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Do You Need Multi-CDN Monitoring? Here's What You Need to Consider - Part 1

Tony Falco
VP Marketing
Hydrolix

Imagine you're the CEO of a major retailer on Black Friday. How your e-commerce site performs on this one day could determine whether your entire year is a success or failure.

Or maybe you're the CTO of a broadcasting company with the contract to air the most-watched championship gridiron football game in history, or maybe the Olympics, with tens of millions of users streaming through a wide range of end devices, content delivery networks (CDNs) and internet service providers (ISPs). Now is not the time for a denial of service attack that prevents millions from watching the game.

Or perhaps you're co-founder of a gaming company that just launched its first multiplayer game, and it has gone viral, with online demand surpassing your wildest dreams. What will it do to your reputation and customer experience if you are unable to scale to meet the demand?

Whether these scenarios describe your reality, fantasies or nightmares, you undoubtedly appreciate the high stakes involved and how incredibly important it is for these companies to provide high-quality, low latency content delivery and to scale to an extreme degree to handle huge spikes in traffic.

There are two key ingredients to the secret sauce that helps enterprises accomplish such amazing feats: multi-CDN and multi-CDN monitoring. In this two part series, I'll provide a short primer on both of those topics.

What is Multi-CDN?

CDNs consist of geographically distributed data centers with servers that cache and serve content close to end users to reduce latency and improve load times. Each data center is strategically placed so that digital signals can rapidly travel from one "point of presence" (PoP) to the next, getting the digital signal to the viewer as fast as possible. Traditional CDNs typically use physical servers, but many CDNs are now using an entirely digital network architecture, commonly referred to as Edge Computing.

Multi-CDN refers to the strategy of utilizing multiple CDNs (e.g., Akamai, Cloudflare, CloudFront, Fastly and Gcore) to deliver digital content across the internet. Since no one CDN completely covers the world like a blanket, companies can leverage multiple CDNs to increase their PoPs and have more widespread coverage to their customers. The multi-CDN approach not only optimizes content delivery and delivers improved performance across different regions but also enables scalability during peak times and ensures redundancy in the event something goes wrong with a PoP or a single CDN's entire network.

CDNs are a critical infrastructure component, particularly for businesses that require fast and reliable content delivery, such as streaming services, e-commerce platforms, and global enterprises. Multi-CDN takes this a step further by combining the strengths of several CDN providers, offering benefits like enhanced availability, load balancing, and improved user experiences on a global scale.

Why Would a Company Take the Multi-CDN Approach?

A multi-CDN approach brings with it a host of technical and operational challenges. So why would an organization do it?

1. Redundancy and Reliability: Relying on a single CDN provider exposes application performance to the same risks that relying on a single cloud provider does. If your only CDN provider experiences an outage or performance degradation in a specific region, it likely will impact the user experience. A multi-CDN approach reduces this risk by providing multiple pathways for content delivery.

2. Performance Optimization: Different CDNs have different strengths in different regions. By leveraging multiple CDNs, companies can direct traffic to the CDN that performs best in a given geographic area at any given time.

3. Scalability: During peak traffic times — think major online sales events or live streaming of popular content — a single CDN might struggle under the load. Multi-CDN allows companies to distribute traffic, essentially load balancing the demand.

4. Cost Management: By using multiple CDNs, teams can optimize costs by routing traffic based on pricing models, bandwidth costs, and performance metrics, ensuring they get the best available value. Using multiple CDNs for cost management is a well documented trend (e.g., this article), with some leading CDNs citing the trend as a contributing factor for declining delivery revenues for the past three years.

Users of Multi-CDN

It's probably evident at this point that multi-CDN is particularly popular among large enterprises and organizations that require robust, reliable, and scalable content delivery solutions. The kinds of applications that fit this general description include:

Streaming Services: Companies like Netflix, Disney+, and Paramount use multi-CDN to deliver video content to millions of users worldwide. By employing multiple CDNs, they ensure that their content is always available and delivered with minimal buffering, even during peak viewing times.

Gaming: Leading gaming companies use multi-CDN to create real-time, instantly responsive experiences for more than 1 billion online gamers worldwide. In gaming, low latency (100 milliseconds or less) is one of the most important aspects of the user experience.

