it is CPG, Whatley said. As a result, he has his eye on going after other verticals within CPG, like electronics and pet food, and then expanding into other areas.

“We are also going to work with enterprise companies — we see an opportunity to work with companies like P&G and General Mills, and we also want to build an ecosystem around trade promotion and launch into other profit and loss areas,” Whatley said.

Site reliability engineering platform Blameless announced Tuesday it raised $30 million in a Series B funding round, led by Third Point Ventures with participation from Accel, Decibel and Lightspeed Venture Partners, to bring total funding to over $50 million.

Site reliability engineering (SRE) is an extension of DevOps designed for more complex environments.

Blameless, based in San Mateo, California, emerged from stealth in 2019 after raising both a seed and Series A round, totaling $20 million. Since then, it has turned its business into a blossoming software platform.

Blameless’ platform provides the context, guardrails and automated workflows so engineering teams are unified in the way they communicate and interact, especially to resolve issues quicker as they build their software systems.

It originally worked with tech-forward teams at large companies, like Home Depot, that were “dipping [their toes] into the space and now [want] to double down,” co-founder and CEO Lyon Wong told TechCrunch.

The company still works with those tech-forward teams, but in the past two years, more companies sought out resident SRE architect Kurt Anderson to advise them, causing Blameless to change up its business approach, Wong said.

Other companies are also seeing a trend of customers asking for support — for example, in March, Google Cloud unveiled its Mission Critical Services support option for SRE to serve in a similar role as a consultant as companies move toward readiness with their systems. And in February, Nobl9 raised a $21 million Series B to provide enterprises with the tools they need to build service-level-objective-centric operations, which is part of a company’s SRE efforts.

Blameless now has interest from more mainstream companies in the areas of enterprise, logistics and healthcare. These companies aren’t necessarily focused on technology, but see a need for SRE.

“Companies recognize the shortfall in reliability, and then the question they come to us with is how do they get from where they are to where they want to be,” Anderson said. “Often companies that don’t have a process respond with ‘all hands on deck’ all the time, but instead need to shift to the right people responding.”

Lyon plans to use the new funding to fill key leadership roles, the company’s go-to-market strategy and product development to enable the company to go after larger enterprises.

Blameless doubled its revenue in the last year and will expand to service all customer segments, adding small and emerging businesses to its roster of midmarket and large companies. The company also expects to double headcount in the next three quarters.

As part of the funding announcement, Third Point Ventures partner Dan Moskowitz will join Blameless’ board of directors with Wong, Accel partner Vas Natarajan and Lightspeed partner Ravi Mhatre.

“Freeing up engineering to focus on shipping code is exactly what Blameless achieves,” said Moskowitz in a written statement. “The Blameless market opportunity is big as we see teams struggle and resort to creating homegrown playbooks and point solutions that are incomplete and costly.”

Reliance on a single technology as a lifeline is a futile battle now. When simple automation no longer does the trick, delivering end-to-end automation needs a combination of complementary technologies that can give a facelift to business processes: the digital operations toolbox.

According to a McKinsey survey, enterprises that have likely been successful with digital transformation efforts adopted sophisticated technologies such as artificial intelligence, Internet of Things or machine learning. Enterprises can achieve hyperautomation with the digital ops toolbox, the hub for your digital operations.

The toolbox is a synchronous medley of intelligent business process management (iBPM), robotic process automation (RPA), process mining, low code, artificial intelligence (AI), machine learning (ML) and a rules engine. The technologies can be optimally combined to achieve the organization’s key performance indicator (KPI) through hyperautomation.

The hyperautomation market is burgeoning: Analysts predict that by 2025, it will reach around $860 billion. Let’s see why.

The purpose of a digital ops toolbox

The toolbox, the treasure chest of technologies it is, helps with three crucial aspects: process automation, orchestration and intelligence.

Process automation: A hyperautomation mindset introduces the world of “automating anything that can be,” whether that’s a process or a task. If something can be handled by bots or other technologies, it should be.

