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Making the Case for Multi-CDN Delivery

Pete Mastin

Audiences have always dropped off when content delivery is slow. Studies of e-commerce and video have proven that the longer it takes for the screen to completely render, the more people close the browser and move on to another enterprise.

We don't need to restate the obvious. Information from Google, Amazon, Yahoo! and Mozilla prove over and over that Internet users stick with high performance sites and abandon slow sites.

DevOps needs to find ways to provide even more reliable, faster, application delivery. One massive innovation is the content delivery network (CDN). These services store the more static assets in high-performance data centers that are strategically located to get assets to the end client as speedily as possible.

These days, any service serious about delivering high quality to large audiences must either utilize a CDN service or implement its own solution. Here's why a multi-CDN strategy makes sense.

The Content Delivery Network: Nobody's Perfect

Nobody is perfect, and no one is perfect for everyone. CDNs strive for perfect uptime, but they can't accomplish it.

In addition, different geographic regions are better served by one CDN or another. As audience expectations increase and as services achieve a global reach, relying on one CDN creates weaknesses.

Distribute Responsibility

A multi-CDN configuration can help maximize content delivery or application performance even in the face of surging traffic – deploying assets on several CDNs at one time. This promotes the best possible performance, helps ensure 100 percent uptime, reduces costs, and leverages regionally dominant CDNs.

With a variety of options, the multiple CDNs need to be managed in a way that creates the best value from them. Different approaches to managing traffic among multiple CDNs exist, among them is: failover, round-robin, geographic and performance-based.

Scalability: Be Ready When Demand Spikes

A single CDN approach has its limits. Whether you deliver a premiere gaming experience, maintain a high-traffic ecommerce platform, or stream over-the-top (OTT) video, one CDN means accepting outages and performance limitations. Moving to a multi-CDN approach will create the foundation needed to provide a better experience for users.

By adopting a multi-CDN approach, businesses are better positioned to negotiate CDN rates. CDN providers, like all companies, want to maximize their value by resisting price reductions. It makes sense to use multiple CDNs. With multiple, high-performing CDNs available, content providers can deliver a premier user experience more cost-effectively.

Simply put, multiple CDNs perform better than a single CDN. This strategy improves performance, reduces costs and promotes ease of management.

Pete Mastin is a Product Evangelist at Cedexis.

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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 ...

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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 ...

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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 ...

Making the Case for Multi-CDN Delivery

Pete Mastin

Audiences have always dropped off when content delivery is slow. Studies of e-commerce and video have proven that the longer it takes for the screen to completely render, the more people close the browser and move on to another enterprise.

We don't need to restate the obvious. Information from Google, Amazon, Yahoo! and Mozilla prove over and over that Internet users stick with high performance sites and abandon slow sites.

DevOps needs to find ways to provide even more reliable, faster, application delivery. One massive innovation is the content delivery network (CDN). These services store the more static assets in high-performance data centers that are strategically located to get assets to the end client as speedily as possible.

These days, any service serious about delivering high quality to large audiences must either utilize a CDN service or implement its own solution. Here's why a multi-CDN strategy makes sense.

The Content Delivery Network: Nobody's Perfect

Nobody is perfect, and no one is perfect for everyone. CDNs strive for perfect uptime, but they can't accomplish it.

In addition, different geographic regions are better served by one CDN or another. As audience expectations increase and as services achieve a global reach, relying on one CDN creates weaknesses.

Distribute Responsibility

A multi-CDN configuration can help maximize content delivery or application performance even in the face of surging traffic – deploying assets on several CDNs at one time. This promotes the best possible performance, helps ensure 100 percent uptime, reduces costs, and leverages regionally dominant CDNs.

With a variety of options, the multiple CDNs need to be managed in a way that creates the best value from them. Different approaches to managing traffic among multiple CDNs exist, among them is: failover, round-robin, geographic and performance-based.

Scalability: Be Ready When Demand Spikes

A single CDN approach has its limits. Whether you deliver a premiere gaming experience, maintain a high-traffic ecommerce platform, or stream over-the-top (OTT) video, one CDN means accepting outages and performance limitations. Moving to a multi-CDN approach will create the foundation needed to provide a better experience for users.

By adopting a multi-CDN approach, businesses are better positioned to negotiate CDN rates. CDN providers, like all companies, want to maximize their value by resisting price reductions. It makes sense to use multiple CDNs. With multiple, high-performing CDNs available, content providers can deliver a premier user experience more cost-effectively.

Simply put, multiple CDNs perform better than a single CDN. This strategy improves performance, reduces costs and promotes ease of management.

Pete Mastin is a Product Evangelist at Cedexis.

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 ...