Skip to main content

InfluxData Releases Telegraf Enterprise

InfluxData announced the general availability of Telegraf Enterprise, a new offering that gives organizations centralized control over large-scale Telegraf deployments. 

With automated configuration management, fleet-wide visibility, enhanced security, and commercial support, teams can operate tens of thousands of agents as a single system, while reducing the complexity of running telemetry pipelines across distributed environments.

Telegraf is the open source standard for connecting any data source to any destination across cloud, edge, and physical infrastructure. As deployments grow to tens of thousands of agents, teams often rely on fragmented tools and manual processes to manage configuration, maintain consistency, and monitor fleet health. Telegraf Enterprise provides centralized control and visibility, keeping every agent consistent and up to date from a single control plane.

"Telegraf solved the challenge of collecting data from virtually anywhere. At scale, the challenge becomes operating that collection layer reliably,” said Evan Kaplan, CEO of InfluxData. "Telegraf Enterprise gives platform teams a centralized way to manage, monitor, and update their entire fleet, turning thousands of individual agents into a system that can operate as one."

The foundation of Telegraf Enterprise is Telegraf Controller, a centralized management console, API, and UI that provides a single view of agent health, configuration status, and fleet-wide operations. By replacing fragmented processes with centralized control and automation, Telegraf Enterprise turns a scattered fleet of agents into a single system that teams can operate reliably at scale.

With Telegraf Enterprise, teams can:

  • Standardize fleet configurations: Utilize reusable templates to maintain absolute consistency across diverse environments.
  • Automate safe rollouts: Push bulk configuration updates across fleets with granular control.
  • Gain real-time health monitoring: Instantly pinpoint underperforming agents, errors, or blind spots across the network.
  • Eliminate operational risk: Reduce configuration drift and manual errors before they impact telemetry pipelines.

Telegraf Enterprise introduces dedicated, commercial support for both the Telegraf open source agent and Telegraf Controller, as well as more than 400 official plugins, backed by InfluxData’s support program. By easing the burden of configuration maintenance from internal engineering teams, organizations can deploy Telegraf at scale with greater confidence and operational reliability.

Telegraf Enterprise integrates into existing architectures without changing where data is stored or analyzed. Its pull-based configuration model keeps updates predictable and aligned with how teams already operate, so it can be adopted without disruption.

Telegraf Controller is available now with a free tier for teams looking to centralize Telegraf management. Teams operating large-scale deployments can upgrade to Telegraf Enterprise for enhanced security, commercial support, and operational capabilities.

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 ...

InfluxData Releases Telegraf Enterprise

InfluxData announced the general availability of Telegraf Enterprise, a new offering that gives organizations centralized control over large-scale Telegraf deployments. 

With automated configuration management, fleet-wide visibility, enhanced security, and commercial support, teams can operate tens of thousands of agents as a single system, while reducing the complexity of running telemetry pipelines across distributed environments.

Telegraf is the open source standard for connecting any data source to any destination across cloud, edge, and physical infrastructure. As deployments grow to tens of thousands of agents, teams often rely on fragmented tools and manual processes to manage configuration, maintain consistency, and monitor fleet health. Telegraf Enterprise provides centralized control and visibility, keeping every agent consistent and up to date from a single control plane.

"Telegraf solved the challenge of collecting data from virtually anywhere. At scale, the challenge becomes operating that collection layer reliably,” said Evan Kaplan, CEO of InfluxData. "Telegraf Enterprise gives platform teams a centralized way to manage, monitor, and update their entire fleet, turning thousands of individual agents into a system that can operate as one."

The foundation of Telegraf Enterprise is Telegraf Controller, a centralized management console, API, and UI that provides a single view of agent health, configuration status, and fleet-wide operations. By replacing fragmented processes with centralized control and automation, Telegraf Enterprise turns a scattered fleet of agents into a single system that teams can operate reliably at scale.

With Telegraf Enterprise, teams can:

  • Standardize fleet configurations: Utilize reusable templates to maintain absolute consistency across diverse environments.
  • Automate safe rollouts: Push bulk configuration updates across fleets with granular control.
  • Gain real-time health monitoring: Instantly pinpoint underperforming agents, errors, or blind spots across the network.
  • Eliminate operational risk: Reduce configuration drift and manual errors before they impact telemetry pipelines.

Telegraf Enterprise introduces dedicated, commercial support for both the Telegraf open source agent and Telegraf Controller, as well as more than 400 official plugins, backed by InfluxData’s support program. By easing the burden of configuration maintenance from internal engineering teams, organizations can deploy Telegraf at scale with greater confidence and operational reliability.

Telegraf Enterprise integrates into existing architectures without changing where data is stored or analyzed. Its pull-based configuration model keeps updates predictable and aligned with how teams already operate, so it can be adopted without disruption.

Telegraf Controller is available now with a free tier for teams looking to centralize Telegraf management. Teams operating large-scale deployments can upgrade to Telegraf Enterprise for enhanced security, commercial support, and operational capabilities.

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 ...