Orchestration: Hyperautomation, per se, adds an orchestration layer to simple automation. Technologies like intelligent business process management orchestrate the entire process.

Intelligence: Machines can automate repetitive tasks, but they lack the decision-making capabilities of humans. And, to achieve a perfect harmony where machines are made to “think and act,” or attain cognitive skills, we need AI. Combining AI, ML and natural language processing algorithms with analytics propels simple automation to become more cognitive. Instead of just following if-then rules, the technologies help gather insights from the data. The decision-making capabilities enable bots to make decisions.

Simple automation versus hyperautomation

Here’s a story of evolving from simple automation to hyperautomation with an example: an order-to-cash process.

Israeli security startup Cycode, which specializes in helping enterprises secure their DevOps pipelines and prevent code tampering, today announced that it has raised a $20 million Series A funding round led by Insight Partners. Seed investor YL Ventures also participated in this round, which brings the total funding in the company to $24.6 million.

Cycode’s focus was squarely on securing source code in its early days, but thanks to the advent of infrastructure as code (IaC), policies as code and similar processes, it has expanded its scope. In this context, it’s worth noting that Cycode’s tools are language and use case agnostic. To its tools, code is code.

“This ‘everything as code’ notion creates an opportunity because the code repositories, they become a single source of truth of what the operation should look like and how everything should function, Cycode CTO and co-founder Ronen Slavin told me. “So if we look at that and we understand it — the next phase is to verify this is indeed what’s happening, and then whenever something deviates from it, it’s probably something that you should look at and investigate.”

Cycode Dashboard. Image Credits: Cycode

The company’s service already provides the tools for managing code governance, leak detection, secret detection and access management. Recently it added its features for securing code that defines a business’ infrastructure; looking ahead, the team plans to add features like drift detection, integrity monitoring and alert prioritization.

“Cycode is here to protect the entire CI/CD pipeline — the development infrastructure — from end to end, from code to cloud,” Cycode CEO and co-founder Lior Levy told me.

“If we look at the landscape today, we can say that existing solutions in the market are kind of siloed, just like the DevOps stages used to be,” Levy explained. “They don’t really see the bigger picture, they don’t look at the pipeline from a holistic perspective. Essentially, this is causing them to generate thousands of alerts, which amplifies the problem even further, because not only don’t you get a holistic view, but also the noise level that comes from those thousands of alerts causes a lot of valuable time to get wasted on chasing down some irrelevant issues.”

What Cycode wants to do then is to break down these silos and integrate the relevant data from across a company’s CI/CD infrastructure, starting with the source code itself, which ideally allows the company to anticipate issues early on in the software life cycle. To do so, Cycode can pull in data from services like GitHub, GitLab, Bitbucket and Jenkins (among others) and scan it for security issues. Later this year, the company plans to integrate data from third-party security tools like Snyk and Checkmarx as well.

“The problem of protecting CI/CD tools like GitHub, Jenkins and AWS is a gap for virtually every enterprise,” said Jon Rosenbaum, principal at Insight Partners, who will join Cycode’s board of directors. “Cycode secures CI/CD pipelines in an elegant, developer-centric manner. This positions the company to be a leader within the new breed of application security companies — those that are rapidly expanding the market with solutions which secure every release without sacrificing velocity.”

The company plans to use the new funding to accelerate its R&D efforts, and expand its sales and marketing teams. Levy and Slavin expect that the company will grow to about 65 employees this year, spread between the development team in Israel and its sales and marketing operations in the U.S.

Opsera, a startup that’s building an orchestration platform for DevOps teams, today announced that it has raised a $15 million Series A funding round led by Felicis Ventures. New investor HMG Ventures, as well as existing investors Clear Ventures, Trinity Partners and Firebolt Ventures also participated in this round, which brings the company’s total funding to $19.3 million.

Founded in January 2020, Opsera lets developers provision their CI/CD tools through a single framework. Using this framework, they can then build and manage their pipelines for a variety of use cases, including their software delivery lifecycle, infrastructure as code and their SaaS application releases. With this, Opsera essentially aims to help teams set up and operate their various DevOps tools.

