Skip to main content

Infoblox to Acquire Kentik

Infoblox has entered into a definitive agreement to acquire Kentik. 

Kentik provides real-time visibility across networks, applications and cloud environments, helping organizations optimize performance and accelerate AI-driven network operations. This planned acquisition is subject to regulatory approvals and closing conditions.

The planned acquisition of Kentik will help customers understand what is happening across their infrastructure continuously and identify and prioritize issues more efficiently, while also solidifying data foundations for AI tools. This accelerates the company's vision for an infrastructure-centric, AI-driven security and networking platform.

"Infoblox sits at a unique intersection. Every device, application and cloud workload on a customer's network runs through our technology, generating unparalleled context. With Kentik, we expand and enrich that context, allowing us to provide networking, cloud and security teams the real-time hybrid cloud intelligence they need to act with confidence," said Scott Harrell, CEO, Infoblox. "Our customers need robust data and insights for their agentic operations so they can provide the increasing levels of resiliency, performance and security their businesses demand. Together with Kentik, we’re able to deliver that.”

Together, the combined platform will allow customers to have a more holistic, real-time view of everything that is happening on the network and in the cloud. Infoblox knows what's on the network and why it should be trusted; Kentik knows how traffic moves across it and whether it's performing. The result is a pioneering infrastructure-centric network and security intelligence platform: one place where network identity, DNS context, real-time traffic behavior and AI-guided workflows converge. That platform will give customers the following key benefits:

  • Hybrid and multi-cloud observability: With Kentik's topology maps enriched by Infoblox's DNS names, asset context and user identity, every flow reveals not just where traffic went, but who sent it and what it reached. Network and operations teams get a single, correlated view of traffic with full context behind every source and destination.
  • Security intelligence: Infoblox’s preemptive, DNS-based threat intelligence will be correlated with Kentik's flow data. Together, the combination of technologies will confirm whether the asset connected, how much data left the network and which other assets it then reached, compressing triage time and raising confidence in response decisions, all in one platform.
  • An AI-ready data foundation: A shared data fabric that provides a clean, continuously updated data layer helps teams move from signal to answer to action. Integration through open protocols like the Infoblox Model Context Protocol (MCP) Server will ultimately enable teams to connect this fabric to the orchestration and AI tools they already use.

“Kentik's vision has been to help organizations operate increasingly complex networks with intelligence rather than intuition, and Infoblox is the trusted source of truth and identity that provides the foundation for the infrastructure our customers depend on every day,” said Avi Freedman, CEO, Kentik. “Combining that authoritative infrastructure context with Kentik's network intelligence platform creates powerful new opportunities to automate operations, accelerate troubleshooting, strengthen security and support the velocity of networking today."

Oppenheimer & Co. Inc. is acting as exclusive financial advisor, and Fenwick & West LLP is acting as legal advisor to Kentik. Cleary Gottlieb Steen & Hamilton LLP is acting as legal advisor to Infoblox.

The Latest

For fifteen years, observability lived downstream of everything else. Code shipped, something broke, an engineer went to the dashboards. The job was forensic. The pillars we built, such as logs, metrics, and traces, were designed for that role: tell a human what just happened, fast enough that they can make it stop. That role has quietly ended ...

Hybrid IT has become the standard operating model for enterprises — but that companies are still looking for the right hybrid IT mix, according to the 2026 State of the Data Center Report from CoreSite. After years of cloud migration and hybrid adoption, organizations are shifting their focus from deciding whether to use cloud, colocation or on-premises infrastructure to determining which workloads belong in each environment ...

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

Infoblox to Acquire Kentik

Infoblox has entered into a definitive agreement to acquire Kentik. 

Kentik provides real-time visibility across networks, applications and cloud environments, helping organizations optimize performance and accelerate AI-driven network operations. This planned acquisition is subject to regulatory approvals and closing conditions.

The planned acquisition of Kentik will help customers understand what is happening across their infrastructure continuously and identify and prioritize issues more efficiently, while also solidifying data foundations for AI tools. This accelerates the company's vision for an infrastructure-centric, AI-driven security and networking platform.

"Infoblox sits at a unique intersection. Every device, application and cloud workload on a customer's network runs through our technology, generating unparalleled context. With Kentik, we expand and enrich that context, allowing us to provide networking, cloud and security teams the real-time hybrid cloud intelligence they need to act with confidence," said Scott Harrell, CEO, Infoblox. "Our customers need robust data and insights for their agentic operations so they can provide the increasing levels of resiliency, performance and security their businesses demand. Together with Kentik, we’re able to deliver that.”

Together, the combined platform will allow customers to have a more holistic, real-time view of everything that is happening on the network and in the cloud. Infoblox knows what's on the network and why it should be trusted; Kentik knows how traffic moves across it and whether it's performing. The result is a pioneering infrastructure-centric network and security intelligence platform: one place where network identity, DNS context, real-time traffic behavior and AI-guided workflows converge. That platform will give customers the following key benefits:

  • Hybrid and multi-cloud observability: With Kentik's topology maps enriched by Infoblox's DNS names, asset context and user identity, every flow reveals not just where traffic went, but who sent it and what it reached. Network and operations teams get a single, correlated view of traffic with full context behind every source and destination.
  • Security intelligence: Infoblox’s preemptive, DNS-based threat intelligence will be correlated with Kentik's flow data. Together, the combination of technologies will confirm whether the asset connected, how much data left the network and which other assets it then reached, compressing triage time and raising confidence in response decisions, all in one platform.
  • An AI-ready data foundation: A shared data fabric that provides a clean, continuously updated data layer helps teams move from signal to answer to action. Integration through open protocols like the Infoblox Model Context Protocol (MCP) Server will ultimately enable teams to connect this fabric to the orchestration and AI tools they already use.

“Kentik's vision has been to help organizations operate increasingly complex networks with intelligence rather than intuition, and Infoblox is the trusted source of truth and identity that provides the foundation for the infrastructure our customers depend on every day,” said Avi Freedman, CEO, Kentik. “Combining that authoritative infrastructure context with Kentik's network intelligence platform creates powerful new opportunities to automate operations, accelerate troubleshooting, strengthen security and support the velocity of networking today."

Oppenheimer & Co. Inc. is acting as exclusive financial advisor, and Fenwick & West LLP is acting as legal advisor to Kentik. Cleary Gottlieb Steen & Hamilton LLP is acting as legal advisor to Infoblox.

The Latest

For fifteen years, observability lived downstream of everything else. Code shipped, something broke, an engineer went to the dashboards. The job was forensic. The pillars we built, such as logs, metrics, and traces, were designed for that role: tell a human what just happened, fast enough that they can make it stop. That role has quietly ended ...

Hybrid IT has become the standard operating model for enterprises — but that companies are still looking for the right hybrid IT mix, according to the 2026 State of the Data Center Report from CoreSite. After years of cloud migration and hybrid adoption, organizations are shifting their focus from deciding whether to use cloud, colocation or on-premises infrastructure to determining which workloads belong in each environment ...

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...