E-commerce Platforms: During high-traffic events like Black Friday, massive surges in traffic can compromise the user experience — a potentially catastrophic problem for online retailers. Just a delay of a few seconds can lead to customer dissatisfaction, lower conversion rates, and lost revenue. Multi-CDN helps distribute traffic, reducing server loads and keeping response times within the desired service level objectives.

Other Global Enterprises: Businesses operating on a global scale use multi-CDN to ensure consistent content delivery and user experience across different markets.

Challenges of Multi-CDN

Implementing and operating multi-CDN isn't trivial. First and foremost, managing multiple CDNs demands a sophisticated orchestration capability to ensure you can manage service requests and content delivery across different providers with different architectures, SLAs, APIs, etc. This involves complex routing logic, real-time traffic monitoring, and dynamic decision-making to autonomously switch among CDNs as needed.

This brings up what is probably the biggest single barrier: data standardization. Or, more accurately, the lack of data standardization. Each CDN likely will have different logging formats and naming conventions, making it challenging to aggregate and analyze data. Companies must standardize log data across CDNs to gain meaningful insights and monitor performance effectively. Organizations that run multi-CDN will tell you this can be a big lift.

Cost management is another challenge. While it's true that multi-CDN can reduce costs by placing loads on the CDNs that are most cost effective for a given set of conditions, it can also lead to higher expenses if the various contract commitments are not managed carefully. Multi-CDN can also indirectly increase costs by requiring more server-related resources, introducing software or hardware issues, and increasing the need for oversight in general.

And of course, with any architecture that spans multiple environments, security and all the compliance it demands has to be addressed. But that's a topic for its own article.

How Multi-CDN Monitoring Can Address These Challenges

In Part 1 of this series I've defined multi-CDN and explored the benefits and challenges it brings to organizations that rely on super-fast and reliable content delivery to their end users. In Part 2, we'll look at how monitoring can address the challenges of multi-CDN and help organizations capitalize on this valuable approach.

Read: <a href="https://www.apmdigest.com/do-you-need-multi-cdn-monitoring-heres-what-you-need-to-consider-part-2" target="_blank">Do You Need Multi-CDN Monitoring? Here's What You Need to Consider - Part 2</a>

Tony Falco is VP Marketing at Hydrolix

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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 ...

Do You Need Multi-CDN Monitoring? Here's What You Need to Consider - Part 1

Tony Falco
VP Marketing
Hydrolix

Imagine you're the CEO of a major retailer on Black Friday. How your e-commerce site performs on this one day could determine whether your entire year is a success or failure.

Or maybe you're the CTO of a broadcasting company with the contract to air the most-watched championship gridiron football game in history, or maybe the Olympics, with tens of millions of users streaming through a wide range of end devices, content delivery networks (CDNs) and internet service providers (ISPs). Now is not the time for a denial of service attack that prevents millions from watching the game.

Or perhaps you're co-founder of a gaming company that just launched its first multiplayer game, and it has gone viral, with online demand surpassing your wildest dreams. What will it do to your reputation and customer experience if you are unable to scale to meet the demand?

Whether these scenarios describe your reality, fantasies or nightmares, you undoubtedly appreciate the high stakes involved and how incredibly important it is for these companies to provide high-quality, low latency content delivery and to scale to an extreme degree to handle huge spikes in traffic.

There are two key ingredients to the secret sauce that helps enterprises accomplish such amazing feats: multi-CDN and multi-CDN monitoring. In this two part series, I'll provide a short primer on both of those topics.

What is Multi-CDN?

CDNs consist of geographically distributed data centers with servers that cache and serve content close to end users to reduce latency and improve load times. Each data center is strategically placed so that digital signals can rapidly travel from one "point of presence" (PoP) to the next, getting the digital signal to the viewer as fast as possible. Traditional CDNs typically use physical servers, but many CDNs are now using an entirely digital network architecture, commonly referred to as Edge Computing.

Multi-CDN refers to the strategy of utilizing multiple CDNs (e.g., Akamai, Cloudflare, CloudFront, Fastly and Gcore) to deliver digital content across the internet. Since no one CDN completely covers the world like a blanket, companies can leverage multiple CDNs to increase their PoPs and have more widespread coverage to their customers. The multi-CDN approach not only optimizes content delivery and delivers improved performance across different regions but also enables scalability during peak times and ensures redundancy in the event something goes wrong with a PoP or a single CDN's entire network.