The company’s two co-founders, Chandra Ranganathan and Kumar Chivukula, originally met while working at Symantec a few years ago. Ranganathan then spent the last three years at Uber, where he ran that company’s global infrastructure. Meanwhile, Chivukula ran Symantec’s hybrid cloud services.

Image Credits: Opsera

“As part of the transformation [at Symantec], we delivered over 50+ acquisitions over time. That had led to the use of many cloud platforms, many data centers,” Ranganathan explained. “Ultimately we had to consolidate them into a single enterprise cloud. That journey is what led us to the pain points of what led to Opsera. There were many engineering teams. They all had diverse tools and stacks that were all needed for their own use cases.”

The challenge then was to still give developers the flexibility to choose the right tools for their use cases, while also providing a mechanism for automation, visibility and governance — and that’s ultimately the problem Opsera now aims to solve.

Image Credits: Opsera

“In the DevOps landscape, […] there is a plethora of tools, and a lot of people are writing the glue code,” Opsera co-founder Chivukula noted. “But then they’re not they don’t have visibility. At Opsera, our mission and goal is to bring order to the chaos. And the way we want to do this is by giving choice and flexibility to the users and provide no-code automation using a unified framework.”

Wesley Chan, a managing director for Felicis Ventures who will join the Opsera board, also noted that he believes that one of the next big areas for growth in DevOps is how orchestration and release management is handled.

“We spoke to a lot of startups who are all using black-box tools because they’ve built their engineering organization and their DevOps from scratch,” Chan said. “That’s fine, if you’re starting from scratch and you just hired a bunch of people outside of Google and they’re all very sophisticated. But then when you talk to some of the larger companies. […] You just have all these different teams and tools — and it gets unwieldy and complex.”

Unlike some other tools, Chan argues, Opsera allows its users the flexibility to interface with this wide variety of existing internal systems and tools for managing the software lifecycle and releases.

“This is why we got so interested in investing, because we just heard from all the folks that this is the right tool. There’s no way we’re throwing out a bunch of our internal stuff. This would just wreak havoc on our engineering team,” Chan explained. He believes that building with this wide existing ecosystem in mind — and integrating with it without forcing users onto a completely new platform — and its ability to reduce friction for these teams, is what will ultimately make Opsera successful.

Opsera plans to use the new funding to grow its engineering team and accelerate its go-to-market efforts.

SLAs, SLOs, SLIs. If there’s one thing everybody in the business of managing software development loves, it’s acronyms. And while everyone probably knows what a Service Level Agreement (SLA) is, Service Level Objectives (SLOs) and Service Level Indicators (SLIs) may not be quite as well known. The idea, though, is straightforward, with SLOs being the overall goals a team must hit to meet the promises of its SLA agreements, and SLIs being the actual measurements that back up those other two numbers. With the advent of DevOps, these ideas, which are typically part of a company’s overall Site Reliability Engineering (SRE) efforts, are becoming more mainstream, but putting them into practice isn’t always straightforward.

Nobl9 aims to provide enterprises with the tools they need to build SLO-centric operations and the right feedback loops inside an organization to help it hit its SLOs without making too many trade-offs between the cost of engineering, feature development and reliability.

The company today announced that it has raised a $21 million Series B round led by its Series A investors Battery Ventures and CRV. In addition, Series A investors Bonfire Ventures and Resolute Ventures also participated, together with new investors Harmony Partners and Sorenson Ventures.

Before starting Nobl9, co-founders Marcin Kurc (CEO) and Brian Singer (CPO) spent time together at Orbitera, where Singer was the co-founder and COO and Kurc the CEO, and then at Google Cloud, after it acquired Orbitera in 2016. In the process, the team got to work with and appreciate Google’s site reliability engineering frameworks.

