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Logentries Releases Logging Container for Docker 1.5

Logentries announced a new Logging Container built for Docker 1.5 featuring easy collection and analysis of container-level performance stats and system-level log data.

The new Logentries Container features Docker’s new stats API and addresses existing challenges with logging on Docker, enabling easy access to collecting and visualizing important server and resources usage stats. Additionally, Logentries has released a new Community Pack for Docker that offers users immediate access to pre-configured searches, tags, alerts, and data visualizations. The Community Pack makes Docker container log data easy to aggregate, search and visualize for deeper understanding of Docker environments.

Nick Stinemates, Head of Business Development & Technical Alliances, Docker, said: “The Logentries logging container for Docker uses the new stats API to enable our users to easily collect key per container performance statistics around CPU, memory, network and storage I/O and to analyze them to understand important information and trends.”

“There is a huge shift in organizations moving to containerized, distributed application architectures,” said Cian Ó Maidín, CEO, nearForm. “At nearForm we are helping organizations design and build micro-service architectures everyday. We were excited to work with Logentries to build this integration and provide Docker users with better visibility into their Docker environments.”

Containerization and distributed applications are changing how Development and Operations teams design, build and monitor systems. Historically there has been little native logging support for collecting logs from containers, especially at large scale, limiting users’ ability to monitor and troubleshoot their production environments. For the first time, users now have access to a multi-dimensional approach to understanding container utilization and performance by logging stats via the new Docker stats API. With access to these Docker stats, users can correlate this valuable data with other structured log data sources (e.g. JSON) to produce deeper visibility into Docker environments.

The Logentries Container and Community Pack for Docker provide users with:

- A specialized Docker container for log collection and monitoring

- Centralized logging capabilities for Docker environments

- Container-level resource usage statistics such as CPU, Memory, Network, etc.

- Out-of-the-box saved searches, tags, alerts and data visualizations

“The new Docker stats API is a big step forward in enabling customers to collect important information about their Docker containers and the apps running on them,” said Trevor Parsons, Co-founder and Chief Scientist, Logentries. “With the Logentries new logging container, users can easily capture this data, correlate it with other important system metrics, providing required visibility into production Docker environments.”

The Logentries Docker Container was developed together with the nearForm team, leaders in Node.js and Docker development. Logentries and nearForm jointly tackled the challenge of getting deeper insight into Docker environments by enabling users to easily collect and analyze valuable container-level stats. The cloud-based Logentries service collects and pre-processes log events in real-time for on-demand analysis, alerting and visualization. With custom tagging and filtering, users can correlate data across their infrastructure to better understand application usage and performance, security and performance issues, and user behavior.

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Logentries Releases Logging Container for Docker 1.5

Logentries announced a new Logging Container built for Docker 1.5 featuring easy collection and analysis of container-level performance stats and system-level log data.

The new Logentries Container features Docker’s new stats API and addresses existing challenges with logging on Docker, enabling easy access to collecting and visualizing important server and resources usage stats. Additionally, Logentries has released a new Community Pack for Docker that offers users immediate access to pre-configured searches, tags, alerts, and data visualizations. The Community Pack makes Docker container log data easy to aggregate, search and visualize for deeper understanding of Docker environments.

Nick Stinemates, Head of Business Development & Technical Alliances, Docker, said: “The Logentries logging container for Docker uses the new stats API to enable our users to easily collect key per container performance statistics around CPU, memory, network and storage I/O and to analyze them to understand important information and trends.”

“There is a huge shift in organizations moving to containerized, distributed application architectures,” said Cian Ó Maidín, CEO, nearForm. “At nearForm we are helping organizations design and build micro-service architectures everyday. We were excited to work with Logentries to build this integration and provide Docker users with better visibility into their Docker environments.”

Containerization and distributed applications are changing how Development and Operations teams design, build and monitor systems. Historically there has been little native logging support for collecting logs from containers, especially at large scale, limiting users’ ability to monitor and troubleshoot their production environments. For the first time, users now have access to a multi-dimensional approach to understanding container utilization and performance by logging stats via the new Docker stats API. With access to these Docker stats, users can correlate this valuable data with other structured log data sources (e.g. JSON) to produce deeper visibility into Docker environments.

The Logentries Container and Community Pack for Docker provide users with:

- A specialized Docker container for log collection and monitoring

- Centralized logging capabilities for Docker environments

- Container-level resource usage statistics such as CPU, Memory, Network, etc.

- Out-of-the-box saved searches, tags, alerts and data visualizations

“The new Docker stats API is a big step forward in enabling customers to collect important information about their Docker containers and the apps running on them,” said Trevor Parsons, Co-founder and Chief Scientist, Logentries. “With the Logentries new logging container, users can easily capture this data, correlate it with other important system metrics, providing required visibility into production Docker environments.”

The Logentries Docker Container was developed together with the nearForm team, leaders in Node.js and Docker development. Logentries and nearForm jointly tackled the challenge of getting deeper insight into Docker environments by enabling users to easily collect and analyze valuable container-level stats. The cloud-based Logentries service collects and pre-processes log events in real-time for on-demand analysis, alerting and visualization. With custom tagging and filtering, users can correlate data across their infrastructure to better understand application usage and performance, security and performance issues, and user behavior.

The Latest

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 ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.