CDNs are a critical infrastructure component, particularly for businesses that require fast and reliable content delivery, such as streaming services, e-commerce platforms, and global enterprises. Multi-CDN takes this a step further by combining the strengths of several CDN providers, offering benefits like enhanced availability, load balancing, and improved user experiences on a global scale.

Why Would a Company Take the Multi-CDN Approach?

A multi-CDN approach brings with it a host of technical and operational challenges. So why would an organization do it?

1. Redundancy and Reliability: Relying on a single CDN provider exposes application performance to the same risks that relying on a single cloud provider does. If your only CDN provider experiences an outage or performance degradation in a specific region, it likely will impact the user experience. A multi-CDN approach reduces this risk by providing multiple pathways for content delivery.

2. Performance Optimization: Different CDNs have different strengths in different regions. By leveraging multiple CDNs, companies can direct traffic to the CDN that performs best in a given geographic area at any given time.

3. Scalability: During peak traffic times — think major online sales events or live streaming of popular content — a single CDN might struggle under the load. Multi-CDN allows companies to distribute traffic, essentially load balancing the demand.

4. Cost Management: By using multiple CDNs, teams can optimize costs by routing traffic based on pricing models, bandwidth costs, and performance metrics, ensuring they get the best available value. Using multiple CDNs for cost management is a well documented trend (e.g., this article), with some leading CDNs citing the trend as a contributing factor for declining delivery revenues for the past three years.

Users of Multi-CDN

It's probably evident at this point that multi-CDN is particularly popular among large enterprises and organizations that require robust, reliable, and scalable content delivery solutions. The kinds of applications that fit this general description include:

Streaming Services: Companies like Netflix, Disney+, and Paramount use multi-CDN to deliver video content to millions of users worldwide. By employing multiple CDNs, they ensure that their content is always available and delivered with minimal buffering, even during peak viewing times.

Gaming: Leading gaming companies use multi-CDN to create real-time, instantly responsive experiences for more than 1 billion online gamers worldwide. In gaming, low latency (100 milliseconds or less) is one of the most important aspects of the user experience.

E-commerce Platforms: During high-traffic events like Black Friday, massive surges in traffic can compromise the user experience — a potentially catastrophic problem for online retailers. Just a delay of a few seconds can lead to customer dissatisfaction, lower conversion rates, and lost revenue. Multi-CDN helps distribute traffic, reducing server loads and keeping response times within the desired service level objectives.

Other Global Enterprises: Businesses operating on a global scale use multi-CDN to ensure consistent content delivery and user experience across different markets.

Challenges of Multi-CDN

Implementing and operating multi-CDN isn't trivial. First and foremost, managing multiple CDNs demands a sophisticated orchestration capability to ensure you can manage service requests and content delivery across different providers with different architectures, SLAs, APIs, etc. This involves complex routing logic, real-time traffic monitoring, and dynamic decision-making to autonomously switch among CDNs as needed.

This brings up what is probably the biggest single barrier: data standardization. Or, more accurately, the lack of data standardization. Each CDN likely will have different logging formats and naming conventions, making it challenging to aggregate and analyze data. Companies must standardize log data across CDNs to gain meaningful insights and monitor performance effectively. Organizations that run multi-CDN will tell you this can be a big lift.

Cost management is another challenge. While it's true that multi-CDN can reduce costs by placing loads on the CDNs that are most cost effective for a given set of conditions, it can also lead to higher expenses if the various contract commitments are not managed carefully. Multi-CDN can also indirectly increase costs by requiring more server-related resources, introducing software or hardware issues, and increasing the need for oversight in general.

And of course, with any architecture that spans multiple environments, security and all the compliance it demands has to be addressed. But that's a topic for its own article.

How Multi-CDN Monitoring Can Address These Challenges

In Part 1 of this series I've defined multi-CDN and explored the benefits and challenges it brings to organizations that rely on super-fast and reliable content delivery to their end users. In Part 2, we'll look at how monitoring can address the challenges of multi-CDN and help organizations capitalize on this valuable approach.

Read: <a href="https://www.apmdigest.com/do-you-need-multi-cdn-monitoring-heres-what-you-need-to-consider-part-2" target="_blank">Do You Need Multi-CDN Monitoring? Here's What You Need to Consider - Part 2</a>

Tony Falco is VP Marketing at Hydrolix

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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 ...