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LogDNA Expands Template Library for NGINX and Kubernetes

LogDNA released new templates for DevOps teams using NGINX with Kubernetes.

Configured specifically for the open source and NGINX Plus-based versions of NGINX Ingress Controller, these templates ensure that developers can use best practices to quickly gain visibility into their Kubernetes Ingress logs, allowing them to create an end-to-end view of their data across all systems for easier monitoring and troubleshooting.

Container orchestration with Kubernetes has exploded in popularity over the past several years with more than 80% of enterprises using it in production. An Ingress controller is one of the most important components of a production-grade Kubernetes deployment, and as the most widely used Ingress technology, NGINX provides developers with an easy-to-use and highly customizable solution. For these developers, LogDNA is a critical part of their observability stack, offering actionable visibility into Kubernetes clusters so that engineers aren’t drowning in data that they can’t use effectively.

“Kubernetes clusters produce a lot of data. This is good because more data means more visibility into the environment, but it’s important that our customers have shortcuts to actionability that save them time,” said Peter Cho, VP of Product, LogDNA. “The NGINX Ingress Controller template can be set up in minutes, making it easy for users to quickly gain value from our platform. They spend less time with manual configurations so they can get back to building features and products that benefit their businesses.”

As technology partners, LogDNA and NGINX leveraged our respective expertise around Kubernetes to create a DevOps-focused logging template for NGINX Ingress Controller. The new template includes preconfigured Views, Boards, and Screens that help developers visualize web traffic, latency, and response codes coming from the NGINX Ingress Controller. NGINX users who are using NGINX as a web server can also leverage the Web Server template to quickly unlock insights and gain visibility into HTTP web servers via LogDNA with similar pre-configured Views, Boards, and Screens. Now, developers using NGINX can view logs from various sources—from the frontend to the backend—all in one platform.

LogDNA Template Library is a growing collection of Views, Boards, and Screens templates to simply install into a user’s LogDNA account with just a few clicks. The library also includes web server templates for Apache, Heroku templates for Dynos and web apps, and Windows Security templates for events via NXLog. These make it easy for LogDNA customers to leverage best practices without having to fiddle with log line formats, manually configure alert conditions, or figure out which metrics to prioritize and plot. Templates also ensure that users get the maximum benefit from using multiple platforms together.

All templates in the Template Library are configurable after installation so it’s easy to expand, alert on, and customize.

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LogDNA Expands Template Library for NGINX and Kubernetes

LogDNA released new templates for DevOps teams using NGINX with Kubernetes.

Configured specifically for the open source and NGINX Plus-based versions of NGINX Ingress Controller, these templates ensure that developers can use best practices to quickly gain visibility into their Kubernetes Ingress logs, allowing them to create an end-to-end view of their data across all systems for easier monitoring and troubleshooting.

Container orchestration with Kubernetes has exploded in popularity over the past several years with more than 80% of enterprises using it in production. An Ingress controller is one of the most important components of a production-grade Kubernetes deployment, and as the most widely used Ingress technology, NGINX provides developers with an easy-to-use and highly customizable solution. For these developers, LogDNA is a critical part of their observability stack, offering actionable visibility into Kubernetes clusters so that engineers aren’t drowning in data that they can’t use effectively.

“Kubernetes clusters produce a lot of data. This is good because more data means more visibility into the environment, but it’s important that our customers have shortcuts to actionability that save them time,” said Peter Cho, VP of Product, LogDNA. “The NGINX Ingress Controller template can be set up in minutes, making it easy for users to quickly gain value from our platform. They spend less time with manual configurations so they can get back to building features and products that benefit their businesses.”

As technology partners, LogDNA and NGINX leveraged our respective expertise around Kubernetes to create a DevOps-focused logging template for NGINX Ingress Controller. The new template includes preconfigured Views, Boards, and Screens that help developers visualize web traffic, latency, and response codes coming from the NGINX Ingress Controller. NGINX users who are using NGINX as a web server can also leverage the Web Server template to quickly unlock insights and gain visibility into HTTP web servers via LogDNA with similar pre-configured Views, Boards, and Screens. Now, developers using NGINX can view logs from various sources—from the frontend to the backend—all in one platform.

LogDNA Template Library is a growing collection of Views, Boards, and Screens templates to simply install into a user’s LogDNA account with just a few clicks. The library also includes web server templates for Apache, Heroku templates for Dynos and web apps, and Windows Security templates for events via NXLog. These make it easy for LogDNA customers to leverage best practices without having to fiddle with log line formats, manually configure alert conditions, or figure out which metrics to prioritize and plot. Templates also ensure that users get the maximum benefit from using multiple platforms together.

All templates in the Template Library are configurable after installation so it’s easy to expand, alert on, and customize.

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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 ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...