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What's Slowing Down Your Cloud? (Hint: It's Not the App)

Eli Lahr
Leaseweb USA

Most teams go straight to the usual suspects when performance tanks in the cloud: app bugs, code regressions, maybe even the cloud provider itself. However, the real bottleneck is hiding in plain sight more often than not: the network.

Of course, apps are only as fast and secure as the connections between them in today's hybrid and multi-cloud environments. In other words, don't just audit your app if you're dealing with lag, downtime, or data sync issues. It's time to look under the hood at the infrastructure that's supposed to be holding it all together.

Cloud's Silent Performance Killer

Modern apps run across public cloud, private data centers, edge nodes, and even bare metal. They don't live in a single place anymore. That level of distribution demands a strong, purpose-built network strategy. Every transaction, real-time response, and secure data transfer will get bogged down without it.

Here's what happens when the network doesn't keep up:

  • Latency creeps in - which frustrates users and breaks real-time workflows
  • Data bottlenecks emerge - delaying analytics and AI pipelines
  • Security gaps appear - especially when traffic flows aren't encrypted or monitored

And yet, most cloud strategies barely mention the network until something goes wrong.

Why the Network Matters More Than You Think

The benefits are immediate and undeniable when your network is built to support the complexity of hybrid and multi-cloud:

  • Redundant paths, failover routes, and resilient infrastructure ensure business continuity and faster disaster recovery (DR) = Greater Reliability
  • Low-latency connectivity powers time-sensitive workloads, i.e., video streaming, gaming, and AI inferencing = Improved Performance
  • A modern network design can flex as you scale, whether that's across regions or providers = Increased Scalability
  • Advanced encryption, traffic segmentation, and monitoring protect data in motion, not just at rest = Enhanced Security

Real-World Impact, From AI to Gaming

Let's get a bit more specific.

One global enterprise running AI workloads needed to connect on-prem GPU clusters to cloud analytics tools without crushing performance. The solution? High-speed, direct network links that cut training times and eliminated sync delays.

A gaming company faced a different problem: players abandoning matches due to latency. Shifting to a bare metal setup with smart routing and dedicated bandwidth changed the game - literally.

These companies didn't rewrite code. They rethought their network.

The Next Wave Is Already Here

With today's AI-driven services and real-time data expectations, networks have to evolve fast. Traditional setups can't keep up. What's needed now are intelligent, adaptive, secure networks that align with how apps and users actually operate today.

Cloud isn't slowing down. But without the right network infrastructure, your ability to deliver on its promise will. If your cloud feels slow, glitchy, or unreliable, don't blame the app, or the platform. Blame the pipes. The truth is that your network could be the biggest factor holding you back.

So, before your next deployment or expansion, take a hard look at the infrastructure below the surface. Because the real difference between "it works" and "it scales beautifully" often comes down to how well your network is doing its job.

Eli Lahr is a Senior Solutions Engineer at Leaseweb USA

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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 ...

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 ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

What's Slowing Down Your Cloud? (Hint: It's Not the App)

Eli Lahr
Leaseweb USA

Most teams go straight to the usual suspects when performance tanks in the cloud: app bugs, code regressions, maybe even the cloud provider itself. However, the real bottleneck is hiding in plain sight more often than not: the network.

Of course, apps are only as fast and secure as the connections between them in today's hybrid and multi-cloud environments. In other words, don't just audit your app if you're dealing with lag, downtime, or data sync issues. It's time to look under the hood at the infrastructure that's supposed to be holding it all together.

Cloud's Silent Performance Killer

Modern apps run across public cloud, private data centers, edge nodes, and even bare metal. They don't live in a single place anymore. That level of distribution demands a strong, purpose-built network strategy. Every transaction, real-time response, and secure data transfer will get bogged down without it.

Here's what happens when the network doesn't keep up:

  • Latency creeps in - which frustrates users and breaks real-time workflows
  • Data bottlenecks emerge - delaying analytics and AI pipelines
  • Security gaps appear - especially when traffic flows aren't encrypted or monitored

And yet, most cloud strategies barely mention the network until something goes wrong.

Why the Network Matters More Than You Think

The benefits are immediate and undeniable when your network is built to support the complexity of hybrid and multi-cloud:

  • Redundant paths, failover routes, and resilient infrastructure ensure business continuity and faster disaster recovery (DR) = Greater Reliability
  • Low-latency connectivity powers time-sensitive workloads, i.e., video streaming, gaming, and AI inferencing = Improved Performance
  • A modern network design can flex as you scale, whether that's across regions or providers = Increased Scalability
  • Advanced encryption, traffic segmentation, and monitoring protect data in motion, not just at rest = Enhanced Security

Real-World Impact, From AI to Gaming

Let's get a bit more specific.

One global enterprise running AI workloads needed to connect on-prem GPU clusters to cloud analytics tools without crushing performance. The solution? High-speed, direct network links that cut training times and eliminated sync delays.

A gaming company faced a different problem: players abandoning matches due to latency. Shifting to a bare metal setup with smart routing and dedicated bandwidth changed the game - literally.

These companies didn't rewrite code. They rethought their network.

The Next Wave Is Already Here

With today's AI-driven services and real-time data expectations, networks have to evolve fast. Traditional setups can't keep up. What's needed now are intelligent, adaptive, secure networks that align with how apps and users actually operate today.

Cloud isn't slowing down. But without the right network infrastructure, your ability to deliver on its promise will. If your cloud feels slow, glitchy, or unreliable, don't blame the app, or the platform. Blame the pipes. The truth is that your network could be the biggest factor holding you back.

So, before your next deployment or expansion, take a hard look at the infrastructure below the surface. Because the real difference between "it works" and "it scales beautifully" often comes down to how well your network is doing its job.

Eli Lahr is a Senior Solutions Engineer at Leaseweb USA

Hot Topics

The Latest

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 ...

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 ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...