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LogicMonitor Introduces Kubernetes Container Monitoring and LM Service Insight

LogicMonitor introduced two innovations to help engineers monitor their dynamic microservices and containerized applications: Kubernetes container monitoring and LM Service Insight.

According to a Cloud Native Computing Foundation survey, 40 percent of enterprise companies are running Kubernetes in production. These containers create a dynamic environment that is difficult to monitor. LogicMonitor addresses these challenges by combining robust Kubernetes monitoring and long-term data retention into one integrated platform.

LogicMonitor’s event-based Kubernetes monitoring:

- Eliminates the need to have an agent on every node

- Automatically adds and removes cluster resources from monitoring

- Offers comprehensive performance and health metrics at both the cluster and application level

- Provides insight on underutilized resources (including CPU and memory) for maximum optimization

“If you want to break up a monolithic service into microservices orchestrated with Kubernetes, you shouldn’t have to stop and make sure your monitoring solution can keep up. You should never have to sacrifice business vision because of infrastructure challenges,” said Steve Francis, Founder and Chief Evangelist at LogicMonitor.

LM Service Insight provides service-oriented monitoring, enabling the dynamic grouping of resources that support a common application, service or cluster together into one logical group, while still providing visibility into the underlying resources. LM Service Insight™ enables DevOps teams to ensure better availability and performance of services and applications, view historical service performance and reduce alert noise. Additionally, service topology is automatically generated and presented alongside monitoring and alerting data.

Organizations can use Kubernetes container monitoring and LM Service Insight together to aggregate data across ephemeral containers and better understand overall application performance over time.

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LogicMonitor Introduces Kubernetes Container Monitoring and LM Service Insight

LogicMonitor introduced two innovations to help engineers monitor their dynamic microservices and containerized applications: Kubernetes container monitoring and LM Service Insight.

According to a Cloud Native Computing Foundation survey, 40 percent of enterprise companies are running Kubernetes in production. These containers create a dynamic environment that is difficult to monitor. LogicMonitor addresses these challenges by combining robust Kubernetes monitoring and long-term data retention into one integrated platform.

LogicMonitor’s event-based Kubernetes monitoring:

- Eliminates the need to have an agent on every node

- Automatically adds and removes cluster resources from monitoring

- Offers comprehensive performance and health metrics at both the cluster and application level

- Provides insight on underutilized resources (including CPU and memory) for maximum optimization

“If you want to break up a monolithic service into microservices orchestrated with Kubernetes, you shouldn’t have to stop and make sure your monitoring solution can keep up. You should never have to sacrifice business vision because of infrastructure challenges,” said Steve Francis, Founder and Chief Evangelist at LogicMonitor.

LM Service Insight provides service-oriented monitoring, enabling the dynamic grouping of resources that support a common application, service or cluster together into one logical group, while still providing visibility into the underlying resources. LM Service Insight™ enables DevOps teams to ensure better availability and performance of services and applications, view historical service performance and reduce alert noise. Additionally, service topology is automatically generated and presented alongside monitoring and alerting data.

Organizations can use Kubernetes container monitoring and LM Service Insight together to aggregate data across ephemeral containers and better understand overall application performance over time.

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Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

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