The OTT streaming industry is one of the most dynamic and fast-growing sectors in the digital economy. But behind the subscriber growth and content investment, there is a cost problem that does not get talked about enough. According to technology consultancy To The New, post-launch infrastructure expenses, including CDN fees, cloud egress, DRM licensing, and data transfer overhead, can consume up to 50% of gross revenue, a stark illustration of just how significant OTT infrastructure costs can become. For platforms that have spent years building their audience and content library, that is a significant share of earnings being absorbed by the pipes that deliver it. The response has always been to add more CDN capacity when demand grows, which for a long time was the logical conclusion. But the relationship between CDN spend and streaming performance is not linear, and it never really was. Each additional provider delivers less incremental capacity than the last, while the root causes of degraded performance, including routing inefficiencies, congestion at ISP interconnect points, and bottlenecks in the middle mile, go untouched.

Luckily, there is a smarter approach to controlling OTT CDN costs, and it starts by understanding where the real problems actually lie.

The CDN trap… more isn't necessarily more

A Content Delivery Network operates by caching and serving video content from edge servers positioned close to end users, reducing the distance data must travel and minimizing the load on origin infrastructure. In theory, deploying multiple CDN providers should multiply that effect, distributing traffic across a broader set of edge locations and increasing overall delivery capacity. In practice, however, the gains from each additional CDN provider are subject to a law of diminishing returns that the industry hasn’t quite reckoned with.

Beyond the first provider, subsequent CDNs increasingly overlap in their network coverage and edge server locations, meaning that each new addition contributes less incremental capacity than the last while adding another layer of cost, complexity, and vendor management overhead to an already demanding operational environment. What makes CDN optimization particularly challenging is that additional CDN capacity does not address the places where streaming performance most commonly breaks down. Congestion during large-scale live events, for example, is rarely a problem that originates inside the CDN itself.

In practice, bottlenecks emerge at ISP interconnect points where CDNs hand off traffic, at backbone routers and peering links between networks, and at regional aggregation points inside ISP infrastructure. Multi-CDN steering systems are designed on the assumption that congestion is localized and easy to route around, but during peak demand, shifting traffic between CDN providers often does little more than move the bottleneck to a different location. The underlying

congestion remains, and so do the streaming delivery costs of the infrastructure that failed to resolve it.

Where the real challenges lie…

To understand why CDN-first strategies fall short, it helps to look more closely at how internet traffic actually moves. Video content passes through a complex web of interconnected networks, each with different capacities, peering arrangements, and congestion profiles. The Border Gateway Protocol, or BGP, governs how traffic navigates this web by directing data along the shortest available network path rather than the fastest or least congested one. During a major live event, BGP concentrates demand on the same routers and network links, and those links become the bottleneck regardless of how many CDN providers are in the delivery stack.

This is the fundamental flaw in the traditional approach to live streaming infrastructure. It treats delivery as a capacity problem when it is, more precisely, a routing problem. Adding edge servers brings content closer to users, but it does not reduce streaming costs or increase the capacity of the congested ISP interconnect points and backbone links that traffic must still pass through. When congestion is widespread across the public internet during peak demand, multi-CDN steering systems have nowhere better to route traffic to. The result is a delivery operation that is over-engineered in terms of CDN contracts and under-equipped to handle the moments that matter most.

A more intelligent approach to OTT infrastructure costs

Addressing the routing problem at the heart of OTT delivery requires a different architectural approach, which is precisely what we built Edge Intelligence to provide. Rather than relying on BGP to find the shortest path through a congested network, our technology continuously monitors the capacity of routers across the entire public internet and routes traffic over whichever paths are actually free, in real time. In practice, a longer uncongested route is frequently faster than a shorter congested one, and Edge Intelligence is designed to exploit that reality rather than ignore it. The result is a meaningful step toward genuine OTT cost optimization, built into the architecture rather than bolted on afterward.

The delivery model that underpins Edge Intelligence is also structurally different from traditional CDN architecture. Instead of requiring every device to connect back to an edge server, our technology builds centrally orchestrated peer-to-peer broadcast trees that allow devices to share content with one another, reducing the volume of traffic that needs to travel through the public internet at all. This approach is also what makes true OTT platform scalability possible: rather than provisioning for peak demand in advance and paying for excess capacity that sits idle the rest of the time, the network grows organically with the audience.

At System73, we have seen this approach reduce CDN costs by offloading up to 80% of streaming traffic from CDN infrastructure, increasing effective delivery capacity by up to 4x without adding a single new CDN contract. For OTT platforms looking to control infrastructure

costs without compromising on streaming quality, that advantage scales in exactly the right direction: the larger the audience, the more capacity the network generates.

For more information about reducing streaming costs with solutions such as Edge Intelligence, visit our website and contact a member of t

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