Data platform Splunk today announced that it has acquired two startups, Plumbr and Rigor, to build out its new Observability Suite, which is also launching today. Plumbr is an application performance monitoring service, while Rigor focuses on digital experience monitoring, using synthetic monitoring and optimization tools to help businesses optimize their end-user experiences. Both of these acquisitions complement the technology and expertise Splunk acquired when it bought SignalFx for over $1 billion last year.

Splunk did not disclose the price of these acquisitions, but Estonia-based Plumbr had raised about $1.8 million, while Atlanta-based Rigor raised a debt round earlier this year.

When Splunk acquired SignalFx, it said it did so in order to become a leader in observability and APM. As Splunk CTO Tim Tully told me, the idea here now is to accelerate this process.

Image Credits: Splunk

“Because a lot of our users and our customers are moving to the cloud really, really quickly, the way that they monitor [their] applications changed because they’ve gone to serverless and microservices a ton,” he said. “So we entered that space with those acquisitions, we quickly folded them together with these next two acquisitions. What Plumbr and Rigor do is really fill out more of the portfolio.”

He noted that Splunk was especially interested in Plumbr’s bytecode implementation and its real-user monitoring capabilities, and Rigor’s synthetics capabilities around digital experience monitoring (DEM). “By filling in those two pieces of the portfolio, it gives us a really amazing set of solutions because DEM was the missing piece for our APM strategy,” Tully explained.

Image Credits: Splunk

With the launch of its Observability Suite, Splunk is now pulling together a lot of these capabilities into a single product — which also features a new design that makes it stand apart from the rest of Splunk’s tools. It combines logs, metrics, traces, digital experience, user monitoring, synthetics and more.

“At Yelp, our engineers are responsible for hundreds of different microservices, all aimed at helping people find and connect with great local businesses,” said Chris Gordon, Technical Lead at Yelp, where his team has been testing the new suite. “Our Production Observability team collaborates with Engineering to improve visibility into the performance of key services and infrastructure. Splunk gives us the tools to empower engineers to monitor their own services as they rapidly ship code, while also providing the observability team centralized control and visibility over usage to ensure we’re using our monitoring resources as efficiently as possible.”

Four years ago, Ping Identity was at a crossroads. A venerable player in the single sign-on market, its product was not a market leader, and after 14 years and $128 million in venture capital, it needed to find a new path.

While the company had once discussed an IPO, by 2016 it began putting out feelers for buyers. Vista Equity Partners made a $600 million offer and promised to keep building the company, something that corporate buyers wouldn’t guarantee. Ping CEO and co-founder Andre Durand accepted Vista’s offer, seeing it as a way to pay off his investors and employees and exit the right way. Even better, his company wasn’t subsumed into a large entity as likely would have happened with a typical M&A transaction.

As it turned out, the IPO-or-acquisition question wasn’t an either/or proposition. Vista continued to invest in the company, using small acquisitions like UnboundID and Elastic Beam to fill in its roadmap, and Ping went public last year. The company’s experience shows that private equity offers a reasonable way for mature enterprise startups with decent but not exceptional growth — like the 100% or more venture firms tend to favor — to exit, pay off investors, reward employees and still keep building the company.

But not everyone that goes this route has a tidy outcome like Ping’s. Some companies get brought into the P/E universe where they replace the executive team, endure big layoffs or sell off profitable pieces and stop investing in the product. But the three private equity firms we spoke to — Vista Equity, Thoma Bravo and Scaleworks — all wanted to see their acquisitions succeed, even if they each go about it differently.

Viable companies with good numbers

Temporal, a Seattle-based startup that is building an open-source, stateful microservices orchestration platform, today announced that it has raised an $18.75 million Series A round led by Sequoia Capital. Existing investors Addition Ventures and Amplify Partners also joined, together with new investor Madrona Venture Group. With this, the company has now raised a total of $25.5 million.

Founded by Maxim Fateev (CEO) and Samar Abbas (CTO), who created the open-source Cadence orchestration engine during their time at Uber, Temporal aims to make it easier for developers and operators to run microservices in production. Current users include the likes of Box and Snap.

“Before microservices, coding applications was much simpler,” Temporal’s Fateev told me. “Resources were always located in the same place — the monolith server with a single DB — which meant developers didn’t have to codify a bunch of guessing about where things were. Microservices, on the other hand, are highly distributed, which means developers need to coordinate changes across a number of servers in different physical locations.”

Those servers could go down at any time, so engineers often spend a lot of time building custom reliability code to make calls to these services. As Fateev argues, that’s table stakes and doesn’t help these developers create something that builds real business value. Temporal gives these developers access to a set of what the team calls “reliability primitives” that handle these use cases. “This means developers spend far more time writing differentiated code for their business and end up with a more reliable application than they could have built themselves,” said Fateev.

Temporal’s target use is virtually any developer who works with microservices — and wants them to be reliable. Because of this, the company’s tool — despite offering a read-only web-based user interface for administering and monitoring the system — isn’t the main focus here. The company also doesn’t have any plans to create a no-code/low-code workflow builder, Fateev tells me. However, since it is open-source, quite a few Temporal users build their own solutions on top of it.

The company itself plans to offer a cloud-based Temporal-as-a-Service offering soon. Interestingly, Fateev tells me that the team isn’t looking at offering enterprise support or licensing in the near future. “After spending a lot of time thinking it over, we decided a hosted offering was best for the open-source community and long-term growth of the business,” he said.