As they started looking into what to do next, that experience led them to look into productizing these ideas. “We came to this conclusion that if you’re going into Kubernetes, into service-based applications and modern architectures, there’s really no better way to run that than SRE,” Kurc told me. “And when we started looking at this, naturally SRE is a complete framework, there are processes. We started looking at elements of SRE and we agreed that SLO — service level objectives — is really the foundational part. You can’t do SRE without SLOs.”

As Singer noted, in order to adopt SLOs, businesses have to know how to turn the data they have about the reliability of their services, which could be measured in uptime or latency, for example, into the right objectives. That’s complicated by the fact that this data could live in a variety of databases and logs, but the real question is how to define the right SLOs for any given organization based on this data.

“When you go into the conversation with an organization about what their goals are with respect to reliability and how they start to think about understanding if there’s risks to that, they very quickly get bogged down in how are we going to get this data or that data and instrument this or instrument that,” Singer said. “What we’ve done is we’ve built a platform that essentially takes that as the problem that we’re solving. So no matter where the data lives and in what format it lives, we want to be able to reduce it to very simply an error budget and an objective that can be tracked and measured and reported on.”

The company’s platform launched into general availability last week, after a beta that started last year. Early customers include Brex and Adobe.

As Kurc told me, the team actually thinks of this new funding round as a Series A round, but because its $7.5 million Series A was pretty sizable, they decided to call it a Series A instead of a seed round. “It’s hard to define it. If you define it based on a revenue milestone, we’re pre-revenue, we just launched the GA product,” Singer told me. “But I think just in terms of the maturity of the product and the company, I would put us at the [Series] B.”

The team told me that it closed the round at the end of last November, and while it considered pitching new VCs, its existing investors were already interested in putting more money into the company and since its previous round had been oversubscribed, they decided to add to this new round some of the investors that didn’t make the cut for the Series A.

The company plans to use the new funding to advance its roadmap and expand its team, especially across sales, marketing and customer success.

As a product manager, I’m a true believer that you can solve any problem with the right product and process, even one as gnarly as the multiheaded hydra that is microservice overhead.

Working for Vertex Ventures US this summer was my chance to put this to the test. After interviewing 30+ industry experts from a diverse set of companies — Facebook, Fannie Mae, Confluent, Salesforce and more — and hosting a webinar with the co-founders of PagerDuty, LaunchDarkly and OpsLevel, we were able to answer three main questions:

1. How do teams adopt microservices?
2. What are the main challenges organizations face?
3. Which strategies, processes and tools do companies use to overcome these challenges?

How do teams adopt microservices?

Out of dozens of companies we spoke with, only two had not yet started their journey to microservices, but both were actively considering it. Industry trends mirror this as well. In an O’Reilly survey of 1500+ respondents, more than 75% had started to adopt microservices.

It’s rare for companies to start building with microservices from the ground up. Of the companies we spoke with, only one had done so. Some startups, such as LaunchDarkly, plan to build their infrastructure using microservices, but turned to a monolith once they realized the high cost of overhead.

“We were spending more time effectively building and operating a system for distributed systems versus actually building our own services so we pulled back hard,” said John Kodumal, CTO and co-founder of LaunchDarkly.

“As an example, the things we were trying to do in mesosphere, they were impossible,” he said. “We couldn’t do any logging. Zero downtime deploys were impossible. There were so many bugs in the infrastructure and we were spending so much time debugging the basic things that we weren’t building our own service.”

As a result, it’s more common for companies to start with a monolith and move to microservices to scale their infrastructure with their organization. Once a company reaches ~30 developers, most begin decentralizing control by moving to a microservice architecture.

Large companies with established monoliths are keen to move to microservices, but costs are high and the transition can take years. Atlassian’s platform infrastructure is in microservices, but legacy monoliths in Jira and Confluence persist despite ongoing decomposition efforts. Large companies often get stuck in this transition. However, a combination of strong, top-down strategy combined with bottoms-up dev team support can help companies, such as Freddie Mac, make substantial progress.

