LogDNA announced that cybersecurity investment and advisory firm NightDragon will lead a Series D funding round of $50 million with participation from existing investors Emergence and Initialized Capital.
This accelerates LogDNA’s vision of enabling enterprises to maximize the value of observability data in motion.
With LogDNA’s cloud-first platform, some of the world’s largest companies are able to manage and take concrete action on observability data in real time and at hyperscale. The need for this type of solution is at an all-time high, and the LogDNA team sees a huge market opportunity to build on the platform’s existing capabilities. The investment will enable the company to deliver a more robust observability data pipeline solution that will empower builders — the application developers, the site reliability engineers, the platform engineers, and the teams that make sure that what’s being built is secure — to harness the full power of machine data within their workflows.
NightDragon Co-founder and Managing Director, Dave DeWalt, who serves as Vice Chair for LogDNA’s Board of Directors, said he sees this as an opportunity to rethink the paradigm around data, especially for use cases like cybersecurity.
“Organizations need a comprehensive platform that ingests and normalizes massive amounts of data in the cloud and at hyperscale. With this type of platform, stakeholders from the developer to the C-Suite are empowered to make smarter, more cost-effective decisions and reduce the mean time to detection and remediation for cyberattacks,” said Dave DeWalt, Founder and Managing Director, NightDragon. “LogDNA has the right team and technology to address this challenge head on. NightDragon is proud to partner with them to accelerate their vision and help enterprises everywhere realize the true potential of data across their organizations.”
The prevailing approach in the observability market today is to manage the massive amount of data through a ‘single pane of glass’. While seemingly practical, it becomes a choke point, making data-intensive innovation and operations slower, more complicated, and prone to errors and heightened risk. They struggle to control costs and enable a wide array of people who need access to their observability data.
“Now that open systems, cloud-native architectures and interconnected applications and data are commonplace, a single pane of glass is far too limiting. It’s time to shift the focus to the people who use the data,” said Tucker Callaway, CEO, LogDNA. “The data consumer must be able to capture the real-time value of data in motion, not just data at rest in storage. They must be able to ingest and process data to a central point — the pipeline — and then route it to the tools where people are actually working, rather than forcing them to break their workflow to use a different tool. This is the problem that LogDNA aims to solve.”
The investment allows LogDNA to accelerate time to market for a new observability data pipeline solution, which will enable enterprises to ingest all of their data to a single platform, normalize it, and seamlessly route it to the appropriate teams, so they can take meaningful action quickly. The solution will be generally available in 2022. LogDNA will also continue rapidly expanding its team to support its growth and innovation, and it plans to expand its strategic partnerships to support more cloud and services providers, platforms, and technical integrations.
The Latest
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...
The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...
Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...
If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...