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Netdata Raises $14.2M in Series A Funding

Netdata completed a new round of financing totaling $14.2M led by Bessemer Venture Partners, with participation from existing investors Bain Capital Ventures and Uncorrelated Ventures, bringing its total raised in Series A to $31M.

The investment will be used to accelerate research and development in support of Netdata’s open, interoperable, and extensible monitoring and troubleshooting platform.

The funding extends Netdata’s Series A round and builds on an exceptional year punctuated by the growing adoption of the open-source Netdata Agent and the introduction of the new Netdata Cloud software-as-a-service offering, a cloud-based console for infrastructure-wide monitoring, enabling teams to collaborate and work in parallel to streamline troubleshooting workflows, driving down incident response times.

Key features and benefits of the Netdata platform include:

- Visualizes unlimited, highly granular, real-time metrics optimized for anomaly detection

- Deploys easily with no preplanning and zero configuration, with autodetection of hundreds of turnkey integrations, enabling monitoring and troubleshooting of web servers, file systems, databases, containers, and more

- Runs seamlessly on physical or virtual servers, containers and IoT devices to collect per-second or per-event metrics with no limits on scalability thanks to its distributed data architecture

- Works autonomously to collect, store, visualize, check, stream, and archive data, or can be easily integrated into existing monitoring tool chains

Both open-source Netdata Agent and closed-source Netdata Cloud are offered free of charge.

“Netdata has experienced exponential growth by filling an unmet need: giving SREs, DevOps engineers, sysadmins, and developers a way to gain visibility into their infrastructure in minutes, with zero configuration, thousands of metrics, and milliseconds from data collection to visualization,” said Costa Tsaousis, founder and CEO of Netdata. “With nearly a million new Docker pulls every day, Netdata has proven to be the most useful tool in the troubleshooting arsenal of IT professionals who are often challenged by the cost, complexity, and limitations of existing monitoring solutions. This new investment will further our vision by enabling us to build upon our community momentum to deliver innovative solutions in both our open source project and future commercial products.”

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Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

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When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

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Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Netdata Raises $14.2M in Series A Funding

Netdata completed a new round of financing totaling $14.2M led by Bessemer Venture Partners, with participation from existing investors Bain Capital Ventures and Uncorrelated Ventures, bringing its total raised in Series A to $31M.

The investment will be used to accelerate research and development in support of Netdata’s open, interoperable, and extensible monitoring and troubleshooting platform.

The funding extends Netdata’s Series A round and builds on an exceptional year punctuated by the growing adoption of the open-source Netdata Agent and the introduction of the new Netdata Cloud software-as-a-service offering, a cloud-based console for infrastructure-wide monitoring, enabling teams to collaborate and work in parallel to streamline troubleshooting workflows, driving down incident response times.

Key features and benefits of the Netdata platform include:

- Visualizes unlimited, highly granular, real-time metrics optimized for anomaly detection

- Deploys easily with no preplanning and zero configuration, with autodetection of hundreds of turnkey integrations, enabling monitoring and troubleshooting of web servers, file systems, databases, containers, and more

- Runs seamlessly on physical or virtual servers, containers and IoT devices to collect per-second or per-event metrics with no limits on scalability thanks to its distributed data architecture

- Works autonomously to collect, store, visualize, check, stream, and archive data, or can be easily integrated into existing monitoring tool chains

Both open-source Netdata Agent and closed-source Netdata Cloud are offered free of charge.

“Netdata has experienced exponential growth by filling an unmet need: giving SREs, DevOps engineers, sysadmins, and developers a way to gain visibility into their infrastructure in minutes, with zero configuration, thousands of metrics, and milliseconds from data collection to visualization,” said Costa Tsaousis, founder and CEO of Netdata. “With nearly a million new Docker pulls every day, Netdata has proven to be the most useful tool in the troubleshooting arsenal of IT professionals who are often challenged by the cost, complexity, and limitations of existing monitoring solutions. This new investment will further our vision by enabling us to build upon our community momentum to deliver innovative solutions in both our open source project and future commercial products.”

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...