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Kentik Launches Kentik Labs - Open-Source Network Observability Initiative

Kentik announced Kentik Labs, a hub for the developer, DevOps and site reliability engineering (SRE) community.

Kentik Labs launches with five open-source projects and a focus on democratizing network data to support the community in finding and fixing issues across any infrastructure.

Developer and IT teams face a number of complexities when it comes to understanding the health and performance of applications distributed across clouds and data centers. When a failure occurs, these teams scramble to investigate whether the issue is due to code, the infrastructure or something else. Kentik Labs will help developers and IT professionals reduce their mean time to recovery (MTTR), or the amount of time it takes to recover when a problem occurs, by providing open-source network observability tools to quickly rule the network in or out as the issue.

“Whether the problem is the application or the network, Kentik Labs offers tools to focus your efforts at the right layer with the right team,” said Ian Pye, co-founder and Chief Scientist of Kentik and co-chair of Kentik Labs. “In addition, branching out into open-source technology will allow us to participate in adjacent communities and define the future of observability.”

Kentik Labs launches with five open-source projects:

- kTranslate: A system for pulling and pushing network data and the basis of the integration with New Relic

- NetDiag: Scalable, asynchronous implementations of low-level network diagnostics

- Convis: Example code showing how to use eBPF to attribute process and container information to network traffic; learn more about this project in Pye’s recent talk at the eBPF Summit 2021

- kProbe: A high-performance host and sensor network probe (PCAP)

- Grafana App: A real-time internet-scale ingest and querying of network data

“Cloud and containers are pushing applications to be distributed by default. This means the network is a critical component in building anything,” said Nick Stinemates, VP of BD at Kentik and co-chair of Kentik Labs. “With the launch of Kentik Labs, we’ll be exploring how to best integrate these concepts with the tools our users and customers are using today.”

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Kentik Launches Kentik Labs - Open-Source Network Observability Initiative

Kentik announced Kentik Labs, a hub for the developer, DevOps and site reliability engineering (SRE) community.

Kentik Labs launches with five open-source projects and a focus on democratizing network data to support the community in finding and fixing issues across any infrastructure.

Developer and IT teams face a number of complexities when it comes to understanding the health and performance of applications distributed across clouds and data centers. When a failure occurs, these teams scramble to investigate whether the issue is due to code, the infrastructure or something else. Kentik Labs will help developers and IT professionals reduce their mean time to recovery (MTTR), or the amount of time it takes to recover when a problem occurs, by providing open-source network observability tools to quickly rule the network in or out as the issue.

“Whether the problem is the application or the network, Kentik Labs offers tools to focus your efforts at the right layer with the right team,” said Ian Pye, co-founder and Chief Scientist of Kentik and co-chair of Kentik Labs. “In addition, branching out into open-source technology will allow us to participate in adjacent communities and define the future of observability.”

Kentik Labs launches with five open-source projects:

- kTranslate: A system for pulling and pushing network data and the basis of the integration with New Relic

- NetDiag: Scalable, asynchronous implementations of low-level network diagnostics

- Convis: Example code showing how to use eBPF to attribute process and container information to network traffic; learn more about this project in Pye’s recent talk at the eBPF Summit 2021

- kProbe: A high-performance host and sensor network probe (PCAP)

- Grafana App: A real-time internet-scale ingest and querying of network data

“Cloud and containers are pushing applications to be distributed by default. This means the network is a critical component in building anything,” said Nick Stinemates, VP of BD at Kentik and co-chair of Kentik Labs. “With the launch of Kentik Labs, we’ll be exploring how to best integrate these concepts with the tools our users and customers are using today.”

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...