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LogicMonitor Expands Monitoring for Google Cloud

LogicMonitor launched expanded capabilities to LM Cloud to support Google Cloud Platform. Companies that have invested in multi-cloud deployments can now comprehensively view the health, performance, usage and spend of all public cloud investments, including Google Cloud Platform (GCP), in a single view using LogicMonitor’s enhanced LM Cloud solution.

GCP joins Amazon Web Services (AWS) and Microsoft Azure as cloud platform technologies supported by LogicMonitor’s hybrid monitoring platform.

“Enterprise workloads are moving to the cloud, and more than half now use more than one type of public cloud service. Using multiple cloud providers can significantly decrease risk and costs while increasing security and autonomy,” said Steve Francis, Founder and Chief Evangelist at LogicMonitor. “Evolving our multi-cloud monitoring to include GCP gives companies the single-pane-of-glass view that enables them to optimize technical performance and financial management of their cloud resources.”
Multi-Cloud Deployments Demand a Tailored Market Solution

“LogicMonitor developed LM Cloud in 2018 to help customers with data in multiple clouds. Previously, these users were forced to monitor each of their cloud environments individually or pick a monitoring tool with limited multi-cloud support”, stated Sarah Terry, Senior Product Manager, LogicMonitor. “The need for a unified view across resources and services increases in multi-cloud environments. LM Cloud’s unified view with comprehensive cloud performance and availability monitoring eliminates downtime for multi-cloud deployments.”

The difference in cloud implementations goes beyond physical infrastructure components as cloud providers vary in characteristics, functionality, pricing models and policies. By supporting the unique needs of each cloud platform, LM Cloud makes managing multi-cloud environments easier:

- Automation: LM Cloud auto-discovers all cloud resources across providers and automatically monitors the data. The platform produces dashboards based on cloud management best practices to visualize the overall health of the cloud environments.

- Comprehensive Metrics: LM Cloud goes beyond simple cloud provider metrics by monitoring application and operating system-level metrics. Pre-configured alert thresholds ensure that meaningful alerts are generated from the onset.

- Optimization: In addition to performance, LM Cloud monitors cloud provider service availability and cloud spend so that users can control costs across multiple cloud providers.

GCP monitoring is available now with all LogicMonitor LM Cloud licenses.

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LogicMonitor Expands Monitoring for Google Cloud

LogicMonitor launched expanded capabilities to LM Cloud to support Google Cloud Platform. Companies that have invested in multi-cloud deployments can now comprehensively view the health, performance, usage and spend of all public cloud investments, including Google Cloud Platform (GCP), in a single view using LogicMonitor’s enhanced LM Cloud solution.

GCP joins Amazon Web Services (AWS) and Microsoft Azure as cloud platform technologies supported by LogicMonitor’s hybrid monitoring platform.

“Enterprise workloads are moving to the cloud, and more than half now use more than one type of public cloud service. Using multiple cloud providers can significantly decrease risk and costs while increasing security and autonomy,” said Steve Francis, Founder and Chief Evangelist at LogicMonitor. “Evolving our multi-cloud monitoring to include GCP gives companies the single-pane-of-glass view that enables them to optimize technical performance and financial management of their cloud resources.”
Multi-Cloud Deployments Demand a Tailored Market Solution

“LogicMonitor developed LM Cloud in 2018 to help customers with data in multiple clouds. Previously, these users were forced to monitor each of their cloud environments individually or pick a monitoring tool with limited multi-cloud support”, stated Sarah Terry, Senior Product Manager, LogicMonitor. “The need for a unified view across resources and services increases in multi-cloud environments. LM Cloud’s unified view with comprehensive cloud performance and availability monitoring eliminates downtime for multi-cloud deployments.”

The difference in cloud implementations goes beyond physical infrastructure components as cloud providers vary in characteristics, functionality, pricing models and policies. By supporting the unique needs of each cloud platform, LM Cloud makes managing multi-cloud environments easier:

- Automation: LM Cloud auto-discovers all cloud resources across providers and automatically monitors the data. The platform produces dashboards based on cloud management best practices to visualize the overall health of the cloud environments.

- Comprehensive Metrics: LM Cloud goes beyond simple cloud provider metrics by monitoring application and operating system-level metrics. Pre-configured alert thresholds ensure that meaningful alerts are generated from the onset.

- Optimization: In addition to performance, LM Cloud monitors cloud provider service availability and cloud spend so that users can control costs across multiple cloud providers.

GCP monitoring is available now with all LogicMonitor LM Cloud licenses.

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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 ...