Skip to main content

LogDNA Announces Series D Funding

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

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

LogDNA Announces Series D Funding

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

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...