‍

For the past ten years, streaming platforms have been focused on achieving scale. With audiences growing to be larger, more global, and more engaged than ever, major live events now draw millions of concurrent viewers, and on-demand content drives hours of daily consumption. From a surface perspective, this level of expansion should be viewed as a definitive success. More viewers should logically translate to increased revenue, stronger engagement, and a larger market share. However, the economics of streaming are more complex than those of many other digital businesses because they lack the benefit of near-zero marginal costs. When it comes to streaming, every new viewer escalates infrastructure demands, from compute and content delivery to bandwidth. As such, streaming costs increase alongside usage, often at a rate that outpaces audience growth.

‍

Although advancements in streaming scaling consistently propel platforms forward, the associated cost structure struggles to maintain efficiency. The core issue is now understanding why scaling traffic and scaling streaming costs don’t grow in the same direction.

‍

Why streaming scaling is driven by peaks, not averages

‍

While subscriber numbers and overall viewing hours suggest steady audience growth and platform expansion in streaming, the underlying traffic tells a different story. Streaming demand is far from linear; it is fundamentally characterized by spikes, surges, and moments of extreme concurrency. Live events are a clear example. 

‍

Streaming platforms must design their systems to handle worst-case scenarios, often defined by unpredictable but massive traffic spikes. A major global event, like a sports final, can cause traffic levels to surge 10x or more above baseline usage, sometimes within minutes. This means platforms scale not for average, but for these extreme, difficult-to-predict peak moments.

‍

Scaling traffic isn't the only factor; scaling costs are also driven by more data per viewer. Factors like multi-device usage, higher resolutions, and demands for lower latency significantly increase the volume of data that needs real-time delivery. As a result, there is a direct correlation between improved quality and increased video delivery costs: a user streaming in 4K, for instance, requires considerably more bandwidth than a user watching in standard definition. 

‍

Why do streaming costs scale inefficiently?

‍

Traditional streaming delivery models can be inefficient regarding cost, as they rely heavily on CDNs and bandwidth-based pricing. This means streaming costs are directly proportional to usage. Essentially, as viewing figures, streaming duration and quality increase, so does the bill. But the bigger issue may be how platforms prepare for such demand. 

‍

Since traffic is determined by peak demand, infrastructure must be built to accommodate the worst-case scenario. This often results in overprovisioning, adding capacity to handle traffic surges that oftentimes remains underutilized. While it is an expensive compromise, most platforms accept it to safeguard against outages, poor QoE and the risk of customer churn.

‍

Several factors exacerbate streaming infrastructure costs even further, including the use of multi-CDN strategies, variations in regional pricing, and inherent network inefficiencies. In regions with underdeveloped infrastructure, delivery can become substantially more expensive. The result is a model where increased traffic does not translate to improved cost efficiency. Instead, greater engagement directly drives higher expenditure, making OTT cost optimization an increasingly critical focus for streaming platforms.

‍

Decoupling audience growth from infrastructure spend

‍

Instead of relying on the traditional, expensive model of simply adding more infrastructure, platforms are now prioritizing efficiency to manage streaming costs while upholding performance. This approach moves beyond mere brute-force capacity to smarter methods. The focus is on real-time traffic optimization: intelligently routing streams, avoiding network congestion, and strategically lessening the reliance on costly CDN capacity. 

‍

By making delivery decisions based on actual network conditions, platforms gain more control over the cost of their streaming infrastructure. This is where solutions like Edge Intelligence can make a real impact. By leveraging a centrally orchestrated, peer to peer  delivery model, it becomes possible to offload a significant portion of traffic away from traditional CDNs, reducing bandwidth consumption while maintaining or improving QoE. 

‍

Rather than scaling capacity for every peak, the network scales dynamically with demand, improving efficiency across the delivery chain. Ultimately, OTT cost optimization depends on decoupling audience growth from infrastructure spend. Platforms that succeed in doing so will be those that change their focus from scaling capacity to scaling efficiency.

‍

For more information about Edge Intelligence, or how to reduce streaming costs with System73, visit www.system73.com, or contact us via our online chat.

‍