Skip to main content

The Need for Speed: How Video Load Time Affects Ad Delivery

Shlomi Gian

Ask any mobile app developer, and they'll tell you that one of the greatest challenges in monetizing their apps through video ads isn't finding the right demand or knowing when to run the videos; it's figuring out how to present video ads without slowing down their apps.
 
I'm sure every developer at some point has done a Google search for something like, "Which ad networks are optimized for mobile apps?" or "Which mobile ad network is the fastest?"
 
If the primary revenue you get from your app is ad generated, speed is hardly inconsequential.
 

The Developer Challenge

 
One company explained their ad situation like this: After a user completes a level, the user either goes into their store and browses around for a few minutes or goes to the next level within a few seconds. The developer's goal is to show a video ad right before the next level starts, but they've found themselves in a pickle. Most developers will cache their videos a few minutes before showing them, and the expiration is around 15 minutes. If the developer starts the video download as soon as the user finishes the current level, and that user then browses the store for a few minutes, the ad could expire before it can be shown, i.e., right before the next level.
 
On the flipside, if the developer starts the video download shortly after the user finishes the current level, and that user skips the store to go immediately to the next level, then the video has not downloaded yet and cannot be shown to the user before the next level. In this case, it would be ideal for the video to be delivered consistently within a set amount of time. But since ad retrieving timeout is around 300-400 milliseconds, even when you cache the video, slow ad retrieval leads directly to missing revenue.
 
Net net, while video pre-caching is a valid technique, it comes with a cost, and often times expired content would still result in real-time video downloads that usually are not fast enough.
 

Compounding the Challenge: Painful Disconnects

 
Connection drops are another issue that affect video ads. Disconnects happen all the time, especially when users are on the move, such as when they're commuting or walking around a busy city and their connections must transfer to a different network type. If a disconnect happens during an API call to an ad network or while downloading a large video asset, then that ad will fail to load.

When disconnects happen, the end user typically doesn't realize that the fault lies with the networks. Instead, they usually assign blame to the developer and the app itself. Many users will get so frustrated with a dropped connection that they won't even put in the brief time it takes to reload an app.
 
Some networks are faster and more reliable than others. Verizon and T-Mobile top the US for speed, and lag slightly behind AT&T in disconnects. The problem of disconnects is particularly acute in developing countries that don't have reliable cellular and WiFi networks. But it can infest developed countries as well. According to our benchmarks from February of this year, apps users in Russia, Indonesia, Germany and Brazil all suffer from more than 10% disconnects on average, with disconnect rates as high as 30 percent on 2G networks.
 

Examples of Various Ad Download Times

 
The charts below show ads within a popular news app. The top chart shows video ads delivered by SuperSonic and the bottom chart shows those delivered by Vungle. The x axis shows the amount of time a video took to download in milliseconds; the y axis shows the percentage of the total transfers that finished within that bucket of milliseconds. What we can see overall is that ads aren't delivered within a consistent amount of time; they're usually delivered anywhere from half a second to two seconds.





 
What this says is that if you use a mediation ad network, you should expect your ads to load anywhere from less than one second up to four seconds or longer. That doesn't help you to engineer your app if it has time constraints like the example above.

So what do you do? Well, if you choose to just use one ad network, you should be able to engineer your app/game to that specific ad network based on how long that specific network loads ads. Or, you could continue to use an ad mediator with the understanding that some percent of your users will not see the ad due to it loading too fast or too slow.
 
Bottom line is, attention shifts when something takes longer to load, and time is money. So keep this in mind with your in-app video ads.

Hot Topics

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 ...

The Need for Speed: How Video Load Time Affects Ad Delivery

Shlomi Gian

Ask any mobile app developer, and they'll tell you that one of the greatest challenges in monetizing their apps through video ads isn't finding the right demand or knowing when to run the videos; it's figuring out how to present video ads without slowing down their apps.
 
I'm sure every developer at some point has done a Google search for something like, "Which ad networks are optimized for mobile apps?" or "Which mobile ad network is the fastest?"
 
If the primary revenue you get from your app is ad generated, speed is hardly inconsequential.
 

The Developer Challenge

 
One company explained their ad situation like this: After a user completes a level, the user either goes into their store and browses around for a few minutes or goes to the next level within a few seconds. The developer's goal is to show a video ad right before the next level starts, but they've found themselves in a pickle. Most developers will cache their videos a few minutes before showing them, and the expiration is around 15 minutes. If the developer starts the video download as soon as the user finishes the current level, and that user then browses the store for a few minutes, the ad could expire before it can be shown, i.e., right before the next level.
 
On the flipside, if the developer starts the video download shortly after the user finishes the current level, and that user skips the store to go immediately to the next level, then the video has not downloaded yet and cannot be shown to the user before the next level. In this case, it would be ideal for the video to be delivered consistently within a set amount of time. But since ad retrieving timeout is around 300-400 milliseconds, even when you cache the video, slow ad retrieval leads directly to missing revenue.
 
Net net, while video pre-caching is a valid technique, it comes with a cost, and often times expired content would still result in real-time video downloads that usually are not fast enough.
 

Compounding the Challenge: Painful Disconnects

 
Connection drops are another issue that affect video ads. Disconnects happen all the time, especially when users are on the move, such as when they're commuting or walking around a busy city and their connections must transfer to a different network type. If a disconnect happens during an API call to an ad network or while downloading a large video asset, then that ad will fail to load.

When disconnects happen, the end user typically doesn't realize that the fault lies with the networks. Instead, they usually assign blame to the developer and the app itself. Many users will get so frustrated with a dropped connection that they won't even put in the brief time it takes to reload an app.
 
Some networks are faster and more reliable than others. Verizon and T-Mobile top the US for speed, and lag slightly behind AT&T in disconnects. The problem of disconnects is particularly acute in developing countries that don't have reliable cellular and WiFi networks. But it can infest developed countries as well. According to our benchmarks from February of this year, apps users in Russia, Indonesia, Germany and Brazil all suffer from more than 10% disconnects on average, with disconnect rates as high as 30 percent on 2G networks.
 

Examples of Various Ad Download Times

 
The charts below show ads within a popular news app. The top chart shows video ads delivered by SuperSonic and the bottom chart shows those delivered by Vungle. The x axis shows the amount of time a video took to download in milliseconds; the y axis shows the percentage of the total transfers that finished within that bucket of milliseconds. What we can see overall is that ads aren't delivered within a consistent amount of time; they're usually delivered anywhere from half a second to two seconds.





 
What this says is that if you use a mediation ad network, you should expect your ads to load anywhere from less than one second up to four seconds or longer. That doesn't help you to engineer your app if it has time constraints like the example above.

So what do you do? Well, if you choose to just use one ad network, you should be able to engineer your app/game to that specific ad network based on how long that specific network loads ads. Or, you could continue to use an ad mediator with the understanding that some percent of your users will not see the ad due to it loading too fast or too slow.
 
Bottom line is, attention shifts when something takes longer to load, and time is money. So keep this in mind with your in-app video ads.

Hot Topics

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 ...