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Logentries Integrates Server Metrics with Log Data for Real-time Visibility into System Performance and Capacity

Logentries introduced server monitoring metrics integrated with machine generated log data for real-time correlation.

Logentries is providing a single view of server metrics integrated with both client- and server- side log data as well as application level performance metrics to give a complete end-to-end view of system performance. The Logentries Windows and Linux agents actively track server CPU, server memory (total and active), disk I/O, and network activity, automatically creating server metrics logs that can be monitored, analyzed, and archived.

Many development and operations teams use multiple tools to understand what is happening across all servers, applications and end users. Logentries now provides users with a single view of server monitoring unified with existing monitoring and analytics using auto-generated log files. By utilizing logs to capture key server metrics, users can easily drill down to get a fine-grained view into issues or particular actions to understand root cause and deeper context around the events that matter to the business. The Logentries service applies its unique pre-processing engine to these server logs and enables users to easily build dashboards, custom tagging and alerting on the real-time data.

Some of the most valuable server metrics that can be collected, analyzed and alerted on using log data include:

-CPU usage

- Memory (active and available)

- Network activity (sent and received)

- Bandwidth usage

- Disk activity

- Data consumption per user

“As we work to connect our users to all of their data sources and systems using logs, we know that server monitoring is an important element to overall system health and performance,” said Trevor Parsons, Co-founder and Chief scientist at Logentries. “Bringing server monitoring data together with log-level metrics for system-wide monitoring provides users with an all-in-one viewpoint.”

Using logs to understand server health and performance enables customers to create real-time alerts when server resources are running above or below a specified threshold level. Additionally, once these logs are analyzed, Logentries offers valuable visualizations of current or historical data to understand performance trends over time. As operations team look across all system performance metrics, the inclusion of server log data enables them to correlate resource usage metrics with important transactions within application files to identify problems or notable events.

The Logentries service features a unique pre-processing engine that collects and analyzes log files in real-time offering immediate alerting, visualizations, and tailing of the data. There is no complex query language required, making searching the data easy and intuitive with click-through navigation. For larger, auto-scaling, environments, Logentries provides a secure, business-class service that includes dynamic routing, unlimited archiving and filtering for private information.

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Logentries Integrates Server Metrics with Log Data for Real-time Visibility into System Performance and Capacity

Logentries introduced server monitoring metrics integrated with machine generated log data for real-time correlation.

Logentries is providing a single view of server metrics integrated with both client- and server- side log data as well as application level performance metrics to give a complete end-to-end view of system performance. The Logentries Windows and Linux agents actively track server CPU, server memory (total and active), disk I/O, and network activity, automatically creating server metrics logs that can be monitored, analyzed, and archived.

Many development and operations teams use multiple tools to understand what is happening across all servers, applications and end users. Logentries now provides users with a single view of server monitoring unified with existing monitoring and analytics using auto-generated log files. By utilizing logs to capture key server metrics, users can easily drill down to get a fine-grained view into issues or particular actions to understand root cause and deeper context around the events that matter to the business. The Logentries service applies its unique pre-processing engine to these server logs and enables users to easily build dashboards, custom tagging and alerting on the real-time data.

Some of the most valuable server metrics that can be collected, analyzed and alerted on using log data include:

-CPU usage

- Memory (active and available)

- Network activity (sent and received)

- Bandwidth usage

- Disk activity

- Data consumption per user

“As we work to connect our users to all of their data sources and systems using logs, we know that server monitoring is an important element to overall system health and performance,” said Trevor Parsons, Co-founder and Chief scientist at Logentries. “Bringing server monitoring data together with log-level metrics for system-wide monitoring provides users with an all-in-one viewpoint.”

Using logs to understand server health and performance enables customers to create real-time alerts when server resources are running above or below a specified threshold level. Additionally, once these logs are analyzed, Logentries offers valuable visualizations of current or historical data to understand performance trends over time. As operations team look across all system performance metrics, the inclusion of server log data enables them to correlate resource usage metrics with important transactions within application files to identify problems or notable events.

The Logentries service features a unique pre-processing engine that collects and analyzes log files in real-time offering immediate alerting, visualizations, and tailing of the data. There is no complex query language required, making searching the data easy and intuitive with click-through navigation. For larger, auto-scaling, environments, Logentries provides a secure, business-class service that includes dynamic routing, unlimited archiving and filtering for private information.

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...