Unsurprisingly, the company plans to use the new funding to improve its existing tool and build out this cloud service, with plans to launch it into general availability next year. At the same time, the team plans to say true to its open-source roots and host events and provide more resources to its community.

“Temporal enables Snapchat to focus on building the business logic of a robust asynchronous API system without requiring a complex state management infrastructure,” said Steven Sun, Snap Tech Lead, Staff Software Engineer. “This has improved the efficiency of launching our services for the Snapchat community.”

Dataloop, a Tel Aviv-based startup that specializes in helping businesses manage the entire data life cycle for their AI projects, including helping them annotate their data sets, today announced that it has now raised a total of $16 million. This includes a $5 seed round that was previously unreported, as well as an $11 million Series A round that recently closed.

The Series A round was led by Amiti Ventures, with participation from F2 Venture Capital, crowdfunding platform OurCrowd, NextLeap Ventures and SeedIL Ventures.

“Many organizations continue to struggle with moving their AI and ML projects into production as a result of data labeling limitations and a lack of real-time validation that can only be achieved with human input into the system,” said Dataloop CEO Eran Shlomo. “With this investment, we are committed, along with our partners, to overcoming these roadblocks and providing next generation data management tools that will transform the AI industry and meet the rising demand for innovation in global markets.”

Image Credits: Dataloop

For the most part, Dataloop specializes in helping businesses manage and annotate their visual data. It’s agnostic to the vertical its customers are in, but we’re talking about anything from robotics and drones to retail and autonomous driving.

The platform itself centers around the “humans in the loop” model that complements the automated systems, with the ability for humans to train and correct the model as needed. It combines the hosted annotation platform with a Python SDK and REST API for developers, as well as a serverless Functions-as-a-Service environment that runs on top of a Kubernetes cluster for automating dataflows.

Image Credits: Dataloop

The company was founded in 2017. It’ll use the new funding to grow its presence in the U.S. and European markets, something that’s pretty standard for Israeli startups, and build out its engineering team as well.

Atlassian has been offering collaboration tools, often favored by developers and IT for some time with such stalwarts as Jira for help desk tickets, Confluence to organize your work and BitBucket to organize your development deliverables, but what it lacked was a machine learning layer across the platform to help users work smarter within and across the applications in the Atlassian family.

That changed today, when Atlassian announced it has been building that machine learning layer, called Atlassian Smarts, and is releasing several tools that take advantage of it. It’s worth noting that unlike Salesforce, which calls its intelligence layer Einstein or Adobe, which calls its Sensei, Atlassian chose to forgo the cutesy marketing terms and just let the technology stand on its own.

Shihab Hamid, the founder of the Smarts and Machine Learning Team at Atlassian, who has been with the company 14 years, says they avoided a marketing name by design. “I think one of the things that we’re trying to focus on is actually the user experience and so rather than packaging or branding the technology, we’re really about optimizing teamwork,” Hamid told TechCrunch.

Hamid says that the goal of the machine learning layer is to remove the complexity involved with organizing people and information across the platform.

“Simple tasks like finding the right person or the right document becomes a challenge, or at least they slow down productivity and take time away from the creative high-value work that everyone wants to be doing, and teamwork itself is super messy and collaboration is complicated. These are human challenges that don’t really have one right solution,” he said.

He says that Atlassian has decided to solve these problems using machine learning with the goal of speeding up repetitive, time-intensive tasks. Much like Adobe or Salesforce, Atlassian has built this underlying layer of machine smarts, for lack of a better term, that can be distributed across their platform to deliver this kind of machine learning-based functionality wherever it makes sense for the particular product or service.

“We’ve invested in building this functionality directly into the Atlassian platform to bring together IT and development teams to unify work, so the Atlassian flagship products like JIRA and Confluence sit on top of this common platform and benefit from that common functionality across products. And so the idea is if we can build that common predictive capability at the platform layer we can actually proliferate smarts and benefit from the data that we gather across our products,” Hamid said.

The first pieces fit into this vision. For starters, Atlassian is offering a smart search tool that helps users find content across Atlassian tools faster by understanding who you are and how you work. “So by knowing where users work and what they work on, we’re able to proactively provide access to the right documents and accelerate work,” he said.

The second piece is more about collaboration and building teams with the best personnel for a given task. A new tool called predictive user mentions helps Jira and Confluence users find the right people for the job.

“What we’ve done with the Atlassian platform is actually baked in that intelligence, because we know what you work on and who you collaborate with, so we can predict who should be involved and brought into the conversation,” Hamid explained.

Finally, the company announced a tool specifically for Jira users, which bundles together similar sets of help requests and that should lead to faster resolution over doing them manually one at a time.

“We’re soon launching a feature in JIRA Service Desk that allows users to cluster similar tickets together, and operate on them to accelerate IT workflows, and this is done in the background using ML techniques to calculate the similarity of tickets, based on the summary and description, and so on.”

All of this was made possible by the company’s previous shift from mostly on-premises to the cloud and the flexibility that gave them to build new tooling that crosses the entire platform.

Today’s announcements are just the start of what Atlassian hopes will be a slew of new machine learning-fueled features being added to the platform in the coming months and years.