Skip to main content

The Struggle Is Real: Multi-Cloud Network Monitoring Isn't Always Easy

Robert Gates
Enterprise Management Associates

Everyone has visibility into their multi-cloud networking environment, but only some are happy with what they see.

Unfortunately, this continues a trend.

According to EMA's latest research, Multi-Cloud Networking: Connecting and Securing the Future, most network teams have some end-to-end visibility across their multi-cloud networks. Still, only 23.6% are fully satisfied with their multi-cloud network monitoring and troubleshooting capabilities.

EMA WEBINAR TOMORROW JAN 31: MULTI-CLOUD NETWORKING.

More importantly, EMA found that overall multi-cloud networking success correlates strongly with monitoring and troubleshooting satisfaction.

Better visibility and control into multi-cloud networks is an area that EMA predicts will be a significant focus of enterprise network teams during the next few years. Public cloud and multi-cloud adoption are the primary drivers of enterprise network operations strategies, and pain points such as monitoring and improved visibility will continue to be a focus.

We typically ask IT pros in our surveys about their satisfaction with various components of their network environment. What is often most alarming is when the people closest to an operation are the least satisfied. In this case, members of network engineering teams were the least satisfied with their multi-cloud networking monitoring and troubleshooting capabilities.


In a recent conversation with EMA, a network architect at a $15 billion retailer highlighted a need for more visibility to monitor the cloud network as his top challenge. "Once [traffic] goes into Azure, we don't have much visibility into what it does. Knock on wood. For the most part, it works. But it doesn't always work."

Network teams are generally less satisfied with multi-cloud network visibility partly because of the deep, comprehensive, and complete visibility they are accustomed to having with their on-premises environments.

Take, for example, a senior network engineer at a large university hospital system and medical school. He told us he has visibility into everything on-premises but called his cloud environment "opaque." He said, "We don't know if something is wrong, and we don't get alerts if a region is having a problem."

His team can see that a cloud link is up, but that is about all. He points at a poor integration between on-premises networks and the cloud as the source of trouble and seeks visibility into cloud traffic and interfaces in the cloud. "It's possible [to get this visibility], but it needs to be done from the beginning," he said.

Another area for improvement is collaboration gaps with teams and tools that offer better monitoring and visibility into the multi-cloud network.

The senior network engineer also told us he finds working with his cloud teams on networking and security issues difficult. He finds them reluctant to give the networking team visibility into their environment and doesn't trust them to do what's right.

These struggles have led enterprises to acquire new third-party monitoring tools. While that helps to improve observability across the multi-cloud network, others are simply trying to adapt their existing tools. Others use the native monitoring capabilities of their multi-cloud networking providers in hopes of closing their visibility gaps. Network teams are adopting multiple approaches to improve visibility into multi-cloud networks, but EMA research demonstrates that they have more work to do.

To hear more insights from EMA's new "Multi-Cloud Networking" research report, please join the webinar on Tuesday, Jan. 31, at 11 a.m. Pacific/2 p.m. Eastern.

Robert Gates is Senior Analyst, Network Infrastructure and Operations, Enterprise Management Associates (EMA)

Hot Topics

The Latest

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

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 ...

The Struggle Is Real: Multi-Cloud Network Monitoring Isn't Always Easy

Robert Gates
Enterprise Management Associates

Everyone has visibility into their multi-cloud networking environment, but only some are happy with what they see.

Unfortunately, this continues a trend.

According to EMA's latest research, Multi-Cloud Networking: Connecting and Securing the Future, most network teams have some end-to-end visibility across their multi-cloud networks. Still, only 23.6% are fully satisfied with their multi-cloud network monitoring and troubleshooting capabilities.

EMA WEBINAR TOMORROW JAN 31: MULTI-CLOUD NETWORKING.

More importantly, EMA found that overall multi-cloud networking success correlates strongly with monitoring and troubleshooting satisfaction.

Better visibility and control into multi-cloud networks is an area that EMA predicts will be a significant focus of enterprise network teams during the next few years. Public cloud and multi-cloud adoption are the primary drivers of enterprise network operations strategies, and pain points such as monitoring and improved visibility will continue to be a focus.

We typically ask IT pros in our surveys about their satisfaction with various components of their network environment. What is often most alarming is when the people closest to an operation are the least satisfied. In this case, members of network engineering teams were the least satisfied with their multi-cloud networking monitoring and troubleshooting capabilities.


In a recent conversation with EMA, a network architect at a $15 billion retailer highlighted a need for more visibility to monitor the cloud network as his top challenge. "Once [traffic] goes into Azure, we don't have much visibility into what it does. Knock on wood. For the most part, it works. But it doesn't always work."

Network teams are generally less satisfied with multi-cloud network visibility partly because of the deep, comprehensive, and complete visibility they are accustomed to having with their on-premises environments.

Take, for example, a senior network engineer at a large university hospital system and medical school. He told us he has visibility into everything on-premises but called his cloud environment "opaque." He said, "We don't know if something is wrong, and we don't get alerts if a region is having a problem."

His team can see that a cloud link is up, but that is about all. He points at a poor integration between on-premises networks and the cloud as the source of trouble and seeks visibility into cloud traffic and interfaces in the cloud. "It's possible [to get this visibility], but it needs to be done from the beginning," he said.

Another area for improvement is collaboration gaps with teams and tools that offer better monitoring and visibility into the multi-cloud network.

The senior network engineer also told us he finds working with his cloud teams on networking and security issues difficult. He finds them reluctant to give the networking team visibility into their environment and doesn't trust them to do what's right.

These struggles have led enterprises to acquire new third-party monitoring tools. While that helps to improve observability across the multi-cloud network, others are simply trying to adapt their existing tools. Others use the native monitoring capabilities of their multi-cloud networking providers in hopes of closing their visibility gaps. Network teams are adopting multiple approaches to improve visibility into multi-cloud networks, but EMA research demonstrates that they have more work to do.

To hear more insights from EMA's new "Multi-Cloud Networking" research report, please join the webinar on Tuesday, Jan. 31, at 11 a.m. Pacific/2 p.m. Eastern.

Robert Gates is Senior Analyst, Network Infrastructure and Operations, Enterprise Management Associates (EMA)

Hot Topics

The Latest

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

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 ...