Some startups, like Instacart, first shifted to a modular monolith that allows the code to reside in a single repository while beginning the process of distributing ownership of discrete code functions to relevant teams. This enables them to mitigate the overhead associated with a microservice architecture by balancing the visibility of having a centralized repository and release pipeline with the flexibility of discrete ownership over portions of the codebase.

What challenges do teams face?

Teams may take different routes to arrive at a microservice architecture, but they tend to face a common set of challenges once they get there. John Laban, CEO and co-founder of OpsLevel, which helps teams build and manage microservices told us that “with a distributed or microservices based architecture your teams benefit from being able to move independently from each other, but there are some gotchas to look out for.”

Indeed, the linked O’Reilly chart shows how the top 10 challenges organizations face when adopting microservices are shared by 25%+ of respondents. While we discussed some of the adoption blockers above, feedback from our interviews highlighted issues around managing complexity.

The lack of a coherent definition for a service can cause teams to generate unnecessary overhead by creating too many similar services or spreading related services across different groups. One company we spoke with went down the path of decomposing their monolith and took it too far. Their service definitions were too narrow, and by the time decomposition was complete, they were left with 4,000+ microservices to manage. They then had to backtrack and consolidate down to a more manageable number.

Defining too many services creates unnecessary organizational and technical silos while increasing complexity and overhead. Logging and monitoring must be present on each service, but with ownership spread across different teams, a lack of standardized tooling can create observability headaches. It’s challenging for teams to get a single-pane-of-glass view with too many different interacting systems and services that span the entire architecture.

Atlassian today launched a slew of DevOps-centric updates to a variety of its services, ranging from Bitbucket Cloud and Pipelines to Jira and others. While it’s quite a grab-bag of announcements, the overall idea behind them is to make it easier for teams to collaborate across functions as companies adopt DevOps as their development practice of choice.

“I’ve seen a lot of these tech companies go through their agile and DevOps transformations over the years,” Tiffany To, the head of agile and DevOps solutions at Atlassian told me. “Everyone wants the benefits of DevOps, but — we know it — it gets complicated when we mix these teams together, we add all these tools. As we’ve talked with a lot of our users, for them to succeed in DevOps, they actually need a lot more than just the toolset. They have to enable the teams. And so that’s what a lot of these features are focused on.”

As To stressed, the company also worked with several ecosystem partners, for example, to extend the automation features in Jira Software Cloud, which can now also be triggered by commits and pull requests in GitHub, GitLab and other code repositories that are integrated into Jira Software Cloud. “Now you get these really nice integrations for DevOps where we are enabling these developers to not spend time updating the issues,” To noted.

Indeed, a lot of the announcements focus on integrations with third-party tools. This, To said, is meant to allow Atlassian to meet developers where they are. If your code editor of choice is VS Code, for example, you can now try Atlassian’s now VS Code extension, which brings your task like from Jira Software Cloud to the editor, as well as a code review experience and CI/CD tracking from Bitbucket Pipelines.

Also new is the “Your Work” dashboard in Bitbucket Cloud, which can now show you all of your assigned Jira issues, as well as Code Insights in Bitbucket Cloud. Code Insights features integrations with Mabl for test automation, Sentry for monitoring and Snyk for finding security vulnerabilities. These integrations were built on top of an open API, so teams can build their own integrations, too.

“There’s a really important trend to shift left. How do we remove the bugs and the security issues earlier in that dev cycle, because it costs more to fix it later,” said To. “You need to move that whole detection process much earlier in the software lifecycle.”

Jira Service Desk Cloud is getting a new Risk Management Engine that can score the risk of changes and auto-approve low-risk ones, as well as a new change management view to streamline the approval process.

Finally, there is new Opsgenie and Bitbucket Cloud integration that centralizes alerts and promises to filter out the noise, as well as a nice incident investigation dashboard to help teams take a look at the last deployment that happened before the incident occurred.

“The reason why you need all these little features is that as you stitch together a very large number of tools […], there is just lots of these friction points,” said To. “And so there is this balance of, if you bought a single toolchain, all from one vendor, you would have fewer of these friction points, but then you don’t get to choose best of breed. Our mission is to enable you to pick the best tools because it’s not one-size-fits-all.”

Enterprise startups UIPath and Scale have drawn huge attention in recent years from companies looking to automate workflows, from RPA (robotic process automation) to data labeling.

What’s been overlooked in the wake of such workflow-specific tools has been the base class of products that enterprises are using to build the core of their machine learning (ML) workflows, and the shift in focus toward automating the deployment and governance aspects of the ML workflow.

That’s where MLOps comes in, and its popularity has been fueled by the rise of core ML workflow platforms such as Boston-based DataRobot. The company has raised more than $430 million and reached a $1 billion valuation this past fall serving this very need for enterprise customers. DataRobot’s vision has been simple: enabling a range of users within enterprises, from business and IT users to data scientists, to gather data and build, test and deploy ML models quickly.

Founded in 2012, the company has quietly amassed a customer base that boasts more than a third of the Fortune 50, with triple-digit yearly growth since 2015. DataRobot’s top four industries include finance, retail, healthcare and insurance; its customers have deployed over 1.7 billion models through DataRobot’s platform. The company is not alone, with competitors like H20.ai, which raised a $72.5 million Series D led by Goldman Sachs last August, offering a similar platform.

Why the excitement? As artificial intelligence pushed into the enterprise, the first step was to go from data to a working ML model, which started with data scientists doing this manually, but today is increasingly automated and has become known as “auto ML.” An auto-ML platform like DataRobot’s can let an enterprise user quickly auto-select features based on their data and auto-generate a number of models to see which ones work best.

As auto ML became more popular, improving the deployment phase of the ML workflow has become critical for reliability and performance — and so enters MLOps. It’s quite similar to the way that DevOps has improved the deployment of source code for applications. Companies such as DataRobot and H20.ai, along with other startups and the major cloud providers, are intensifying their efforts on providing MLOps solutions for customers.

We sat down with DataRobot’s team to understand how their platform has been helping enterprises build auto-ML workflows, what MLOps is all about and what’s been driving customers to adopt MLOps practices now.

The rise of MLOps

Checkly, a Berlin-based startup that is developing a monitoring and testing platform for DevOps teams, today announced that it has raised a $2.25 million seed round led by Accel. A number of angel investors, including Instana CEO Mirko Novakovic, Zeit CEO Guillermo Rauch and former Twilio CTO Ott Kaukver, also participated in this round.

The company’s SaaS platform allows developers to monitor their API endpoints and web apps — and it obviously alerts you when something goes awry. The transaction monitoring tool makes it easy to regularly test interactions with front-end websites without having to actually write any code. The test software is based on Google’s open-source Puppeteer framework and to build its commercial platform, Checkly also developed Puppeteer Recorder for creating these end-to-end testing scripts in a low-code tool that developers access through a Chrome extension.

The team believes that it’s the combination of end-to-end testing and active monitoring, as well as its focus on modern DevOps teams, that makes Checkly stand out in what is already a pretty crowded market for monitoring tools.

“As a customer in the monitoring market, I thought it had long been stuck in the 90s and I needed a tool that could support teams in JavaScript and work for all the different roles within a DevOps team. I set out to build it, quickly realizing that testing was equally important to address,” said Tim Nolet, who founded the company in 2018. “At Checkly, we’ve created a market-defining tool that our customers have been demanding, and we’ve already seen strong traction through word of mouth. We’re delighted to partner with Accel on building out our vision to become the active reliability platform for DevOps teams.”

Nolet’s co-founders are Hannes Lenke, who founded TestObject (which was later acquired by Sauce Labs), and Timo Euteneuer, who was previously Director Sales EMEA at Sauce Labs.

Tthe company says that it currently has about 125 paying customers who run about 1 million checks per day on its platform. Pricing for its services starts at $7 per month for individual developers, with plans for small teams starting at $29 per month.